covid patient case study

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The Impact of COVID-19 on Patient Experience Within a Midwest Hospital System: A Case Study

Affiliations.

1 Health Sciences, Drake University, Des Moines, IA, USA.
2 Drake University, Des Moines, IA, USA.
3 Pediatric Oncology and Hematology, Blank Children's Hospital, Des Moines, IA, USA.
PMID: 34901416
PMCID: PMC8664302
DOI: 10.1177/23743735211065298

Patient-centered communication and patient-provider relationships directly affect patient outcomes. The purpose of this study was to compare inpatient perception of provider/nurse communication in both COVID versus non-COVID diagnoses groups. A qualitative retrospective study was conducted by performing a priori coding analysis on Hospital Consumer Assessment of Healthcare Providers and Systems surveys from 4 different hospitals for both COVID and non-COVID diagnoses. Five themes emerged from non-COVID patient data: inconsistent health care provider communication, variable patient-provider education, pandemic influenced patient satisfaction and mental health stress, inconsistent hospital services, and stable provider professionalism. Five themes arose from the COVID patient data: provider gratitude, controversial communication methods, consistent patient education, lack of quality patient care, and poor timeliness. There is evidence of shared patient perceptions between both COVID and non-COVID patients, but also differences including timeliness and quality of care. The pandemic influenced all patients by creating non-mutually exclusive themes including overall gratitude and patient satisfaction. Future research should focus on a quantitative analysis of pandemic-related patient-provider communication effects on patient outcomes.

Keywords: COVID-19; HCAHPS; communication; nursing; physician engagement; provider.

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Conflict of interest statement

Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Novel coronavirus 2019 (COVID-19)

A case report and review of treatments.

Editor(s): Saranathan., Maya

a Department of Medicine, Hackensack Meridian Jersey Shore University Medical Center Neptune

b Department of Medicine, Hackensack Meridian School of Medicine at Seton Hall University Nutley

c Department of Pulmonology and Critical Care, Hackensack Meridian Jersey Shore University Medical Center Neptune, NJ, USA.

∗Correspondence: Steven Douedi, Jersey Shore University Medical Center, Neptune, NJ 07753 (e-mail: [email protected] ).

Abbreviations: ARDS = acute respiratory distress syndrome, CoV = coronavirus, COVID-19 = novel coronavirus 2019, CVVHD = continuous veno-venous hemodialysis, ED = emergency department, FiO2 = fraction of inspired oxygen, ICU = intensive care unit, MERS-CoV = Middle East respiratory syndrome coronavirus, PCR = polymerase chain reaction, PEEP = positive end-expiratory pressure, RSV = Respiratory syncytial virus, SARS-CoV = severe acute respiratory syndrome coronavirus, SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2.

How to cite this article: Douedi S, Miskoff J. Novel coronavirus 2019 (COVID-19): a case report and review of treatments. Medicine . 2020;99:19(e20207).

The authors have no conflicts of interests to disclose.

This manuscript is a unique submission and is not being considered for publication by any other source in any medium. Further, the manuscript has not been published, in part or in full, in any form.

The patient's next of kin provided consent for this manuscript to be published.

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0

Rationale:

Novel coronavirus 2019 (COVID-19) also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an enveloped, non-segmented positive-sense RNA virus belonging to the beta-coronaviridae family. This virus is known to cause severe bilateral pneumonia and acute respiratory distress syndrome (ARDS) which can lead to difficulty breathing requiring mechanical ventilation and intensive care unit management.

Patient concerns:

A 77-year-old female with a history of hypertension and hyperlipidemia who presented as a transfer to our hospital facility with worsening fevers, cough, and respiratory distress.

Diagnosis:

Chest X-rays revealed bilateral infiltrates worse at the lung bases and CT scan of the chest showed bilateral ground-glass opacities consistent with COVID-19. While our testing revealed a negative COVID-19 result at our institution, the result at a previous hospital returned a positive result.

Interventions:

She was being treated aggressively in the intensive care unit with high dose intravenous ascorbic acid, hydroxychloroquine, and anti-interleukin-6 monoclonal antibody. She also received a loading dose of remdesivir however was unable to complete the course due to organ failure and requirement of vasopressors for hemodynamic stability.

Outcomes:

She remained critically ill and was eventually placed on comfort care as per the family's wishes and passed away.

Lessons:

With a rapidly growing death rate and more than 200,000 confirmed cases worldwide, COVID-19 has become a global pandemic and major hit to our healthcare systems. While several companies have already begun vaccine trials and healthcare facilities have been using a wide-range of medications to treat the virus and symptoms, there is not yet an approved medication regimen for COVID-19 infections. The alarming increase in cases per day adds additional pressure to find a cure and decrease the global health burden and mortality rate.

1 Introduction

The novel coronavirus 2019 (COVID-19) also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an enveloped, non-segmented positive-sense RNA virus belonging to the beta-coronaviridae family. [1] COVID-19 has been found to be the cause of severe pneumonia and acute respiratory distress syndrome (ARDS) with a significantly high mortality rate. [2] According to the World Health Organization, there are 207,855 confirmed cases and 8648 deaths from COVID-19 as of March 19, 2020 and rapidly increasing. [3] Originating from bats like other virulent coronavirus (CoV) strains such as severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), COVID-19 has become the focus of the medical world and the pandemic of 2020. [1,4] We present a case of elderly female presenting with fever, cough, and shortness of breath found to be positive for COVID-19 and started on high-dose IV ascorbic acid, anti-interleukin-6, hydroxychloroquine, and remdesivir requiring high ventilator settings and eventually requiring vasopressors and continuous veno-venous hemodialysis (CVVHD).

2 Case presentation

A 77-year-old Middle-Eastern female with a medical history of hypertension and hyperlipidemia presented to the emergency department (ED) from a day care facility apartment where 2 people at the facility have tested positive for COVID-19 but she did not have any direct contact with these individuals. About 5 days before admission the patient developed a fever with a temperature of 102°F at home, and went to her primary medical doctor who sent her to the ED. In the ED she was found to have bilateral opacities on chest X-ray and had continued intermittent fevers with generalized weakness, cough, lethargy, and dyspnea and was sent for testing for COVID-19 then transferred to our facility for further management. In our facility, her temperature was 101.7°F, blood pressure 148/76 mm Hg, heart rate of 99 beats per minute, respiratory rate of 18 per minute, and oxygen saturation of 93% on room air. Physical exam was significant for a dry cough and bilateral rales on auscultation of the lung fields bilaterally but was unremarkable otherwise. A chest X-ray ( Fig. 1 ) was performed showing bilateral opacities throughout the lung fields with predominance of the lower lung lobes she was admitted for possible pneumonia with isolation precautions for suspected COVID-19 and was started on oxygen via nasal cannula and on 1-gram ceftazidime intravenously every 8 hours and 500 mg azithromycin orally daily. CT scan of the chest ( Fig. 2 ) was performed showing bilateral ground glass appearance throughout the lung with predominance in the peripheral lower lobes. Respiratory viral panel was sent including a repeat COVID-19 test ( Table 1 ). All results came back negative however the patient's condition deteriorated 2 days after admission to our facility, and she became hypoxic to 85% oxygen saturation while on nasal cannula and remained spiking fevers up to 103.4°F. She was intubated and transferred to the intensive care unit (ICU) for further management and was switched to ceftriaxone 1 g intravenously daily and azithromycin 500 mg via orogastric tube daily and was started on hydroxychloroquine 400 mg loading dose followed by 200 mg twice daily for a 7-day course. She required 100% fraction of inspired oxygen (FiO2) and a positive end-expiratory pressure (PEEP) of 12 to maintain an oxygen saturation of >90%. 12 hours later, the COVID-19 test from the initial facility returned positive results. On day 3 of hospitalization she was started on 6 g of IV ascorbic acid twice daily and given one dose of 8 mg per kg (567 mg) of tocilizumab, an anti-interleukin-6 monoclonal antibody. Due to a shortage of vitamin C in the hospital, her dose was decreased to 1 g IV daily on the 6th day of hospitalization and she was given another dose of tocilizumab. On day 7, her PEEP increased from 12 to 16 due to worsening oxygen saturation and increased requirement despite 100% FiO2. Due to severe ARDS, the decision was made to prone the patient for 18 hours a day. She completed her course of antibiotics and hydroxychloroquine but remained on vitamin C and zinc. Approval for remdesivir was obtained from Gilead Sciences Inc and she was given a loading dose of 200 mg on day 10 and due to worsening oxygen saturation her PEEP was again increased to 18. On day 11, the patient was unable to tolerate being prone due to significant desaturation to 65% on pulse oximetry and remained supine. She eventually required levophed for maintenance of hemodynamic stability and her creatinine increased from her baseline of 0.5-0.6 since admission until day 10 to 2.65 on day 12. For this reason, remdesivir was discontinued and nephrology was consulted and recommended CVVHD on day 13. On day 14 her PEEP requirement again increased to 20 while on 100% FiO2 to maintain an oxygen saturation >90%. Her condition remained critical while being aggressively managed in the ICU and ultimately the patient's family decision was to pursue comfort measures and the patient passed away.

3 Discussion

COVID-19 is the cause of severe viral pneumonia rapidly leading to ARDS. In a case series of 135 patients, Wan et al reported 88.9% of patients presented with a fever and 76.5% had a cough. [5] Fatigue and myalgias (32.5%), headache (17.7%), and dyspnea (13.3%) were less commonly reported. [5] These symptoms were also found on presentation with our patient. While the COVID-19 tests were pending, the CT scan of the chest provided valuable information as it met the trend of findings in infected patients. Wan et al obtained CT scans on all patients in their study and found bilateral involvement and multiple patchy or ground glass appearance to be the primary finding. [5] Huang et al found similar findings where 98% of CT scans obtained had bilateral involvement and multilobular consolidations. [6] These findings on CT scans are not unusual for a viral pneumonia. Influenza A (H1N1) was first found to cause a pandemic in 2009, a retrospective review of 92 patients by Çörtük et al found 69.6% of patients with H1N1 had bilateral patchy pneumonic infiltrates and 41.3% had bilateral ground glass opacities. [7] While the lack of rapid testing for COVID-19 has caused a delay in diagnosis, perhaps the use of CT scans could provide an increased suspicion of COVID-19 infection leading to earlier treatment and management.

Our patient presented in this case received treatment with vitamin C and zinc, both of which are known to improve the human immune system and aid in shortening the duration of and improving outcomes in respiratory infections including pneumonia. [8,9] In addition to vitamin and mineral supplements, hydroxychloroquine and azithromycin have obtained a large amount of attention for the treatment of COVID-19. Hydroxychloroquine, a well-known anti-malarial and auto-immune medication, is relatively inexpensive and has been extensively studied in the treatment for COVID-19. Studies have suggested hydroxychloroquine can interfere with glycosylation of the coronavirus receptors and increase endosomal pH thus inhibiting viral fusion and decreasing viral load. [10,11] Gautret et al reported a synergistic effect using hydroxychloroquine and azithromycin in viral elimination and decreasing viral load. [12] Despite this evidence, the use of hydroxychloroquine for viral infections has been questioned. Roques et al reported a study using chloroquine in Chikungunya virus reporting cytokines were reduced causing the adaptive immune response to be delayed, exacerbating fever, and unchanged suppression of viral load. [13] While further studies are in need to provide concrete evidence on the use of hydroxychloroquine, clinical trials from China have already shown promising results for COVID-19 and several countries around the world have begun using these medications. Tocilizumab, a recombinant humanized anti-interleukin-6 receptor monoclonal antibody, has been extensively used in auto-immune conditions such as rheumatoid arthritis. [14] With this monoclonal antibody, interleukin-6 function is blocked and hence the differentiation of T helper cells and B cells into immunoglobulin-secreting cells are inhibited. [14] The cytokine storm observed in patients with COVID-19 has been difficult to control and manage leading to increased mortality, tocilizumab therefore helps decrease the immune response and the resulting damage caused by cytokines. [6,15] While still not approved in the United States, tocilizumab has thus far shown promising results in clinical trials. [15]

Other treatments for COVID-19 have also emerged and have thus far shown promising results in ongoing clinical trials. Of these, remdesivir (GS-5734) and favipiravir (T-705) have become the center of attention. Remdesivir is an adenosine analog that incorporates into viral RNA causing premature termination. [10,14] It has been found effective at inhibiting viral replication in Ebola, SARS-CoV, and MERS-CoV infections. [10,16,17] Favipiravir, an RNA-dependent RNA polymerase inhibitor, has already obtained approval for the treatment of COVID-19 in China on February 15th, 2020. [18] Studies have shown favipiravir inhibited RNA polymerase activity and thus prevented replication of RNA viruses like COVID-19 with minimal side effects. [18] Remdesivir (GS-5734, Gilead Sciences Inc.) is currently under several clinical trials and all of its side effects have not yet been defined. In our patient, within 2 days of starting remdesivir our patient had worsening renal function eventually requiring CVVHD and vasopressors thus preventing further treatment with the medication. While our patient was critically ill in the ICU, it is not known if this medication was the cause for further decompensation due to kidney injury. Further studies and clinical trials are required to fully understand the role of remdesivir and other medications in COVID-19 infected patients.

4 Conclusion

COVID-19 is a serious infection that has led to thousands of cases of severe pneumonia, ARDS, and even deaths across the globe. As of now there are no approved treatments for this viral pandemic. While several medications have shown to be effective in clinical trials, further studies are needed to establish dosing, treatment course, and side effects of these medications. As the number of cases and deaths continue to increase in the world, the race to develop faster testing modalities to rapidly diagnose and manage these patients earlier continues to be the focus of the global healthcare system.

Author contributions

Conceptualization: Steven Douedi, Jeffery Miskoff.

Writing – original draft: Steven Douedi.

Writing – review & editing: Steven Douedi, Jeffery Miskoff.

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acute respiratory distress syndrome; coronavirus; novel coronavirus 2019; infection; respiratory; severe acute respiratory syndrome coronavirus 2

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Case Study: Teenage Patient With COVID-19 Experiences Severe Encephalopathy

A recently published case study described a case of severe encephalopathy associated with coronavirus disease 2019 (COVID-19) in a pediatric patient who developed clinical features characteristic of akinetic mutism. This was published in a recent edition of Neurology .

The patient was a 16-year-old female who developed anxiety, insomnia , anorexia, paranoia, and ritualistic behaviors 3 days after developing a sore throat. She was referred to the emergency department for urgent psychiatric review following the onset of visual hallucinations. The patient’s temperature rose to 38.6°C at time of attendance.

Clinicians tested for and detected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on up to 6 nasopharyngeal swabs over the following several weeks. During the first 10 days of admission, the patient remained pyrexial and tachycardic.

Visual and auditory hallucinations in this patient included seeing lions leaping towards her and fears that she had harmed her family members. She developed mutism 5 days after admission and exhibited little to no voluntary motor activity. The patient displayed repetitive scissoring movements with her legs and circular movements with her arms; this lasted for hours at a time during the first 2 weeks of clinical course.

Treatment included 1 course of 0.4 mg/kg/day of intravenous immunoglobulin (IVIG) over the course of 5 days from day 3 of admission. The patient then received 1 g of intravenous methylprednisolone per day over 3 days. While clinicians initiated a second course of IVIG on day 14, it was discontinued following the development of a widespread rash.

Approximately 4 weeks after the initial presentation, the patient began to show signs of increased voluntary movement, speech attempts, and a reduction in limb rigidity. Her gait remained hesitant, but she was finally discharged home on day 98. She still required significant support in washing and feeding and was waiting for admission to a facility for rehabilitation.

The patient continued to have significant cognitive and physical difficulties 4 months after the onset of symptoms, but these symptoms began to improve at 6 months. At this time, her neurologic examination normalized. The patient’s speech also became fluent with an engaged affect. She has since regained activities of daily life and has resumed her local dance classes. Despite this, she still has memory difficulties and complaints of fatigue.

Regarding this case, the study authors stated that “this was a devastating, presumed parainfectious encephalopathy, with slow and incomplete recovery to date.” They concluded that, ultimately, “the patient will require close neurological and psychiatric long-term follow-up.”

Gaughan M, Connolly S, O’Riordan S, et al. Pediatric parainfectious encephalitis associated with COVID-19 . Published online January 4, 2021. Neurology . doi:10.1212/WNL.0000000000011476

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CASE REPORT article

Case report: clinical features of a covid-19 patient with cirrhosis.

$\nJian Zhou&#x;$

1 Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China
2 Department of Respiratory Medicine, The First Hospital of Changsha City, Changsha, China
3 Department of Anesthesiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China

Coronavirus disease 2019 (COVID-19) was first reported in Wuhan, Hubei Province, China in December 2019. At present, COVID-19 has emerged as a global pandemic. The clinical features of this disease are not fully understood, especially the interaction of COVID-19 and preexisting comorbidities and how these together further impair the immune system. In this case study, we report a COVID-19 patient with cirrhosis. A 73-year-old woman with cirrhosis reported a fever and cough on February 6, 2020. CT of the chest indicated an infection in her bilateral lungs. She tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The woman was treated with lopinavir and ritonavir tablets and interferon alpha-2b injection, but there was no obvious effect. Although this patient was basically asymptomatic after 2 days in the hospital, the inflammation of the bilateral lungs was slow to subside as shown in CT of the chest. In addition, the white blood cell count (WBC), absolute neutrophil count, and absolute lymphocyte count remained decreased and the result of real-time reverse transcription polymerase chain reaction (PCR) (rRT-PCR) assay was still positive for SARS-CoV-2 on hospital day 28. After infusion of plasma from a recovered COVID-19 patient four times, the patient tested negative for SARS-CoV-2. She was discharged on March 13, 2020. This patient tested negative for SARS-CoV-2 after infusion of plasma from a recovered COVID-19 patient four times. Cirrhosis could impair the homeostatic role of the liver in the systemic immune response, which may affect the removal of SARS-CoV-2. This could lead to a diminished therapeutic effect of COVID-19. Thus, clinicians should pay more attention to COVID-19 patients with cirrhosis.

Introduction

At present, many studies have indicated the epidemiological and clinical characteristics of coronavirus disease 2019 (COVID-19) ( 1 – 4 ). However, there are many diseases that may affect the immune system, such as AIDS, cirrhosis, and advanced malignant tumors, which may affect the removal of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), further affecting the treatment of COVID-19 patients. A nationwide analysis in China analyzed the major strategies for patients with cancer in this COVID-19 crisis ( 5 ). The process of advanced cirrhosis is complicated with cirrhosis-associated immune dysfunction. Cirrhosis has the potential to injure the homeostatic role of the liver in the immune system ( 6 , 7 ). In this case study, we report a case of a COVID-19 patient with cirrhosis. We describe the symptoms, diagnosis, treatment, and management of this patient, which may provide more information for the treatment of COVID-19 patients with cirrhosis.

Case Report

On February 11, 2020, a 73-year-old woman came to the Fever Clinic of the First Hospital of Changsha, China. Ten minutes later, she was taken to the examination room and evaluated by a clinic doctor. The chief complaint of the patient was a fever—her body temperature peaked at 39°C—with cough, expectoration, shortness of breath, and general weakness that started prior 5 days. She developed mild diarrhea (3–4 stools/day) 2 days prior to coming to the hospital. Her daughter was diagnosed with COVID-19. Given her symptoms and recent close contact with a COVID-19-positive patient, she decided to go to a healthcare provider. The patient had a history of cirrhosis and type 2 diabetes, but no history of smoking or drinking. Physical examination indicated a body temperature of 38.8°C, a pulse of 100 beats/min, a respiratory rate of 22 breaths/min, an oxygen saturation of 85%, and bowel sounds at four times/min. She presented with a characteristic feature of chronic liver disease, hepatic facies, and liver palms, but no spider nevus. In addition, she had thick breathing sounds on both sides of the lungs and audible wet murmurs in both the lungs. The abdomen of the patient was soft and had no lumps. No pain was found in the liver without mobile dullness.

Considering the possibility of SARS-COV-2 infection, we performed a chest CT examination and found bilateral pneumonia ( Figure 1 ). The results of a nucleic acid amplification test (NAAT) for influenza A and B were negative. Her blood tests demonstrated simultaneous reduction of the ternary systems (red blood cells: 2.83 × 10 12 cells/l; peripheral blood hemoglobin: 83 g/l; white blood cells: 0.78 × 10 9 cells/l; lymphocytes: 0.11 × 10 9 cells/l; lym%: 14.5%; platelets: 41 × 10 9 cells/l) and an elevated percentage of neutrophils (0.65 × 10 9 /L; n%: 82.8%), C-reactive protein (62.5 mg/l), and erythrocyte sedimentation rate (129 mm/h) ( Table 1 ). In view of the close contact history and clinical examination results of the patient, we carried out COVID-19 test for the patient. Specimens were collected following the Chinese Center for Disease Control and Prevention (CCDC) guidance. The results showed that she tested positive for SARS-COV-2. Therefore, she was admitted to the isolation ward for further treatment.

Figure 1 . CT of the chest of the patient. (A) CT of the chest was obtained on February 12, 2020 (hospital day 2, illness day 6). The major morphogenesis of her bilateral lungs took on increased bronchovascular shadows and multiple patchy and maculas shadows, with cord-like ground-glass opacity (GGO) in the middle and lower regions of the lung. CT scan of the chest also showed increased lung markings. The texture of the trachea and blood vessels in both the lungs became thicker. (B) CT of the chest was obtained on February 16, 2020 (hospital day 6, illness day 10). The patchy lesions and maculas in both the lungs were partially absorbed. Increased lung markings were observed in the bilateral lungs. (C) CT of the chest was obtained on February 20, 2020 (hospital day 10, illness day 14). Decreased density of the patchy lesions in both the lungs was observed. The texture of the trachea and blood vessels in both the lungs became thicker. (D) CT of the chest was obtained on February 24, 2020 (hospital day 14, illness day 18). The pulmonary lesions remained unchanged. (E) CT of the chest was obtained on February 28, 2020 (hospital day 18, illness day 22). There was no obvious change in the patchy lesions in both the lungs. GGO was slightly increased. (F) CT of the chest was obtained on March 3, 2020 (hospital day 22, illness day 26). The major lesions of the bilateral lungs were not absorbed. (G) CT of the chest was obtained on March 10, 2020 (hospital day 29, illness day 33). The multiple patchy and maculas shadows of the bilateral lungs were further absorbed and the bronchovascular shadows were reduced.

Table 1 . Clinical laboratory results.

On day 1 of the hospital stay (illness day 5), the patient was administered lopinavir and ritonavir tablets (2 pills BID peros), which were recommended by the Diagnosis and Treatment of Pneumonitis with COVID-19 Infection (DTPI) published by the National Health Commission of the People's Republic of China (PRC) and interferon alpha-2b injection (5 million IU added into 2 ml of sterile water, inhalation BID). To inhibit inflammation in the lungs, she was treated with methylprednisolone sodium succinate (40 mg QD, intravenously). Yellow-green expectoration predicted the presence of a bacterial infection and, as such, moxifloxacin hydrochloride and sodium chloride injection (0.4 g QD) were given intravenously to the patient as treatment. Moreover, other supportive treatments included human immunoglobulin (10 g QD, intravenously) for improving immunity, Bifidobacterium lactobacillus trifecta orally for regulating the intestinal flora, recombinant human granulocyte colony-stimulating factor for promoting cell proliferation, and ampeptide elemente tablets for promoting the formation of platelets.

On day 2 of the hospital stay (illness day 6), she was asymptomatic apart from a cough, expectoration, chest tightness, and shortness of breath. Additionally, her temperature dropped to 36.9°C, but she reported that diarrhea still existed, approximately four times/day ( Table 2 ). CT scans showed that the patchy infiltration was scattered as a small range of ground-glass opacity effusion and strip lesions in the bilateral lungs, which was similar to day 1 in the hospital ( Figure 1 ). Otherwise, the laboratory results reflected that there was still a reduction in the tertiary system and hypoproteinemia due to liver dysfunction. Human serum albumin (50 ml BID) was then given intravenously. To prevent of episodes of hepatic encephalopathy, which is a chronically debilitating complication of hepatic cirrhosis, lactulose was added to the therapeutic regimen of the patient and nutritious meals were supplied to improve her anemia. The CCDC repeatedly confirmed that the oropharyngeal swabs of this patient tested positive for SARS-CoV-2 by real-time reverse transcription PCR (rRT-PCR) assay.

Table 2 . Body temperatures and symptoms from February 6 to March 13, 2020.

On day 3 of the hospital stay (illness day 7), the patient reported she felt better. Her pulse oxygen saturation increased significantly, up to 100%, at an oxygen flow rate of 2 l/min. Since that she still had diarrhea symptoms and lactulose was stopped to avoid the occurrence of imbalance of water and electrolytes. On day 4 of her hospital stay (illness day 8), a gastroenterologist was contacted to evaluate the persistent diarrhea of the patient. According to the suggestion of the gastroenterologist, the patient was treated with pantoprazole enteric-coated tablets (40 mg QD orally) for acid suppression. In addition, reduced glutathione (0.6 g QD) was given intravenously to protect her liver from subsequent damage.

On days 5–10 of the hospital day (illness days 9–14), the patient reported that her diarrhea improved to a degree and her clinical condition improved with supportive care. On hospital day 6 of the hospital stay, CT scans showed that the partial patchy lesions in the bilateral lungs were absorbed compared with the CT images obtained previously ( Figure 1 ). Given the clinical presentation of the patient, treatment with human serum albumin was stopped on day 6 of the hospital stay. Lopinavir and ritonavir tablets, methylprednisolone sodium succinate, moxifloxacin, ampeptide elemente tablets, pantoprazole enteric-coated tablets, and human immunoglobulin were stopped on day 8 of the hospital stay of the patient ( Table 3 ). However, the clinical course of the patient continued to deteriorate in terms of her respiratory symptoms, who typically presented with a cough and shortness of breath. Thymosin (0.1 g QD) and plasma (200 ml) from recovered COVID-19 patients plasma were then given intravenously to boost the immunity of the patient. On day 9 of the hospital stay (illness day 13), the C-reactive protein of this patient dropped to 3.4 mg/l. Nevertheless, CT scans of the chest indicated that the symptoms of the bilateral lungs of the patient did not improve on day 10 of the hospital stay ( Figure 1 ). Moreover, the oropharyngeal swabs of this patient retested positive. Therefore, chloroquine phosphate (0.5 g BID) was administered orally instead. Additionally, the treatments did not improve the level of blood cells because of liver dysfunction and hypersplenism caused by cirrhosis.

Table 3 . Order sheet of the physician.

On days 11–18 of the hospital stay (illness days 15–22), she was in good clinical condition, except for a persistent cough and intermittent diarrhea. In order to further alleviate the diarrhea of the patient, montmorillonite powder (3 g QD) and loperamide hydrochloride (2 mg QD) were administrated orally. Moreover, interferon alpha-2b injections were stopped due to its limited effect on the clearance of the virus and the plasma from recovered COVID-19 patients was infused again on day 15 of the hospital stay (illness day 19). As the diarrhea of the patient improved, antidiarrheal drugs were discontinued on day 18 of the hospital stay (illness day 22).

On days 19–29 of the hospital stay (illness days 23–33), the vital signs of the patient were largely stable. The patient reported that her cough and diarrhea had abated and her clinical condition improved. Given these good clinical conditions, a reduction in glutathione injections was initiated on day 19 of her hospital stay. However, since the oropharyngeal swabs of this patient tested positive again, she was treated with plasma from a recovered COVID-19 patient for the third time. On day 29 of the hospital stay (illness day 33), CT scans showed that the patchy lesions in the bilateral lungs of the patient had absorbed compared with the CT images obtained previously ( Figure 1 ). On the same day, the patient tested negative for COVID-19 infection ( Table 1 ). On day 30 of the hospital stay (illness day 34), the patient was once again treated with the plasma from a recovered COVID-19 patient in order to ensure that the virus was completely cleared. On days 30–31 of the hospital stay, the patient tested negative for COVID-19 by an rRT-PCR assay for two times. She was discharged on March 13, 2020 (day 32 of the hospital stay, illness day 36).

Cirrhosis affects the cellular and humoral immune response of the entire body and the immune system of the liver ( 6 , 8 ). The proportion of CD4 + /CD8 + cells in the liver of patients with cirrhosis decreases and the distribution of lymphocytes varies within different lesions. CD8 + cells predominate in the necrotic area, while CD4 + cells increase in the manifold area. T-helper type 1 (Th1) cells dominate during the early stages of cirrhosis and then gradually drift toward Th2 cells. In order to understand the impact of cirrhosis on the treatment of COVID-19, we report the symptoms, diagnosis, treatment, and management of a COVID-19 patient with cirrhosis.

In this case study, the patient tested positive for SARS-CoV-2, which was supported by CT scan of the chest and she was admitted to the isolation ward at the First Hospital of Changsha City, China. Lopinavir and ritonavir tablets combined with interferon alpha-2b injections were given to her on her first day in the hospital. Though she was basically asymptomatic on day 2 of her hospital stay and her body temperature also returned to a normal range, the inflammation of her bilateral lungs was difficult to subside, suggesting that clinicians should be aware of COVID-19 patients with diseases affecting the immune system. These patients may show mild or even no symptoms, while the inflammation of lungs may be progressing. Therefore, if a person with basic diseases that impair the immune system was exposed to confirmed COVID-19 cases, they should immediately come to the hospital even if they have no symptoms. Also, doctors need to be aware of the progression of inflammation in the lungs.

Previous reports showed that COVID-19 patients with cirrhosis had lower albumin than patients with COVID-19 ( 9 ), which was consistent with the results of this case study. Moreover, Qi et al. discovered that leukopenia, lymphopenia, and thrombocytopenia occurred in COVID-19 patients with cirrhosis ( 10 ), which were similar to the results we obtained. Additionally, increasing evidence indicated that patients with COVID-19 exhibited a hypercoagulability in the lung ( 11 ). In this case study, the D-dimers of COVID-19 patient with cirrhosis were elevated, suggesting hypercoagulability of the patient. The liver synthesizes a variety of coagulation factors. When cirrhosis causes liver insufficiency, the production of coagulation factors is reduced, which leads to prolonged prothrombin time (PT), activated partial thromboplastin time (APTT) and thrombin time (TT), and a decrease of fibrinogen. Therefore, the PT, APTT, and TT of COVID-19 patient with cirrhosis were prolonged and fibrinogen was decreased, which was similar to the previous study ( 12 ). In addition, venous thromboembolism (VTE) including deep venous thrombosis (DVT) is common in cirrohsis patients. Additionally, the patient in this case study was treated with antiviral drugs, which had no obvious effect on her symptoms. Previous study indicated that 96% cirrhotic patients with confirmed SARS-CoV-2 infection needed hospitalization or prolonged an ongoing one ( 13 ). In this case study, we observed similar results. This COVID-19 patient with cirrhosis was hospitalized for 32 days. She was tested positive for COVID-19 on day 25 of her hospital stay. Moreover, the numbers of WBC and the absolute neutrophil count and absolute lymphocyte count remained reduced in this patient. The process of advanced cirrhosis is complicated with cirrhosis-associated immune dysfunction. Cirrhosis has the potential to injure the homeostatic role of the liver in the immune system, which may be associated with the process of COVID-19. Additionally, although the mortality of COVID-19 was mediated by pulmonary involvement, cirrhosis is assumed to be a high-risk factor for severe COVID-19 because of an altered gut-liver axis and inherent immune dysfunction. Cirrhosis can impair the cellular and humoral immune system of the entire body, which may impair the removal of SARS-CoV-2. Thus, physicians may need to monitor immune indicators in COVID-19-positive patients with comorbidities that impair the immune system.

The patient in this case study was administered the plasma (200 ml) from recovered COVID-19 patients four times. After the last administration of plasma on day 30 of the hospital stay, the patient tested negative for SARS-CoV-2 three consecutive times and then she was discharged on day 32 of her hospital stay. This suggested that the treatment for COVID-19 is passive immunotherapy. Cirrhosis can impair the homeostatic role of the liver in the systemic immune response; thus, passive immunotherapy, such as plasma administration from recovered COVID-19 patients, may be an option for treatment. However, this case study has a limitation that needs to be cautious. These findings have only been observed in one patient. Further multicenter with large sample studies are needed to perform to verify the results.

This case study described the symptoms, diagnosis, treatment, and management of a COVID-19 patient with cirrhosis, emphasizing the need to pay attention to underlying diseases in COVID-19-positive patients. More information about this disease is still needed in order to successfully explore its clinical management.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Ethics Statement

The studies involving human participants were reviewed and approved by The First Hospital of Changsha City Committee for Clinical Research. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author Contributions

JZ and JS conceived and designed the study and also critically revised the manuscript. JZ and WW conducted the experiments and drafted the manuscript. DJ, KH, FZ, YX, and ZZ contributed to the revision of the manuscript. All authors have read and approved the final manuscript.

This study was funded by the Innovative Major Emergency Project Funding against the New Coronavirus Pneumonia in Hunan Province (Grant Nos. 2020SK3014 and 2020SK3013), the Key Research & Developmental Program of Hunan Province (2022SK2047), Chinese Public Health Union (GWLM202039), Health and Family Planning Commission Fund Project in Hunan Province (Grant No. B2017209), Natural Science Foundation of Hunan Province (Grant No. 2018JJ2452), New Coronavirus Pneumonia Emergency Project of Changsha Science and Technology Bureau (Grant Nos. kq2001010 and kq2001008), the Mittal Innovation Project of Central South University (Grant No. GCX20190879Y) and the Fundamental Research Funds for the Central Universities of Central South University (Grant No. 2018zzts930). The study funders/sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would like to thank all the co-investigators and colleagues who made this study possible. The authors would like to thank the Changsha CDC, Hunan CDC, and CCDC for their assistance with laboratory testing. We thank LetPub ( www.letpub.com ) for its linguistic assistance during the preparation of this revised manuscript.

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4. Holshue ML, DeBolt C, Lindquist S, Lofy KH, Wiesman J, Bruce H, et al. First Case of 2019 Novel Coronavirus in the United States. N Engl J Med. (2020) 382:929–36. doi: 10.1056/NEJMoa2001191

5. Liang W, Guan W, Chen R, Wang W, Li J, Xu K, et al. Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. Lancet Oncol. (2020) 21:335–7. doi: 10.1016/S1470-2045(20)30096-6

6. Albillos A, Lario M, Alvarez-Mon M. Cirrhosis-associated immune dysfunction: distinctive features and clinical relevance. J Hepatol. (2014) 61:1385–96. doi: 10.1016/j.jhep.2014.08.010

7. Sipeki N, Antal-Szalmas P, Lakatos PL, Papp M. Immune dysfunction in cirrhosis. World J Gastroenterol. (2014) 20:2564–77. doi: 10.3748/wjg.v20.i10.2564

8. Kreivenaite E, Gedgaudas R, Valantiene I, Mickiene A, Kupcinskas J. COVID-19 in a Patient with Liver Cirrhosis. J Gastrointestin Liver Dis. (2020) 29:263–6. doi: 10.15403/jgld-2440

9. Bajaj JS, Garcia-Tsao G, Biggins SW, Kamath PS, Wong F, McGeorge S, et al. Comparison of mortality risk in patients with cirrhosis and COVID-19 compared with patients with cirrhosis alone and COVID-19 alone: multicentre matched cohort. Gut. (2021) 70:531–6. doi: 10.1136/gutjnl-2020-322118

10. Qi X, Liu Y, Wang J, Fallowfield JA, Wang J, Li X, et al. Clinical course and risk factors for mortality of COVID-19 patients with pre-existing cirrhosis: a multicentre cohort study. Gut. (2021) 70:433–6. doi: 10.1136/gutjnl-2020-321666

11. Jiang M, Mu J, Shen, Zhang H. COVID-19 With Preexisting Hypercoagulability Digestive Disease. Front Med (Lausanne). (2020) 7:587350. doi: 10.3389/fmed.2020.587350

12. Blasi A, von Meijenfeldt FA, Adelmeijer J, Calvo A, Ibañez C, Perdoma J, et al. In vitro hypercoagulability and ongoing in vivo activation of coagulation and fibrinolysis in COVID-19 patients on anticoagulation. J Thromb Haemost. (2020) 18:2646–53. doi: 10.1111/jth.15043

13. Iavarone M, D'Ambrosio R, Soria A, Triolo M, Pugliese N, Poggio PD, et al. High rates of 30-day mortality in patients with cirrhosis and COVID-19. J Hepatol. (2020) 73:1063–71. doi: 10.1016/j.jhep.2020.06.001

Keywords: COVID-19, cirrhosis, SARS-CoV-2, treatment, cured patient

Citation: Zhou J, Jiang D, Wang W, Huang K, Zheng F, Xie Y, Zhou Z and Sun J (2021) Case Report: Clinical Features of a COVID-19 Patient With Cirrhosis. Front. Med. 8:678227. doi: 10.3389/fmed.2021.678227

Received: 09 March 2021; Accepted: 01 November 2021; Published: 26 November 2021.

Reviewed by:

Copyright © 2021 Zhou, Jiang, Wang, Huang, Zheng, Xie, Zhou and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhiguo Zhou, cszhouzhiguo@outlook.com ; Jingjing Sun, 2520064@zju.edu.cn

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

New Study Reveals Majority of Pediatric Long COVID Patients Develop a Dizziness Known as Orthostatic Intolerance

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BALTIMORE, September 16, 2024 — A new study from Kennedy Krieger Institute shows that the majority of children diagnosed with long COVID are likely to experience orthostatic intolerance (OI), a condition that causes the body to struggle with regulating blood pressure and heart rate when standing up. As a result, children often feel dizzy, lightheaded, fatigued and may experience “brain fog” or cognitive difficulties.

Orthostatic intolerance includes disorders such as postural orthostatic tachycardia syndrome (POTS) and orthostatic hypotension. Among the patients studied, dizziness (67%), fatigue (25%), and body pain (23%) are found to be common symptoms, which worsen when standing and improve when lying down. These symptoms can make it difficult to perform everyday activities like exercising, attending school, and socializing, severely impacting their quality of life.

This new research, conducted at Kennedy Krieger’s Pediatric Post-COVID-19 Rehabilitation Clinic , reveals that OI is prevalent among children dealing with the long-term effects of the COVID-19 virus. Researchers found 71% of the patients studied at the Institute experienced at least one orthostatic condition.

Dr. Laura Malone, Director of the Pediatric Post-COVID-19 Rehabilitation Clinic at Kennedy Krieger, is the senior author of this study. She explains these findings show the importance of screening pediatric long COVID patients for OI, as many have symptoms that could be missed without proper testing.

“Research proves this condition is common. Sixty-five out of the 92 children we studied were battling side effects like dizziness and fatigue from OI” Dr. Malone said. “Early diagnosis and treatment will give them the chance to recover and return to their normal routines.”

The study findings call for a multi-faceted approach to treatment. Research emphasizes the importance of increased salt and fluid intake, exercise training, and physical therapy. Medications to manage heart rate and blood pressure are also being explored. However, Dr. Malone says more research is needed to fully understand OI.

“Our goal is to provide more targeted and tailored treatments that will help these children,” Dr. Malone said. “This study is just the beginning, and we hope it will spark further research to support for children with long COVID.”

Click here to discover more about Kennedy Krieger’s research on long COVID and its nationally recognized clinic in Baltimore.

About Kennedy Krieger Institute: Kennedy Krieger Institute, an internationally known nonprofit organization located in the greater Baltimore-Washington, D.C., region, transforms the lives of more than 27,000 individuals a year through inpatient and outpatient medical, behavioral health and wellness therapies; home and community services; school-based programs; training and education for professionals; and advocacy. Kennedy Krieger provides a wide range of services for children, adolescents and adults with diseases, disorders and injuries that impact the nervous system, ranging from mild to severe. The Institute is home to a team of investigators who contribute to the understanding of how disorders develop while at the same time pioneering new interventions and methods of early diagnosis, prevention and treatment. Visit KennedyKrieger.org for more information about Kennedy Krieger.

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Surgery Post-COVID: No Need to Wait More Than 2 Weeks, New Study Says

Postponing operations following a positive COVID-19 test may be creating unnecessary delays in elective surgeries, new findings suggest.

In the early days of the pandemic, the American Society of Anesthesiologists recommended delaying nonurgent surgeries by up to seven weeks following SARS-CoV-2 infection. These guidelines were based on research at the time that showed that COVID-19 was associated with an increased risk of post-operative difficulties including pulmonary complications.

Now, in the midst of the latest wave — predominantly driven by subvariants of Omicron known as FLiRT and LB.1 — many medical institutions continue to take conservative measures even though the newest infections have tended to be milder.

A new study shows that there is no significant benefit in delaying surgeries longer than two weeks after SARS-CoV-2 infection. The researchers published their findings in Annals of Surgery on August 1.

“The same mandate for postponing surgery that was necessary before isn’t supported by the most recent evidence,” says Ira Leeds, MD , assistant professor of surgery at Yale School of Medicine, who was the study’s first author.

Following the onset of the pandemic, elective surgeries came to a screeching halt. “For the first six months to a year of the COVID-19 pandemic, unless there was a true urgency, cases were being routinely delayed based on local policies supported by society guidelines at the time,” says Leeds.

Later on, as these procedures went back on the schedule, surgeons grappled with how to provide patients with the beneficial outcomes of surgery while minimizing the risk of COVID-related post-operative complications. Given that both surgery and COVID-19 can place stress on organs such as the heart and lungs, surgeons took great precautions.

“The data at the time suggested that among those who were seriously ill from COVID-19, there were long-term sequelae [condition following prior disease/injury],” says Leeds. For example, these patients faced greater risks associated with mechanical ventilation and blood clots.

The same mandate for postponing surgery that was necessary before isn’t supported by the most recent evidence. Ira Leeds, MD

However, when later waves of COVID-19 eased in severity, surgeons had little guidance on whether weeks-long delays were still protecting patients, especially those who had mild or asymptomatic infections.

Surgery within two weeks of infection associated with adverse outcomes

In its latest study, Leeds’ team used Veterans Affairs administrative data from April 2020 to September 2022 to identify more than 80,000 patients who had undergone an inpatient surgical procedure. The most common surgeries patients underwent were hernia repairs and knee replacements. Of this cohort, 16,000 had a positive COVID-19 test before surgery. The researchers divided these patients into groups based on the number of days between the most recent positive test and the date of surgery. Then, they matched patients in the COVID-positive and control groups based on factors including which disease they were being treated for, which procedure they underwent, and which medical center they visited.

The researchers compared mortality within 90 days and post-operative complications within 30 days. Their analysis revealed that there were no significant differences between the groups—with the exception of those who had tested positive within two weeks before their surgery. These patients were the only ones who had a higher risk of mortality and post-operative complications [BI1] —including cardiopulmonary complications, blood clots, and post-operative infections—compared to the controls.

The study offers evidence that previous guidelines for delaying surgery are no longer beneficial to patients—preventing them from receiving timely care while offering no further protection from COVID-related complications. “If someone is being hospitalized for COVID a week before their surgery, and they can wait a couple of weeks, then, yes, they should,” says Leeds. “But anything more than two weeks was not associated with better surgical outcomes.”

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Ira Leeds, MD, FACS, FASCRS Assistant Professor of Surgery (Colon and Rectal); Assistant Professor, Biomedical Informatics & Data Science; Clinical Member, Cancer Prevention and Control Program - Yale Cancer Center; Clinical Fellow, Clinical Epidemiology Research Center (CERC), Department of Veterans Affairs

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The dashed line corresponds with the mortality ratio of the number of COVID-19 deaths during the winter Omicron period vs the number of COVID-19 deaths during the wild-type period among the total US population. Mortality ratios above this line indicate greater-than-expected COVID-19 mortality during the Omicron vs wild-type periods compared with the general population. Mortality ratios below this line indicate lower-than-expected COVID-19 mortality during the Omicron vs wild-type periods compared with the general population. The error bars indicate 95% CIs.

eTable 1. International Classification of Diseases, Tenth Revision Codes Used to Define Cancer Sites

eTable 2. Baseline Characteristics of Patients With Cancer and Members of the General Population who Died of COVID-19 During the Study Period

eFigure. Pecentage of COVID-19 Deaths Per Month Relative to the Peak of COVID-19 Deaths per Month During the Wild-Type Period Among Patinets With Cancer and the General Population Stratified by Age Group

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Potter AL , Vaddaraju V , Venkateswaran S, et al. Deaths Due to COVID-19 in Patients With Cancer During Different Waves of the Pandemic in the US. JAMA Oncol. 2023;9(10):1417–1422. doi:10.1001/jamaoncol.2023.3066

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Deaths Due to COVID-19 in Patients With Cancer During Different Waves of the Pandemic in the US

1 Division of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Boston
2 Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
3 Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
4 Department of Medicine, Massachusetts General Hospital, Boston
5 National Bureau of Economic Research, Cambridge, Massachusetts
6 Mongan Institute Health Policy Research Center, Massachusetts General Hospital, Boston

Question Did COVID-19 mortality differ between patients with cancer and the general US population depending on which SARS-CoV-2 variant was dominant?

Findings Among 34 350 patients with cancer who died during the COVID-19 pandemic during periods in which wild-type, Delta, and Omicron variants were predominant between March 2020 and May 2022, the number of deaths was higher during the winter Omicron surge compared with the preceding year’s winter surge of the wild-type variant. In contrast, there were 29% fewer COVID-19 deaths in the general population during the winter Omicron surge compared with the preceding year’s winter surge.

Meaning Findings of this study suggest that patients with cancer experienced a disparate burden of COVID-19 mortality during the winter Omicron wave; strategies to prevent COVID-19 transmission should remain a high priority as new variants arise.

Importance With the ongoing relaxation of guidelines to prevent COVID-19 transmission, particularly in hospital settings, medically vulnerable groups, such as patients with cancer, may experience a disparate burden of COVID-19 mortality compared with the general population.

Objective To evaluate COVID-19 mortality among US patients with cancer compared with the general US population during different waves of the pandemic.

Design, Setting, and Participants This cross-sectional study used data from the Center for Disease Control and Prevention’s Wide-Ranging Online Data for Epidemiologic Research database to examine COVID-19 mortality among US patients with cancer and the general population from March 1, 2020, to May 31, 2022. The number of deaths due to COVID-19 during the 2021 to 2022 winter Omicron surge was compared with deaths during the preceding year’s COVID-19 winter surge (when the wild-type SARS-CoV-2 variant was predominant) using mortality ratios. Data were analyzed from July 21 through August 31, 2022.

Exposures Pandemic wave during which the wild-type variant (December 2020 to February 2021), Delta variant (July 2021 to November 2021), or Omicron variant (December 2021 to February 2022) was predominant.

Main Outcomes and Measures Number of COVID-19 deaths per month.

Results The sample included 34 350 patients with cancer (14 498 females [42.2%] and 19 852 males [57.8%]) and 628 156 members of the general public (276 878 females [44.1%] and 351 278 males [55.9%]) who died from COVID-19 when the wild-type (December 2020-February 2021), Delta (July 2021-November 2021), and winter Omicron (December 2021-February 2022) variants were predominant. Among patients with cancer, the greatest number of COVID-19 deaths per month occurred during the winter Omicron period (n = 5958): at the peak of the winter Omicron period, there were 18% more deaths compared with the peak of the wild-type period. In contrast, among the general public, the greatest number of COVID-19 deaths per month occurred during the wild-type period (n = 105 327), and at the peak of the winter Omicron period, there were 21% fewer COVID-19 deaths compared with the peak of the wild-type period. In subgroup analyses by cancer site, COVID-19 mortality increased the most, by 38%, among patients with lymphoma during the winter Omicron period vs the wild-type period.

Conclusions and Relevance Findings of this cross-sectional study suggest that patients with cancer had a disparate burden of COVID-19 mortality during the winter Omicron wave compared with the general US population. With the emergence of new, immune-evasive SARS-CoV-2 variants, many of which are anticipated to be resistant to monoclonal antibody treatments, strategies to prevent COVID-19 transmission should remain a high priority.

The growing number of US hospitals electing to remove masking requirements that had been in place to control COVID-19 has sparked some debate as to whether it is premature to remove masking mandates, particularly in higher-risk settings, such as hospitals. 1 Compared with the general population, patients with cancer are at increased risk of breakthrough SARS-CoV-2 infection and severe COVID-19. 2 - 4 With the ongoing relaxation of measures to prevent SARS-CoV-2 transmission, patients with cancer may experience a disparate burden of COVID-19 mortality compared with the general population.

Examining COVID-19 mortality during previous pandemic waves in which the transmissibility and severity of the SARS-CoV-2 variant predominant at that time, rates of vaccination, and measures to prevent virus transmission varied greatly, may provide insight into future COVID-19 mortality risk among patients with cancer. In the US, the wild-type virus, Delta variant, and Omicron (BA.1) variant were associated with different degrees of transmissibility and severity. For example, compared with the wild-type virus and previous variants, the Delta variant was reported to be more transmissible and to carry a greater risk of severe COVID-19, 5 whereas the Omicron variant was reported to be significantly more transmissible than both the wild-type virus and Delta variant 6 , 7 but was associated with lower risks of COVID-19–related hospitalization and death. 8 - 10 The objective of this study was to compare COVID-19 mortality among US patients with cancer compared with the general US population during periods in which the wild-type virus, Delta variant, and Omicron variant were predominant.

This population-based, retrospective, cross-sectional study was deemed exempt from review and from the informed consent requirement by the Massachusetts General Hospital Institutional Review Board because publicly available deidentified data were used. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline. Individuals who died from COVID-19 from March 1, 2020, to May 31, 2022, in the Wide-Ranging Online Data for Epidemiologic Research (WONDER) system of the Centers for Disease Control and Prevention (CDC) 11 (eMethods and eTable 1 in Supplement 1 ) were identified for analysis. To examine COVID-19 mortality among patients with cancer, we selected individuals with both cancer and COVID-19 identified as causes of death (eMethods and eTable 1 in Supplement 1 ). To examine COVID-19 mortality among the total US population, we selected all individuals with COVID-19 identified as a cause of death.

Deaths due to COVID-19 among patients with cancer and the general population were grouped according to whether they occurred from December 2020 to February 2021 (wild-type period), July 2021 to November 2021 (Delta period), or December 2021 to February 2022 (winter Omicron period). The date cutoffs for each period were selected based on reports of the dates when the wild-type virus and Delta and Omicron BA.1 variants were widely circulating in the US. 12 , 13

Differences in the characteristics of patients with cancer and differences in the characteristics of members of the general population during the wild-type, Delta, and winter Omicron periods were evaluated using the χ 2 test. Data on race were obtained from death certificates and were collected to evaluate the demographic characteristics of the study cohort during the wild-type, Delta, and winter Omicron periods. Racial categories included in the analysis were American Indian, Asian, Black, multiracial, Native Hawaiian, and White.

The number of COVID-19 deaths per month during the study period was calculated among cancer patients and the general population. The percentage of COVID-19 deaths per month relative to the peak number of COVID-19 deaths per month during the wild-type period was calculated by dividing the number of COVID-19 deaths per month by the peak number of COVID-19 deaths per month during the wild-type period. Subgroup analyses were conducted by age group. To compare the number of COVID-19 deaths between the 3-month winter Omicron period and 3-month wild-type period, mortality ratios were calculated by dividing the number of COVID-19 deaths that occurred during the winter Omicron period by the number of COVID-19 deaths that occurred during the wild-type period. Subgroup analyses were performed by cancer site (eTable 1 in Supplement 1 ). All P values were 2-sided and considered statistically significant at P < .05. Data were analyzed from July 21 through August 31, 2022.

A total of 54 692 patients with cancer and 1 008 510 members of the general public died from COVID-19 from March 1, 2020 to May 31, 2022 (eTable 2 in Supplement 1 ). The study sample included 34 350 patients with cancer (14 498 females [42.2%] and 19 852 males [57.8%]) and 628 156 members of the general public (276 878 females [44.1%] and 351 278 males [55.9%]) who died from COVID-19 when the wild-type (December 2020-February 2021), Delta (July 2021-November 2021), and winter Omicron (December 2021-February 2022) variants were predominant. The Table shows the baseline characteristics of individuals included in the study.

Among patients with cancer, the greatest number of COVID-19 deaths per month occurred during the winter Omicron period in January 2022 (n = 5958). In January 2022, at the peak of the winter Omicron period, there were 18% more deaths compared with the peak of the wild-type period (January 2021) among patients with cancer ( Figure 1 ).

In contrast, among the general public, the greatest number of COVID-19 deaths per month occurred during the wild-type period in January 2021 (n = 105 327). In January 2022, at the peak of the winter Omicron period, there were 21% fewer COVID-19 deaths compared with the peak of the wild-type period among the general population ( Figure 1 ).

Among patients with cancer aged younger than 80 years, the greatest number of COVID-19 deaths per month occurred during the winter Omicron period. Among patients with cancer aged younger than 50 years, 50 to 59 years, 60 to 69 years, and 70 to 79 years, the number of COVID-19 deaths per month at the peak of the winter Omicron period was 64%, 62%, 31%, and 16% greater, respectively, compared with the number of COVID-19 deaths per month at the peak of the wild-type period (eFigure in Supplement 1 ).

In the general population, the greatest number of COVID-19 deaths per month occurred during the Delta period among individuals aged younger than 50 years and 50 to 59 years. For individuals 60 to 69, 70 to 79, and 80 years or older, the greatest number of COVID-19 deaths per month occurred during the wild-type period (eFigure in Supplement 1 ).

More patients with cancer died from COVID-19 during the 3-month winter Omicron period (n = 12 877) vs the wild-type period (n = 12 440). In contrast, there were 29% fewer COVID-19 deaths in the general population during the winter Omicron period (n = 178 509) vs wild-type period (n = 251 714).

Patients with cancer experienced significantly greater COVID-19 mortality during the Omicron period when compared with the wild-type period (mortality ratio, 1.04; 95% CI, 1.02-1.05), while the general US population experienced decreased mortality during the Omicron period relative to the wild-type period (mortality ratio, 0.69; 95% CI, 0.69-0.70) ( Figure 2 ). This finding was consistent across all cancer sites evaluated with the exception of brain (mortality ratio, 0.77; 95% CI, 0.65-0.90), thyroid (mortality ratio, 0.76 (95% CI, 0.54-0.99), and bladder (mortality ratio, 0.58; 95% CI, 0.52-0.65) cancers. Of note, COVID-19 mortality increased the most, by 38% (mortality ratio, 1.38; 95% CI, 1.31-1.45), among patients with lymphoma during the winter Omicron vs wild-type periods.

Although infection with the SARS-CoV-2 Omicron variant has been found to be associated with a lower risk of hospital admission and death compared with previous variants among the general population, 8 , 9 more patients with cancer died during the winter Omicron period compared with the wild-type or Delta periods. In contrast, among the general population, COVID-19 mortality was highest during the wild-type period.

We also investigated differences in COVID-19 mortality by age group among patients with cancer and the general population. Among the general population, COVID-19 mortality was highest during the Delta period among individuals aged 60 years or younger and was highest during the wild-type period among individuals aged 60 years or older; these differences are likely explained by lower vaccination rates among younger adults compared with older adults during the Delta period. 14 In contrast with the general population, patients with cancer aged younger than 80 years—who had access to COVID-19 vaccines, boosters, and antiviral agents prior to the winter Omicron period—experienced the highest COVID-19 mortality during the winter Omicron period compared with the wild-type and Delta periods.

The disparate burden of COVID-19 mortality experienced by patients with cancer compared with the general public during the winter Omicron period is likely explained by the combined effects of greater SARS-CoV-2 exposure during the winter Omicron period (owing to the increased transmissibility of the Omicron variant 6 , 7 and the relaxation of policies to prevent SARS-CoV-2 transmission), reduced effectiveness of COVID-19 vaccines in patients with cancer, 4 , 15 and greater risk of severe COVID-19 among patients with cancer. 2 - 4 Patients with lymphoma had the largest increase in COVID-19 mortality during the winter Omicron vs wild-type periods, which is consistent with literature reporting reduced vaccine effectiveness against the Omicron variant in this population. 4 , 16

A limitation of this study is that the number of patients with cancer who died from COVID-19 in the CDC WONDER database is likely an underestimate, as patients with a remote history of cancer may not have had cancer recorded in their death certificate and may not have been included in the study cohort. Additionally, the CDC WONDER database does not include information on individuals’ vaccination status or cancer staging. Lastly, COVID-19–related delays in cancer diagnoses and treatment may have also contributed to an increase in COVID-19 mortality among patients with cancer during the winter Omicron period.

This cross-sectional study found that, while the general US population experienced a large reduction in COVID-19 mortality during the winter Omicron period, patients with cancer experienced the highest COVID-19 mortality during the winter Omicron period likely due to increased SARS-CoV-2 exposure during this period combined with the reduced effectiveness of COVID-19 vaccines and increased risk of COVID-19 mortality in this population. With future COVID-19 waves imminent, strategies to protect those at highest risk should remain a high priority, even during future pandemic waves with less virulent SARS-CoV-2 variants.

Accepted for Publication: June 2, 2023.

Published Online: August 31, 2023. doi:10.1001/jamaoncol.2023.3066

Corresponding Author: Chi-Fu Jeffrey Yang, MD, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114 ( [email protected] ).

Author Contributions: Ms Potter and Dr Yang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Potter, Vaddaraju, Bajaj, Yang.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Potter, Venkateswaran, Yang.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Potter, Vaddaraju, Venkateswaran, Mansur, Kiang, Yang.

Administrative, technical, or material support: Bajaj.

Supervision: Jena, Yang.

Conflict of Interest Disclosures: Dr Jena reported receiving personal fees from Bioverativ, Merck, Janssen, Edwards Life Sciences, Amgen, Eisai, Otsuka, Vertex Pharmaceuticals, Sage Therapeutics, Precision Health Economics, Analysis Group, Harry Walker Agency, All American Entertainment, Freakonomics M.D., and Doubleday Books outside the submitted work. No other disclosures were reported.

Data Sharing Statement: See Supplement 2 .

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COVID-19 after vaccination doesn't raise risk of autoimmune disease, data suggest

A study of 1.8 million adults published in JAMA Network Open suggests that—except for a slightly higher risk of inflammatory bowel disease and blistering skin disorders in a subgroup hospitalized for SARS-CoV-2 Omicron variant infection—Delta or Omicron BA.1 or BA.2 infection in highly vaccinated adults doesn't significantly raise the long-term risk of autoimmune diseases.

Led by investigators from the National Centre for Infectious Diseases in Singapore, the study team used the SARS-CoV-2 registry and a healthcare claims database to compare the long-term risk of new autoimmune diseases after Delta or Omicron BA.1 or BA.2 infection in recipients of COVID-19 vaccines and boosters with that in uninfected controls. The study period was September 2021 to March 2022, with a 300-day follow-up.

Of all participants, 27.2% had COVID-19, 72.8% were controls, 51.9% were women, and the average age was 49 years.

"Studies have reported increased risk of autoimmune sequelae after SARS-CoV-2 infection," the researchers wrote. "However, risk may potentially be attenuated by milder Omicron (B.1.1.529) variant infection and availability of booster vaccination."

Boosters may lower risk of new autoimmune disease

During Delta predominance, 104,179 participants had COVID-19 infections and 666,575 were controls, while 375,903 and 619,379 controls, respectively were infected during Omicron predominance. A total of 81.1% of infected participants had completed the primary two-dose COVID-19 mRNA vaccine series amid the Delta era, and 74.6% received boosters during the Omicron period.

Continued surveillance for autoimmune conditions arising after COVID-19 is still necessary during the Omicron variant era.

A significantly higher risk of 12 studied autoimmune diseases wasn't observed during the Delta or Omicron periods, except for inflammatory bowel disease (adjusted hazard ratio [aHR], 2.23) and bullous (blistering) skin disorders (aHR, 4.88) in hospitalized COVID-19 patients amid Omicron. An elevated risk of vasculitis was documented in vaccinated Omicron patients (aHR, 5.74) but not those who received boosters.

The study authors concluded, "Continued surveillance for autoimmune conditions arising after COVID-19 is still necessary during the Omicron variant era."

Narrow-spectrum drug shows promise against C diff infection in phase 2 trial

Biopharmaceutical company Crestone Pharmaceuticals last week announced positive topline results from a phase 2 trial of its investigational drug treatment for Clostridioides difficile infection (CDI).

The trial evaluated the safety and efficacy of two different dosages of CRS3123, a small-molecule protein synthesis inhibitor, administered twice daily in adults diagnosed with a primary episode or first recurrence of CDI. Vancomycin was the comparator drug.

Among the 43 patients in the primary intention-to-treat analysis, 28 of 29 (97%) who received one of the two dosages of CRS3123 achieved clinical cure at the day 12 test-of-cure visit, compared with 13 of 14 (93%) who were treated with vancomycin. In addition, only 4% of CRS3123 patients experienced CDI recurrence at day 40, compared with 23% in the vancomycin group. CRS3121 was also well-tolerated, with no serious treatment-emergent adverse events reported.

Minimal microbiome disruption

One of the advantages of CRS3123 over current therapies is its narrow spectrum, which enables it to target C difficile bacteria and inhibit toxin production with minimal disruption to other microbes in the gut. Vancomycin is a broad-spectrum antibiotic known to disrupt the gut microbiome.

CDI is the most common healthcare-associated infection in the United States, with an estimated 500,000 cases occurring each year. Roughly 1 in 6 CDI patients experience a recurrence within 2 to 8 weeks.

"Treatment of C. difficile infection remains in urgent need of agents that spare normal gut microbes, so they can reconstitute the microbiome and prevent further recurrences of CDI," lead trial investigator Thomas Louie, MD, of the University of Calgary said in a company press release . "The findings of this study support CRS3123 as such a candidate for further development."

Crestone also announced that, based on the results of the trial, the National Institute of Allergy and Infectious Diseases will provide $4.5 million in new funding for microbiome analyses, manufacturing process optimization, and other phase 2 supporting studies.

Treatment of C. difficile infection remains in urgent need of agents that spare normal gut microbes, so they can reconstitute the microbiome and prevent further recurrences of CDI.

Modeling study touts cost savings of RSV vaccination in older adults

Targeting older adults with underlying health conditions—as opposed to the general population—for respiratory syncytial virus (RSV) vaccines would reduce spending and prevent illness, according to a modeling study yesterday in the Canadian Medical Association Journal ( CMAJ ).

The study compared the cost-effectiveness of different vaccine programs in different age groups with different medical risks.

The model considered a population of 100,000 people aged 50 years and older. Vaccine characteristics were based on RSV vaccines authorized in Canada as of May 2024, with vaccine protection assumed to last 2 years.

The cost-effectiveness threshold was $50,000 per quality-adjusted life year (QALY).

Optimal in oldest adults with underlying conditions

According to the study authors, without vaccination, they projected 131,389 (95% credible interval [CrI], 120,070 to 143,581) medically attended RSV cases, 12,068 (95% CrI, 10,324 to 13,883) hospital admissions, and 1,015 (95% CrI, 617 to 1,450) deaths annually among Canadians aged 60 years and older.

Vaccinating strategies based on age plus risk for RSV-related complications were projected to avert a median of 20% to 31% of outpatient cases, 38% to 42% of hospital cases, and 39% to 42% of deaths, the authors said. Vaccines were most cost-effective, according to the model, when given to adults ages 70 and older, with one or more chronic medical condition.

We found that vaccination of older adults may be less costly and more effective than no vaccination.

"We found that vaccination of older adults may be less costly and more effective than no vaccination and that vaccinating people aged 70 years and older with chronic medical conditions is likely to be cost-effective based on commonly used cost-effectiveness thresholds," said Ashleigh Tuite, PhD from the Public Health Agency of Canada in a CMAJ press release .

"Strategies focused on adults with underlying medical conditions that place them at increased risk of RSV disease are more likely to be cost-effective than general age-based strategies," Tuite added.

Quick takes: NC measles case, flu vaccine supply estimate, polio vax campaign shifts to northern Gaza

The North Carolina Department of Health and Human Services yesterday reported the state’s first measles case since 2018. In a statement , health officials said the patient is a child in Mecklenburg County who was probably exposed during international travel. The parents kept the child home after returning to the state, except for one medical visit during which health providers took appropriate precautions. Cases in the United States are up sharply this year, part of a global rise in cases.
The US Centers for Disease Control and Prevention (CDC) last week in an update that it projects that vaccine manufacturers will supply the US market with 148 million flu vaccine doses for the 2024-2025 season, with trivalent (three-strain) vaccine making up all formulations. Vaccine makers aren’t reporting any manufacturing delays, the CDC said. Nearly all (94%) of the supply will be thimerosal-free, and about 80% will be made using egg-based manufacturing technology. For comparison, the CDC had projected as many as 156.2 million to 170 million for the 2023-2024 season. It emphasized that projections may change as the season progresses.
A polio vaccination campaign under way in Gaza has now moved to the northern part of the country, with activities slated to last until September 12, World Health Organization (WHO) Director-General Tedros Adhanom Ghebreyesus, PhD, said today on X . He called on groups in the region to maintain a humanitarian pause and respect the safety of healthcare workers. Following the recent detection of circulating vaccine-derived poliovirus type 2 in a child from Gaza, along with environmental positives, health groups planned and launched a two-round vaccine campaign earlier this month targeting 640,000 children.

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Research article
Open access
Published: 13 September 2024

Adiposity and mortality among intensive care patients with COVID-19 and non-COVID-19 respiratory conditions: a cross-context comparison study in the UK

Joshua A. Bell 1 , 2 na1 ,
David Carslake 1 , 2 na1 ,
Amanda Hughes 1 , 2 ,
Kate Tilling 1 , 2 ,
James W. Dodd 1 , 3 ,
James C. Doidge 4 ,
David A. Harrison 4 na2 ,
Kathryn M. Rowan 4 na2 &
George Davey Smith 1 , 2 na2

BMC Medicine volume 22 , Article number: 391 ( 2024 ) Cite this article

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Metrics details

Adiposity shows opposing associations with mortality within COVID-19 versus non-COVID-19 respiratory conditions. We assessed the likely causality of adiposity for mortality among intensive care patients with COVID-19 versus non-COVID-19 by examining the consistency of associations across temporal and geographical contexts where biases vary.

We used data from 297 intensive care units (ICUs) in England, Wales, and Northern Ireland (Intensive Care National Audit and Research Centre Case Mix Programme). We examined associations of body mass index (BMI) with 30-day mortality, overall and by date and region of ICU admission, among patients admitted with COVID-19 ( N = 34,701; February 2020–August 2021) and non-COVID-19 respiratory conditions ( N = 25,205; February 2018–August 2019).

Compared with non-COVID-19 patients, COVID-19 patients were younger, less often of a white ethnic group, and more often with extreme obesity. COVID-19 patients had fewer comorbidities but higher mortality. Socio-demographic and comorbidity factors and their associations with BMI and mortality varied more by date than region of ICU admission. Among COVID-19 patients, higher BMI was associated with excess mortality (hazard ratio (HR) per standard deviation (SD) = 1.05; 95% CI = 1.03–1.07). This was evident only for extreme obesity and only during February–April 2020 (HR = 1.52, 95% CI = 1.30–1.77 vs. recommended weight); this weakened thereafter. Among non-COVID-19 patients, higher BMI was associated with lower mortality (HR per SD = 0.83; 95% CI = 0.81–0.86), seen across all overweight/obesity groups and across dates and regions, albeit with a magnitude that varied over time.

Conclusions

Obesity is associated with higher mortality among COVID-19 patients, but lower mortality among non-COVID-19 respiratory patients. These associations appear vulnerable to confounding/selection bias in both patient groups, questioning the existence or stability of causal effects.

Peer Review reports

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the resultant coronavirus disease 2019 (COVID-19), continues to threaten public health [ 1 ]. Identifying modifiable causes of mortality among patients severely ill with COVID-19 remains a priority. In the post-2022 era, this task coincides with the need to manage possible dual surges of severe COVID-19 and influenza-related respiratory diseases, and studies must now consider the impact of risk factors within both conditions to guide appropriate messaging.

Higher adiposity likely causes numerous non-infectious diseases [ 2 ] and large-scale evidence, including from Mendelian randomisation (MR) studies, now supports adiposity as a likely cause of SARS-CoV-2 infection and hospitalisation with severe COVID-19, with the available (non-MR) studies suggesting higher adiposity is associated with higher mortality with COVID-19 [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. This contrasts sharply with evidence on patients with non-COVID-19 respiratory conditions, with studies suggesting that higher adiposity, including extreme obesity, is associated with lower mortality—although the one available MR study suggests that the causal effect might be positive [ 12 , 13 , 14 ]. Nearly all studies estimating the potential effects of adiposity on mortality among patients hospitalised with severe respiratory disease (COVID-19 and non-COVID-19) have been estimated via conventional observational studies which use multivariable adjustments to address confounding and other biases. Such adjustments rarely fully remove bias because they rely on implausible assumptions of no unmeasured confounding and no measurement error—issues which could explain modest effect sizes commonly observed [ 15 , 16 ]. MR is often unfeasible for assessing mortality in hospital settings given the lack of genetic data at scale in clinically selected samples [ 5 , 7 , 8 , 10 , 11 , 14 ]. As a result, the potential for unmeasured/residual confounding, reverse causation, and selection bias makes causality difficult to infer.

Cross-context comparison is an underutilised tool for causal inference in observational studies. This approach involves directly comparing associations between exposures and outcomes across temporal or geographical contexts where confounding or selection pressures vary. Consistency in the direction and magnitude of exposure-outcome associations across contexts, despite variation in the impact of confounding/selection, builds confidence in the causality of those exposure-outcome associations [ 17 , 18 ]. Previously, comparisons across geographical contexts where socioeconomic gradients in exposures differ helped to affirm the likely effects of gestational blood glucose on offspring birthweight and of breastfeeding on offspring intelligence, and reduce confidence in the suggested benefits of breastfeeding for offspring adiposity and blood pressure [ 17 , 19 ]. Cross-context comparisons have not been formally applied to assess the causality of adiposity for mortality among patients severely ill with COVID-19 and non-COVID-19 respiratory conditions, but this is now feasible within the United Kingdom (UK) setting given that the prevalence and impact of confounding/selection factors among patients may differ by time and geography [ 20 , 21 , 22 ].

Cross-context comparison requires variation in bias across contexts (time/place), i.e. it can assess the impact of context-varying bias, but not the impact of context-stable bias. Given uniquely rapid changes in COVID-19 management over time and geography, we may expect context-varying bias to influence adiposity-mortality associations more among COVID-19 patients than non-COVID-19 respiratory patients. Moreover, the influence of context-stable bias such as reverse causation, which can be expected to be very stable in situations where external confounders have differing effects, may influence adiposity-mortality associations more among non-COVID-19 respiratory patients. This may be assessable by comparing characteristics of COVID-19 and non-COVID-19 respiratory patients and examining how adiposity-mortality associations differ between them.

The Intensive Care National Audit and Research Centre (ICNARC) has coordinated the collection of data on nearly all patients admitted to intensive care units (ICUs) in England, Wales, and Northern Ireland since 2010 [ 20 , 21 , 23 , 24 ]. In this study, we aimed to assess the causality of adiposity for mortality among people hospitalised with severe COVID-19 and non-COVID-19 respiratory conditions using a cross-context comparison approach. We used nationally representative data from the ICNARC Case Mix Programme on patients admitted to ICU with COVID-19 (~ 33,000 patients between February 2020 and August 2021) and with non-COVID-19 respiratory conditions (~ 25,000 patients between February 2018 and August 2019 (pre-pandemic)). Within each patient group, we estimated the overall association between adiposity and mortality. We then examined whether socio-demographic and comorbidity indicators (potential confounding/selection factors) and their associations with adiposity and mortality vary by date and geographical region of ICU admission. Lastly, we examined whether adiposity-mortality associations are consistent across dates and regions with varying confounding/selection pressures.

Study population

We included patients aged ≥ 16 years admitted to any of 280 ICUs across England, Wales, and Northern Ireland with COVID-19 confirmed at or after admission between 5 February 2020 and 1 August 2021, plus adult patients admitted to any of 266 ICUs with respiratory diseases which were not COVID-19, including viral and bacterial pneumonia, bronchitis, bronchiolitis, or laryngotracheobronchitis (encompassing suspected/confirmed influenza) between 1 February 2018 and 31 August 2019 (Fig. 1 ). These start/end dates for COVID-19 admissions were based on data availability when this study commenced; the start/end dates for non-COVID-19 admissions were chosen to match the first/last months of COVID-19 admissions in a similarly long pre-pandemic period. Non-overlapping periods were used for primary analyses because these were expected to involve less disease misclassification and less estimate imprecision for non-COVID-19 respiratory conditions given their relative rarity during COVID-19 waves (e.g. given lockdowns). Data were from ICUs participating in the Case Mix Programme: the national clinical audit covering all National Health Service (NHS) adult, general intensive care, and combined intensive care/high dependency units, plus some additional specialist ICUs and standalone high dependency units, coordinated by ICNARC [ 23 , 24 , 25 ]. This audit excludes all paediatric and neonatal ICUs and ICUs in Scotland. The same individual patient could have one COVID-19 record and one or more non-COVID-19 records. Approval for the collection and use of patient-identifiable data without consent in the Case Mix Programme was obtained from the Confidentiality Advisory Group of the Health Research Authority under Sect. 251 of the NHS Act 2006 (approval number PIAG2–10[f]/2005). All data were pseudonymised (patient identifiers removed) prior to extraction for this research.

Numbers of patients admitted to any of 280 ICUs in England, Wales, and Northern Ireland with COVID-19 (5 Feb 2020 to 1 Aug 2021) and to any of 266 ICUs with non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019 and 1 Feb 2020 to 30 June 2021) participating in the ICNARC Case Mix Programme (297 ICUs overall). E/M East/Midlands, N North, S South. N = 34,701 COVID-19 patients, 25,205 non-COVID-19 patients before the pandemic and 8241 non-COVID-19 patients during the pandemic

Adiposity (exposure)

Body mass index (BMI, as kg/m 2 ) was calculated from height and weight which were recorded or estimated by clinicians on admission to ICU. Patient severity and clinician workload often make directly measuring height and weight in the ICU infeasible [ 26 ]. About half of the height and weight recordings were visually estimated by clinical staff rather than directly measured for both COVID-19 and non-COVID-19 respiratory patients (53% of COVID-19 patients and 54% of non-COVID-19 respiratory patients had at least one of the height and weight estimated, and 38% of COVID-19 patients and 37% of non-COVID-19 respiratory patients had both height and weight estimated). Previous analyses of ICNARC ICU data supported similar associations of BMI with mortality based on measured vs. estimated values of height and weight [ 27 ], and we therefore expected little impact of this measurement error in BMI on BMI-mortality associations aside from potential bias towards the null. We first examined BMI as a continuous variable (per standard deviation (SD) based on z-scores derived separately within COVID-19 and non-COVID-19 groups using all available data). These z-scores were analysed linearly and as cubic splines with five knots at the recommended percentiles [ 28 ]. Secondly, we analysed BMI as a categorical variable based on World Health Organization classifications of underweight (< 18.5 kg/m 2 ), recommended weight (18.5 to < 25 kg/m 2 ), overweight (25.0 to < 30 kg/m 2 ), obesity class 1 (30.0 to < 35 kg/m 2 ), obesity class 2 (35.0 to < 40 kg/m 2 ), and obesity class 3 + (≥ 40.0 kg/m 2 ).

Thirty-day all-cause mortality (outcome)

We included deaths in ICU from any cause (reported directly to ICNARC by hospital staff). Within any continuous hospital stay, a patient’s time in ICU was considered to run from their first ICU admission until their last ICU discharge. We censored analysis time at 30 days after (first) admission to ICU and assumed that patients discharged from ICU (for the last time within a hospital stay) lived to be censored at 30 days post-admission [ 20 ]. This was because censoring discharged patients at discharge would have selectively removed the healthiest patients from follow-up (‘informative’ censoring). On the other hand, discharged patients who were subsequently re-admitted to ICU within the same hospital stay were treated as if they had remained in ICU because assuming their survival to 30 days from the first admission would have included many who in fact died by this time. Deaths occurring more than 30 days post-admission were excluded due to the generally high short-term mortality rate within ICU settings; later deaths are rare and risk introducing unrelated causes of death, while the assumption of survival in discharged patients also becomes less plausible with time [ 23 ]. Patients with unknown outcomes were assumed to have remained alive in the ICU at the end of their follow-up and were censored at the end of their follow-up (or at 30 days if their ICU stay already exceeded this). Follow-up which ended on the date of admission was considered to have lasted 0.5 days.

Socio-demographic and comorbidity indicators (confounding and selection factors)

Measured confounders (hypothesised to cause adiposity and mortality) included patient age, sex, ethnic group, socioeconomic deprivation, and the geographical region and date of ICU admission. Age was used as a continuous variable with a potentially non-linear effect modelled with cubic splines with five knots at the recommended percentiles [ 28 ]. Ethnic groups included ‘White’, ‘Black’, ‘Asian’ (specifically South Asian), and ‘mixed/other’ (including East Asian), based on 2011 census categories used for NHS data entry. Deprivation was based on quintiles of the Index of Multiple Deprivation (IMD) which summarises several area-level deprivation indicators including income, education, and employment for neighbourhood-level areas within each UK country, derived from patients’ postcodes. Geographic region was grouped as (1) London; (2) East England and Midlands; (3) North-East England, North-West England, and Yorkshire; (4) South-East England and South-West England; (5) Wales; and (6) Northern Ireland. Admission date was considered as six periods for COVID-19 patients: (1) 5 February–30 April 2020; (2) 1 May–31 July 2020; (3) 1 August–31 October 2020; (4) 1 November–31 January 2021; (5) 1 February–30 April 2021; and (6) 1 May–1 August 2021; and six corresponding periods 2 years earlier for non-COVID-19 respiratory patients: (1) 1 February–30 April 2018; (2) 1 May–31 July 2018; (3) 1 August–31 October 2018; (4) 1 November 2018–31 January 2019; (5) 1 February–30 April 2019; and (6) 1 May–31 August 2019.

Several comorbidity indicators were recorded and considered here as selection factors. These are factors thought likely to influence selection into the study (through admission to ICU with COVID-19 or non-COVID-19 respiratory disease) and which may also influence mortality. If adiposity also affects the probability of selection, an induced association between adiposity and the selection factor will bias the estimated effect of adiposity on mortality. However, these selection factors are very plausibly caused by adiposity (whereas adjusted-for covariates are either causal of adiposity or share a common cause with it). Adjustment for selection factors would cause bias by blocking a mediated effect of adiposity on mortality. Additionally, if the selection factor has other causes which also influence mortality, adjustment for the selection factor may induce an association between adiposity and these other causes, further biasing estimates, a mechanism referred to as collider bias [ 22 , 29 ]. Rather than adjusting for these selection factors, we therefore only measured their associations with adiposity and mortality, as an indication of the potential for bias in the sample. The comorbidity variables examined as selection factors included severe comorbid diseases documented in the patient notes as being present within the 6 months prior to ICU admission: respiratory disease (shortness of breath with light activity or home ventilation), cardiovascular disease (symptoms at rest), end-stage renal disease requiring chronic renal replacement therapy, liver disease (biopsy-proven cirrhosis, portal hypertension or hepatic encephalopathy), metastatic disease, haematological disease (acute or chronic leukaemia, multiple myeloma, or lymphoma) and an immunocompromised state (chemotherapy, radiotherapy, daily high-dose steroids, AIDS, or acquired immunohumoral or cellular immune deficiency). As indicators of acute severity within ICU, we included (1) the acute physiology and chronic health evaluation-II (APACHE-II) score and the ICNARC extreme physiology score which summarise patient physiology within the first 24 h in ICU (higher values being adverse), (2) the ratio of arterial oxygen partial pressure (PaO 2 ) to fractional inspired oxygen (FiO 2 ) (kPa units) calculated from arterial blood gas with the lowest PaO 2 in the first 24 h, and (3) the number of days on which advanced respiratory support was received (including ventilation). Additionally, we examined two indicators of pre-ICU health status: whether the patient was physically dependent on others for activities of daily living prior to admission (some dependency; total dependency; or independent), and whether the patient had any past severe illness (defined as having a zero value of the APACHE-II past medical history weighting, grouped as yes/no).

Statistical approach

Analyses informing cross-context comparisons.

We described the socio-demographic and comorbidity indicators (confounding/selection factors) of patients admitted to ICU with COVID-19 and non-COVID-19 respiratory conditions via proportions or means and standard deviations. Patient groups were first described overall, and then with separate stratification by time period and region of ICU admission to examine temporal/geographic trends in patient characteristics. These temporal trend descriptions excluded COVID-19 patients admitted during February 2020 and August 2021 due to low case counts (5 and 18 cases, respectively); these patients were not excluded from subsequent regression models because those models use aggregated cases/months only.

We examined associations of socio-demographic and comorbidity indicators (independent variables in separate models) with BMI using linear regression models, and with mortality using Gompertz proportional hazards models, each adjusted for age (cubic splines) and sex. Time since ICU admission was used as the time axis in the survival analyses. The patient group (COVID-19 vs. non-COVID-19) was interacted with all terms allowing separate estimates for each group and a test for heterogeneity between the estimates (using Stata’s post-estimation ‘test’ command). Gompertz models were used as a parametric alternative to semi-parametric Cox models to relax the unrealistic assumption of identical baseline hazards in each patient group and a Gompertz distribution was preferred to the more commonly used Weibull distribution because it gave a closer fit to the observed hazard and survivor functions. These models were then additionally stratified by ICU admission period or region, separately, each in 6 strata as described above. Interactions and independent baseline hazards were used such that separate estimates were made in each combination of stratum and patient condition. Heterogeneity tests were carried out within each patient group, comparing estimates in the six strata.

Analyses for overall adiposity-mortality associations and cross-context comparisons

We used Gompertz parametric survival models to estimate the overall association of BMI with 30-day all-cause mortality among ICU patients with COVID-19 and non-COVID-19 respiratory conditions. BMI was first modelled linearly in SD units, then categorically relative to recommended weight and finally, for plotting, as cubic splines of the z -scores with five knots at the recommended percentiles [ 28 ]. We adjusted for age (cubic splines), sex, ethnic group (4 categories), deprivation (quintile categories), admission period (6 categories), and admission region (6 categories). The time axis, entry, and censoring for these Gompertz models were as described above. The proportional hazards assumption was tested for the linear Gompertz models by splitting follow-up time at the median time to death (10 days for COVID-19 patients and 4 days for non-COVID-19 patients) and comparing hazard ratios (HRs) between these 2 periods. As sensitivity analyses for non-COVID-19 respiratory patients we (1) additionally excluded patients admitted to ICU with bacterial pneumonia (thus considering only viral respiratory conditions for comparison with COVID-19) and (2) considered non-COVID-19 respiratory patients who were admitted during the same months as COVID-19 patients (1 February 2020 to 30 June 2021, since data on non-COVID-19 respiratory patients in July 2021 were not available). In two further sensitivity analyses, we repeated the analyses described above including only those patients (i) with measured (not estimated) height and weight or (ii) who were not dependent on others for the activities of daily living prior to admission.

We then repeated Gompertz models of BMI with mortality among COVID-19 and non-COVID-19 respiratory patients with the same confounding adjustments but with separate stratification by admission date and region, using the groupings noted above. The stratifying variable was omitted from the adjustment set in each analysis. Interactions and independent baseline hazards were used to make estimates equivalent to those from completely separate models in each stratum. Stata’s post-estimation ‘test’ command was used to test for interaction between BMI and each stratifying variable in relation to mortality (smaller P -values indicate stronger evidence that associations vary by date/region). Analyses were conducted using Stata 17 and code is available at https://github.com/Carslake/ICU_BMI_Covid/ .

Characteristics of ICU patients with COVID-19 and non-COVID-19 respiratory conditions

Of 39,426 adult patients admitted to ICU with COVID-19 between 5 February 2020 and 1 August 2021, 34,701 (88%) were eligible for the main analyses based on having data on BMI (exposure), sociodemographic adjustment variables, and 30-day ICU mortality (outcome) (Fig. 2 ). Of 27,328 adult patients admitted to ICU with non-COVID-19 respiratory conditions between 1 February 2018 and 31 August 2019, 25,205 (92%) were eligible for the main analyses based on the same criteria. For both patient groups, data were most often missing for BMI or ethnicity. For the descriptive analysis of comorbidities, patients were further excluded if they were missing the variable in question; this excluded up to 7% of COVID-19 patients and 6% of non-COVID-19 patients.

Flow of patients admitted to ICU with COVID-19 and non-COVID-19 respiratory conditions. ICU Intensive care unit, BMI body mass index

Compared with non-COVID-19 respiratory patients who were admitted to ICU before the COVID-19 pandemic (the main comparison group for analyses), COVID-19 patients were younger, more often male, more deprived and substantially less often of a white ethnic group (Table 1 ). COVID-19 patients had a more than twofold higher proportion of Black, and a threefold higher proportion of Asian and mixed, ethnic groups. All of these sociodemographic variables were stable over time among non-COVID-19 patients (Additional files 1 – 2 : Figs. S1–S2), but there were clear temporal differences among COVID-19 patients. The average age declined, particularly after December 2020 (when UK vaccination programmes began). The proportion of males also declined. Deprivation fluctuated considerably but rose overall. The ethnic composition of COVID-19 patients fluctuated, particularly the relative proportions of white and Asian patients, without showing a clear overall trend. Regional variation in age and sex was minimal in both patient groups, but regions differed in levels of deprivation and ethnic composition (Additional files 3 – 5 : Figs. S3–S5). Regional patterns in deprivation were similar for COVID-19 and non-COVID-19 patients, but regional differences in ethnic composition (non-white ethnic groups being most common in London and least common in Northern Ireland) were more pronounced among COVID-19 patients.

COVID-19 patients had a higher mean BMI, at 30.9 kg/m 2 vs. 27.6 kg/m 2 in non-COVID-19 patients (Table 1 ). They presented as underweight one-sixth as often and as recommended weight nearly half as often, yet presented with obesity, particularly classes 2 and 3 + obesity, about twice as often. Mean BMI was relatively stable over time among non-COVID-19 patients but showed a modest increase among COVID-19 patients, starting at 29.8 kg/m 2 in March 2020 and ending at 31.7 kg/m 2 in July 2021 (Additional file 2 : Fig. S2). This corresponded to a fall in the proportion of recommended weight and overweight patients while the proportion of patients with obesity rose, e.g. from 7.6% in March 2020 to 13.2% in July 2021 for class 3 + obesity (Additional file 6 : Fig. S6). There was regional heterogeneity in BMI, with a lower mean in London reflected in corresponding differences across the BMI categories. This heterogeneity was apparent among both patient groups but was more pronounced among COVID-19 patients (Additional file 5 : Fig. S5, Additional file 7 : Fig. S7). The proportion of BMI which was estimated rather than measured was similar overall for COVID-19 and non-COVID-19 patients but was a little more variable over time in the former group, with the most estimation in spring 2020 and winter 2020–2021, when admissions were highest (Fig. 1 and Additional file 8 : Fig. S8). Regional variation was similar in COVID-19 and non-COVID-19 patients, with weight most frequently estimated in Northern Ireland (Additional file 9 : Fig. S9).

COVID-19 patients had less prior severe illness than non-COVID-19 patients (9.0% vs. 21.4%) and less pre-ICU dependency on others for activities of daily living (11.3% vs. 33.9%; Table 1 ). They also presented less often with severe comorbidities including cardiovascular, respiratory, liver, renal, metastatic, haematological, respiratory, and immunocompromising diseases, compared to non-COVID-19 patients. These pre-existing conditions showed limited variation over time and between regions, to a similar extent for COVID-19 and non-COVID-19 patients (Additional files 6 and 10 : Figs. S6 and S10). COVID-19 patients had a lower PaO 2 /FiO 2 ratio and more days of advanced respiratory support, indicating worse respiratory function than non-COVID-19 patients, but lower overall severity of illness as indicated by mean APACHE-II and ICNARC scores (Table 1 ). While these severity indicators remained relatively constant over time among non-COVID-19 patients, they all declined over time among COVID-19 patients (indicating less severe illness except for the PaO 2 /FiO 2 ratio which suggests the opposite; Additional file 8 : Fig. S8, Additional file 11 : Fig. S11). The ICNARC physiological severity score and the days of advanced respiratory support both showed more variability between regions in COVID-19 patients than among non-COVID-19 patients (Additional files 9 and 12 : Figs. S9 and S12). In contrast, the PaO 2 /FiO 2 ratio was more variable between regions among non-COVID-19 patients.

Despite being younger with fewer comorbidities, COVID-19 patients experienced higher 30-day ICU mortality at 34.3% vs. 22.4% for non-COVID-19 patients (Table 1 ). This difference declined substantially over time, as mortality among COVID-19 patients fell from 41.4% in March 2020 to 14.5% in July 2021 (Additional file 8 : Fig. S8). In contrast, mortality among non-COVID-19 respiratory patients was relatively stable over time, at 23.9% in March 2018 and 21.0% in July 2019. Regional variation in mortality was similar for both conditions, being highest in Wales (39.8% of COVID-19 patients and 27.5% of non-COVID-19 patients; Additional file 13 : Fig. S13).

The characteristics of non-COVID-19 patients admitted to ICU during the COVID-19 pandemic (a smaller comparison group for sensitivity analyses) were not substantially different from those of non-COVID-19 patients admitted pre-pandemic (Table 1 ). They were a little more similar to COVID-19 patients, e.g. younger age, higher proportions of non-white ethnic groups, higher mean BMI, and less historical illness, indicating a greater potential for misclassification with COVID-19 in this overlapping admissions period.

Associations of confounding/selection factors with BMI and mortality among ICU patients with COVID-19 and non-COVID-19 respiratory conditions

Among COVID-19 patients, non-white ethnic groups were associated with lower BMI, particularly the Asian group at − 2.77 kg/m 2 (95% confidence interval (CI) = − 2.99, − 2.56) (Additional file 14 : Table S1). There was considerable temporal and regional heterogeneity in this association, e.g. Asian ethnic group was most associated with lower BMI in South England (excluding London) and Northern Ireland (Additional files 15 – 16 : Tables S2–S3). Among non-COVID-19 patients, an association between BMI and ethnic group was also present but the association, and its temporal and regional heterogeneity, were weaker (Additional files 17 – 18 : Tables S4–S5). While the temporal and regional heterogeneity among COVID-19 patients was strongest for the association between Asian ethnicity and BMI, heterogeneity among non-COVID-19 patients was greatest for the association between Black ethnicity and BMI.

Asian ethnicity was associated with higher mortality in both patient groups but more so in COVID-19 patients (Additional file 14 : Table S1). There was no evidence of temporal or regional heterogeneity in this association (Additional files 19 – 22 : Tables S6–S9). Black ethnicity was associated with slightly higher mortality among COVID-19 patients (HR = 1.04, 95% CI = 0.97, 1.12) and lower mortality among non-COVID-19 patients (HR = 0.67, 95% CI = 0.54, 0.82) but the reverse was found among white patients (HR = 0.85, 95% CI = 0.82, 0.89 and HR = 1.12, 95% CI = 1.02, 1.23, respectively). Higher mortality among Black COVID-19 patients was apparent only in the first admissions period (February–April 2020, HR = 1.22, 95% CI = 1.09, 1.37); the association was null or slightly protective during each period thereafter.

Higher deprivation was similarly associated with higher BMI in COVID-19 and non-COVID-19 patients (Additional file 14 : Table S1). There was strong evidence of regional heterogeneity in this association among non-COVID-19 patients (Additional file 18 : Table S5) and a suggestion of temporal heterogeneity among COVID-19 patients (Additional file 15 : Table S2).

Deprivation was associated with higher mortality among COVID-19 patients but not among non-COVID-19 patients. These associations were largely consistent across regions and admission dates (Additional files 14 , 19 – 22 : Tables S1, S6–S9).

Most comorbidities were associated with lower BMI in both patient groups, but magnitudes were often higher among COVID-19 patients (Additional file 14 : Table S1). In contrast, pre-ICU dependency and comorbid respiratory disease (the definition of which includes home ventilation due to obesity-related sleep disorders) were associated with higher BMI in both groups. Heterogeneity tests indicated that the magnitude of many of these associations between BMI and comorbidities varied over time among COVID-19 patients (Additional file 15 : Table S2) but there was much less evidence of temporal heterogeneity among non-COVID-19 patients (Additional file 17 : Table S4). There was little evidence of regional heterogeneity in associations between BMI and comorbidities in either patient group (Additional files 16 and 18 : Tables S3 and S5).

Comorbidities were almost always associated with higher mortality (Additional file 14 : Table S1). The magnitude of these associations often differed between COVID-19 and non-COVID-19 patients, being usually stronger in non-COVID-19 patients. An interesting exception was renal disease, which was consistently associated with higher mortality among COVID-19 patients but lower mortality among non-COVID-19 patients. Other associations between comorbidities and mortality showed considerable temporal heterogeneity among COVID-19 patients, tending to increase over the study period (Additional file 19 : Table S6) but there was little temporal heterogeneity in the comorbidity-mortality association among non-COVID-19 patients (Additional file 21 : Table S8). There was some evidence of regional heterogeneity in the associations between comorbidities and mortality, particularly among non-COVID-19 patients (Additional files 20 and 22 : Tables S7 and S9).

Overall associations of BMI with mortality among ICU patients with COVID-19 and non-COVID-19 respiratory conditions

Among COVID-19 patients admitted to ICU, higher BMI (per SD, or 7.6 kg/m 2 ) was associated with a small excess ICU mortality (HR = 1.05; 95% CI = 1.03, 1.07; Table 2 ). Compared with recommended weight patients, underweight was associated with excess mortality at HR = 1.27 (95% CI = 1.04, 1.55), whereas there was little evidence that overweight, class 1 obesity, or class 2 obesity were associated with ICU mortality. Mortality was elevated with class 3 + obesity vs. recommended weight at HR = 1.22 (95% CI = 1.14, 1.32). Cubic spline models (Fig. 3 ) also indicated that the positive overall association between BMI and mortality derived mainly from those with a BMI over 35–40 kg/m 2 .

Association between BMI and mortality in COVID-19 and non-COVID-19 respiratory patients. BMI body mass index, HR hazard ratio, CI confidence interval. Results from parametric survival analyses with Gompertz baseline hazard functions. BMI was modelled as a cubic spline with five knots and converted back from condition-specific standard deviations to kg/m 2 for display. Survival time was censored at 30 days with patients discharged earlier assumed to survive to 30 days. Adjusted for sex, age (cubic spline), ethnic group, deprivation, admission period and admission region. Crosses indicate the truncation of plots at the 1st and 99th percentiles of condition-specific BMI z -scores

Among non-COVID-19 patients admitted to ICU, higher BMI (per SD, or 7.5 kg/m 2 ) was associated with lower ICU mortality (HR = 0.83; 95% CI = 0.81, 0.86). Compared with recommended weight patients, those who were underweight had substantial excess mortality at HR = 1.52 (95% CI = 1.36, 1.69), whereas patients who were overweight or with obesity had substantially lower mortality (e.g. HR = 0.72; 95% CI = 0.63, 0.82 for class 3 + obesity). The cubic spline models suggested progressively lower mortality with BMI up to BMI in excess of 35–40 kg/m 2 , above which mortality remained consistently low.

The pattern and magnitude of these associations were similar when (i) considering non-COVID-19 patients admitted during the same months as COVID-19 patients (Table 2 ), (ii) excluding non-COVID-19 patients admitted to ICU with bacterial pneumonia (thus considering only viral respiratory conditions; Table 2 ), (iii) considering only those patients with measured BMI (Additional file 23 : Table S10) or excluding patients who were dependent on others for the activities of daily living prior to admission (Additional file 24 : Table S11). The first and third of these suggested weakly that mortality in non-COVID-19 patients with class 3 + obesity might not be reduced to the degree suggested by the main analysis (but was still reduced relative to recommended weight). However, these estimates were less precise owing to smaller sample size and non-COVID-19 patients admitted during the pandemic may have been more prone to disease misclassification.

Proportional hazards tests splitting follow-up at the median time to death suggested that both the positive BMI-mortality association in COVID-19 patients and the negative one in non-COVID-19 patients attenuated towards the null with increasing time spent in ICU. This was rather more pronounced in non-COVID-19 patients (HR = 0.76, 95% CI = 0.72, 0.80 in the first 4 days in ICU; HR = 0.90, 95% CI = 0.86, 0.94 subsequently; P difference < 0.0001) than in COVID-19 patients (HR = 1.07, 95% CI = 1.04, 1.10 in the first 10 days in ICU; HR = 1.03, 95% CI = 1.00, 1.06 subsequently; P difference = 0.07). In the presence of non-proportional hazards, average HR can be sensitive to the censoring distribution [ 30 , 31 ]. In our main analyses, 13.3% of COVID-19 patients and 4.9% of non-COVID-19 patients were administratively censored at 30 days, with 7.6% and 4.0% of all recorded deaths, respectively, occurring after this censoring. Censoring before 30 days was very rare (1.2% of COVID-19 patients and 0.5% of non-COVID-19 patients). Importantly for our interest in heterogeneity, both forms of censoring were similar across regions and dates of admission, except for COVID-19 patients in the final period (May–August 2021), when there was less administrative censoring (4.4%) and more censoring before 30 days (20.2%).

Temporal cross-context comparison: associations of BMI with mortality among ICU patients with COVID-19 and non-COVID-19 respiratory conditions, by date of ICU admission

Among COVID-19 patients admitted to ICU, higher BMI (per SD) was associated with higher mortality (HR = 1.11, 95% CI = 1.06, 1.15) during February–April 2020 (Table 3 ); this association weakened in May–July 2020 and thereafter with a small positive association during the most populous period of November 2020–January 2021 (HR = 1.04, 95% CI = 1.01, 1.07; BMI-date interaction P = 0.015). Compared with recommended weight patients, mortality was highest for patients with class 3 + obesity in February–April 2020 (HR = 1.52, 95% CI = 1.30, 1.77), reducing thereafter. A small positive gradient in risk associated with overweight and classes 1 and 2 obesity (vs. recommended weight) was seen only during February–April 2020, with HRs for these BMI groups (versus recommended weight) < 1 or null across subsequent periods. Being underweight was associated with higher mortality in each period except May–July 2020, but estimates were imprecise given the rarity of underweight COVID-19 patients. The cubic spline plots of continuous BMI (Additional file 25 : Fig. S14) support the patterns observed for categories of BMI and mortality among COVID-19 patients.

Among non-COVID-19 respiratory patients admitted to ICU, higher BMI (per SD) was associated with lower mortality across admission dates, e.g. HR = 0.81 (95% CI = 0.75, 0.87) in February–April 2018, but with varying strength of association (BMI-date interaction P = 0.001, indicating stronger evidence of heterogeneity in association magnitude than for COVID-19). Mortality was highest among underweight patients across all periods, e.g. HR = 1.60 (95% CI = 1.26, 2.04) during February–April 2018. In the same period, mortality was lower with overweight and each obesity group vs. recommended weight, e.g. HR = 0.60 (95% CI = 0.43, 0.85) for class 3 + obesity. This lower mortality with class 3 + obesity was seen in most periods except for May–July 2018 and November 2018–January 2019 when mortality did not differ from recommended weight. The cubic spline plots of continuous BMI reiterate both the variability and the imprecision of the BMI-mortality association among non-COVID-19 patients with BMI above 40 kg/m 2 .

Geographical cross-context comparison: associations of BMI with mortality among ICU patients with COVID-19 and non-COVID-19 respiratory conditions, by region of ICU admission

Among COVID-19 patients admitted to ICU, the association of BMI (per SD higher) with higher mortality did not vary across regions (BMI-region interaction P = 0.703; Table 4 ). Compared with recommended weight, mortality was consistently elevated with class 3 + obesity across regions, with HR varying between 1.14 (95% CI = 0.94, 1.38) in southern England and an imprecise 1.29 (95% CI = 0.74, 2.25) in Northern Ireland. Neither overweight nor class 1 or 2 obesity were clearly associated with mortality in any region though there was a strong but imprecisely estimated association with class 2 obesity in Northern Ireland (HR = 1.49, 95% CI = 0.90, 2.46). The cubic spline plots (Additional file 26 : Fig. S15) also do not show clear differences between regions in the association between BMI and mortality.

Among non-COVID-19 patients admitted to ICU, the association of BMI (per SD higher) with lower mortality did not vary across regions (BMI-region interaction P = 0.385; Table 4 ). Compared with recommended weight, underweight was associated with higher mortality in all regions except Wales (HR = 0.90, 95% CI = 0.53, 1.52). The magnitude of these positive associations varied between East England and Midlands (HR = 1.32, 95% CI = 1.05, 1.65) and London (HR = 1.89, 95% CI = 1.50, 2.38) or the less precise Northern Ireland (HR = 1.92, 95% CI = 0.95, 3.89). In most regions, mortality continued to decline across higher BMI groups above recommended weight, with class 3 + obesity often associated with the lowest risk, e.g. at HR = 0.60 (95% CI = 0.46, 0.78) in East England and Midlands and HR = 0.48 (95% CI = 0.27, 0.87) in Wales. The cubic spline plots (Additional file 26 : Fig. S15) also suggest regional variation in the degree of elevated mortality at low BMI and show no evidence (albeit with low precision) of elevated mortality at very high BMI, relative to mean BMI.

The numbers of deaths and total sample sizes for each BMI category, admission period, and admission region contributing to the main survival models described above are shown in Additional files 27 – 29 : Tables S12–S14.

We aimed in this study to assess the likely causality of adiposity for mortality among patients severely ill with COVID-19 and non-COVID-19 respiratory conditions using a cross-context comparison approach with nationally representative ICU data in the UK. Consistent adiposity-mortality associations despite varying confounding/selection would increase confidence in causality. Our results suggest that higher adiposity, primarily extreme obesity, is associated with higher mortality among patients admitted to ICU with COVID-19, but lower mortality among patients admitted with non-COVID-19 respiratory conditions both before and during the COVID-19 pandemic. These associations appear vulnerable to confounding/selection bias in both patient groups, questioning the existence or stability of causal effects. Among COVID-19 patients, unfavourable obesity-mortality associations differ substantially by ICU admission date, perhaps reflecting high levels of temporal heterogeneity in potential confounding and selection bias. Among non-COVID-19 respiratory patients, obesity-mortality associations were consistently favourable but varied in magnitude, despite apparently stable circumstances of the measured potential bias. The strong associations of comorbidities with both BMI and mortality (whether stable or unstable) suggest that comorbidity-induced weight loss may bias BMI-mortality associations in both conditions, but particularly among non-COVID-19 patients due to their higher prevalence of comorbidities.

The two contexts examined here were the date and geographical region of ICU admission. These were chosen because the prevalence/level of confounding and selection factors relevant to adiposity-mortality effects differ by time and geography among ICU patients with COVID-19 [ 25 ], and thus the effects of those confounders/selectors on adiposity/mortality might also differ among them, although this had not been previously examined. Such context-varying bias is expected to be most influential for COVID-19 given rapid changes in its viral biology and clinical/public management, whereas context-stable bias is expected to be most influential for non-COVID-19 respiratory conditions which was assessed here by comparing characteristics between patient groups. The ability of COVID-19 to cause severe disease in otherwise healthy people, especially prior to vaccination, at a time of restricted social contact also suggests that COVID-19 patients in ICU include a potentially varying proportion of occupationally exposed patients, with a distinct covariate profile. Our results suggest that the associations of confounding/selection factors with BMI and mortality varied mostly by date (not region) of ICU admission, and particularly for COVID-19. For example, among COVID-19 patients, the Black ethnic group was associated with slightly different degrees of mostly lower BMI across admission dates, and with higher mortality only during February–April 2020. This diminishing association with mortality over time has also been seen in the UK general population outside of ICU settings [ 32 ]. Deprivation showed little variation in its association with BMI or mortality by date but several comorbidity indicators (e.g. liver disease and APACHE score) had varying associations with BMI and/or mortality.

Notably, higher BMI was not consistently associated with higher mortality across admission dates among COVID-19 patients, with ~ 50% higher mortality for extreme obesity relative to recommended weight seen only within the earliest period of February–April 2020. The negative obesity-mortality associations among non-COVID-19 patients were a little more consistent over time in direction and perhaps magnitude but still displayed heterogeneity well beyond that expected by chance. This consistency among non-COVID-19 patients does not necessarily support causality in that group, however, as this could still reflect stable forms of bias such as reverse causation. This was supported by the high proportion (compared to COVID-19 patients) of non-COVID-19 patients admitted to ICU who were underweight and/or had comorbidities. Such patients might be expected to experience both higher mortality and comorbidity-induced weight loss/cachexia.

Adiposity-mortality associations may differ over time because of changing bias or because of genuine changes in causality, e.g. due to changes in viral variants, immunological naivety, vaccines, and therapeutics. Obesity may have increased COVID-19 mortality mostly during the early pandemic stage (February–April 2020) because this was when all individuals were immunologically naïve to SARS-CoV-2, and excess adiposity may have weakened immune system responses to this new virus; whereas later admission periods will have included more patients who have experienced repeat infections [ 33 , 34 , 35 ]. COVID-19 vaccines were introduced in the UK in December 2020 and uptake was earliest among the oldest and most clinically vulnerable (including both extreme obesity and underweight)—the same populations who present most often to ICU. Vaccines substantially reduce mortality from SARS-CoV-2 infection and COVID-19 [ 36 ], and along with improved therapeutics likely explain overall declines in mortality, acute severity, and mean age of patients admitted to ICU with COVID-19 post-2020. Notably, however, our results suggest that adiposity-mortality associations among COVID-19 patients started diminishing in May–July 2020, before vaccines were introduced in December 2020. This suggests that vaccines/therapeutics are not the sole reason for variation over time in adiposity-mortality associations among COVID-19 patients and that, in addition to changes in natural immunological naivety of the population, confounding/selection bias, which also varied over time, played a role. For example, collider (selection) bias [ 22 , 29 , 37 ] could have manifested in COVID-19 patients being admitted to ICU at higher BMIs yet with fewer comorbidities over the course of the pandemic, following early evidence on obesity-mortality associations [ 4 , 20 ]. Our descriptions of patient characteristics over time do suggest increased mean/median BMI of patients admitted to ICU with COVID-19 from July 2020, while the prevalence of past severe illness declined/fluctuated over the same period. These negative BMI-comorbidity associations among COVID-19 patients selected into ICU could have biased obesity-mortality associations in February–April 2020 when obesity was associated with higher mortality, and/or in later time periods where obesity appears unrelated to mortality.

Our results and the results of earlier descriptive reports by ICNARC [ 25 ] indicate that obesity, including extreme obesity, is more common within COVID-19 than non-COVID-19 respiratory conditions. It is well known that severe COVID-19 occurs more frequently in patients with more comorbidities in the general population [ 38 ], but our results suggest that, within ICU, patients with COVID-19 tend to have fewer comorbidities than patients with non-COVID-19 respiratory conditions. This is despite obesity being more common among COVID-19 patients. This same pattern was also seen in the Netherlands, based on one study using ICU data which compared the adiposity and comorbidity profile of ~ 2600 ICU patients with COVID-19 vs. ~ 2900 with non-COVID-19 viral pneumonia [ 39 ]. That Dutch study reported the same contrasting pattern of BMI-mortality associations between patient groups: positive among COVID-19 patients and negative among non-COVID-19 respiratory patients. MR studies of adiposity and COVID-19 mortality do not exist for comparison, including for clinically selected patient samples, except for one MR study which examined ‘critical respiratory illness’ as a composite outcome of death, intubation, or advanced oxygen support, which supported a detrimental effect of BMI [ 5 ]. This obesity paradox in ICU is striking and may help reveal the potential for patient selection and reverse causation to bias BMI-mortality associations within severe respiratory disease more broadly—with COVID-19 vs. non-COVID-19 offering another type of cross-context comparison to assess context-stable bias. Pre-existing disease may reduce BMI and raise mortality, and this may explain long-standing observations of higher mortality with underweight and lower mortality with obesity (vs. recommended weight) among respiratory disease patients [ 12 , 13 ]. Indeed, MR estimates, which should be less prone to confounding by pre-existing disease, suggest that higher adiposity raises pneumonia mortality [ 14 ], although MR analyses are lacking for hospitalised patients specifically given the lack of genetic data at scale. Given the relatively healthy comorbidity profile of COVID-19 patients, associations between BMI and mortality among them may be less subject to reverse causation and thus inform on the likely causality of BMI for mortality among both COVID-19 and non-COVID-19 patients (if these conditions are clinically similar). This is important given the need for appropriate clinical messaging around obesity during potential dual burdens of COVID-19 and influenza in future. Our results suggest unfavourable obesity-mortality associations among COVID-19 patients (who have fewer comorbidities), in contrast to favourable obesity-mortality associations among non-COVID-19 patients (who have more comorbidities). If comorbidity-induced weight loss is expected to bias associations, then these results suggest that obesity may not be protective in either group, and that weight loss/maintenance advice applies to both groups.

Over a dozen previous studies examined associations of BMI with mortality within severe COVID-19; all used conventional observational designs and most were small scale ( N < 500), with larger studies suggesting that excess mortality is driven by extreme obesity, where this was examined [ 40 , 41 , 42 , 43 , 44 , 45 ]. Given the global spread of COVID-19, the totality of studies examining adiposity-mortality associations naturally provides a comparison of these associations across temporal and geographical contexts, but the study designs and methods used differ in many other respects and no previous study directly compared dates or regions using the same analytical strategy. One UK study examined how associations between ethnicity and mortality among COVID-19 patients changed over calendar time, but did not examine BMI [ 32 ]. Importantly, none of these past studies directly compared the associations of confounding/selection factors with adiposity and mortality across contexts to appraise their impact. Previous studies also tended to statistically adjust for comorbidities in main effect models, which may be an overadjustment and could induce collider bias given that comorbidities can result from adiposity. With cross-context comparisons, however, it is difficult to identify specific confounding or selection factors which underpin any differences in exposure-outcome associations; multiple factors are likely influential, many of which are likely unmeasured and are only proxied by factors which are stratified on. Interpreting results necessarily relies on critical judgement and assessing causality is inherently qualitative.

Limitations

This study is observational and associations are subject to confounding, selection bias, reverse causation, and measurement error. Cross-context comparisons are intended to interrogate the extent and impact of such biases on exposure-outcome associations but offer incomplete and qualitative assessments. Measurement error may be problematic for adiposity given that this was measured indirectly using BMI which correlates less well with more objective measures of fat mass in severely ill vs. young healthy populations, although the correlation between BMI and abdominal fat area is ~ 0.7 among severely ill adults [ 46 , 47 ]. The relationship between BMI and percentage body fat may also differ between ethnicity groups, although the extent of this is variable [ 48 , 49 ]. Data are also collected within ‘real world’ ICU settings and are often recorded less accurately than in research-grade clinics. BMI measures here were a mixture of directly measured and visually estimated values upon ICU admission, and these proportions varied more over time for COVID-19 vs. non-COVID-19 patients (reflecting changing staff workloads/resources during virus waves). The extent of estimated values was similar between patient groups, however, and these different methods of BMI recording have previously shown consistent associations with ICU mortality [ 27 ]. Furthermore, the exclusion of estimated BMI values in a sensitivity analysis did not change the overall BMI-mortality associations beyond their confidence limits. The estimation of BMI appeared to be most common when COVID-19 admissions were highest, which probably reflects staff workloads at the time. Staff workload and ICU capacity (including seasonal variation in non-COVID-19 admissions) might cause temporal variation in the potential for bias in BMI-mortality associations if they affect admission criteria and/or clinical practice.

Our study is limited to data which are routinely collected in ICU settings nationally, and thus data on other adiposity measures such as waist circumference and lifestyle factors such as smoking, diet, and physical activity were not available. Smoking history data would be particularly useful for assessing confounding, e.g. where excess mortality with underweight in non-COVID-19 may have been partly confounded by the effects of smoking on weight loss and mortality. Comorbid disease indicators were also limited to very severe forms of disease and excluded less severe diseases which are still relevant to mortality, such as type 2 diabetes and other cardiovascular diseases. One Dutch ICU study did, however, record diabetes history in patients with COVID-19 and non-COVID-19 pneumonia and found no difference between groups (~ 20% in each) [ 39 ].

Proportional hazards tests found that both the positive BMI-mortality associations among COVID-19 patients and the negative ones among non-COVID-19 patients attenuated towards the null with increasing time in ICU. This could be because mortality disproportionately removes frailer patients, causing a decline in the hazard over time, but more so at those levels of BMI where mortality is highest [ 50 ]. A marginal hazard ratio under non-proportional hazards can be interpreted as an average effect over the support of the data, but its value can be sensitive to the censoring distribution [ 30 , 31 ]. Had we chosen to administratively censor at more than 30 days, it is therefore likely that HR would have been a little closer to the null, particularly for COVID-19 patients. Because of our interest in heterogeneity between regions or dates of admission, heterogeneity in the censoring distribution is of concern to us. This was only apparent in the final period for COVID-19 patients (May to August 2021), when there was less administrative censoring (i.e. at 30 days) and much more earlier censoring (people who were still in ICU when data collection ended). It is therefore reassuring that results for this period were not particularly different from the others (with the possible exception of the very high HR for underweight people, which should be interpreted with caution). Rather, it is the first period for COVID-19 patients which was distinctive from the others.

Lastly, the ICU data used here for COVID-19 patients were representative of adult ICU patients in England, Wales, and Northern Ireland, but likely excluded individuals who were most extremely clinically vulnerable as they would have been shielding during peak stages of the pandemic and thus presenting less than usual to ICU, whereas equivalently extremely vulnerable non-COVID-19 respiratory patients in 2018–2019 would have presented more readily. Hospital practices were also atypical during COVID-19 surges and many patients severely ill with COVID-19 were likely managed outside of ICU on regular wards due to limited capacity. Such practices would have likely varied more by time than by geography given that national clinical guidance and protocols were rapidly shared across regions of the UK via the NHS during the pandemic. However, BMI-mortality associations among non-COVID-19 patients during the pandemic resembled closely those from 2018 to 2019 and were similarly different from contemporary BMI-mortality associations among COVID-19 patients, suggesting that patient selection affects BMI-mortality associations much less than the reason for admission does. We did not account for potential pseudoreplication due to the same individuals having multiple non-COVID-19 admissions (or both COVID-19 and non-COVID-19 admissions) but this would have been rare in the short study period.

Our results based on a cross-context comparison approach with nationally representative ICU data in the UK suggest that higher adiposity, primarily extreme obesity, is associated with higher mortality among patients admitted to ICU with COVID-19, but lower mortality among patients admitted with non-COVID-19 respiratory conditions. If these associations among COVID-19 patients had remained consistent despite the observed temporal heterogeneity in potential confounding/selection bias, it would have increased our willingness to interpret them as causal. However, BMI-mortality associations among COVID-19 patients differed by admission date, questioning the existence or stability of causal effects. Among non-COVID-19 respiratory patients, there was less temporal or regional heterogeneity in potential bias, diminishing the power of this approach to test causation. However, the relatively stable and strong associations of comorbidities with both BMI and mortality in this patient group, coupled with their high prevalence of comorbidity, suggest that favourable obesity-mortality associations among non-COVID-19 respiratory patients may reflect comorbidity-induced weight loss.

Availability of data and materials

Individual-level data are available via application to ICNARC (managed access).

Abbreviations

Acute physiology and chronic health evaluation-II

Body mass index

Confidence interval

Coronavirus disease 2019

Fractional inspired oxygen

Hazard ratio

Intensive Care National Audit and Research Centre

Intensive care unit

Index of Multiple Deprivation

Mendelian randomisation

National Health Service

Arterial oxygen partial pressure

Severe acute respiratory syndrome coronavirus 2

Standard deviation

United Kingdom

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Acknowledgements

We thank and respect all those working in critical care units across England, Wales, and Northern Ireland and contributing to the care of patients and, particularly, those responsible for submitting data rapidly and regularly during the COVID-19 epidemic. Additional Intensive Care National Audit & Research Centre Coronavirus Disease 2019 Team Members: Yemi Banjo, Kasia Borowczak, Tom Cousins, Peter Cummins, Keji Dalemo, Robert Darnell, Hanna Demissie, Laura Drikite, Andrew Fleming, Ditte Frederiksen, Sarah Furnell, Abdo Hussein, Abby Koelewyn, Tim Matthews, Izabella Orzechowska, Sam Peters, Alvin Richards-Belle, Tyrone Samuels, and Michelle Saull.

JAB, DC, and AH are supported by the Elizabeth Blackwell Institute for Health Research and the Development and Alumni Relations Office, University of Bristol. JAB, DC, AH, KT, and GDS work in a unit funded by the UK MRC (MC_UU_00011/1; MC_UU_00011/3) and the University of Bristol. This publication is the work of the authors and JAB is the guarantor for its contents. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Joshua A. Bell and David Carslake contributed equally to this work.

David A. Harrison, Kathryn M. Rowan and George Davey Smith contributed equally to this work.

Authors and Affiliations

MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK

Joshua A. Bell, David Carslake, Amanda Hughes, Kate Tilling, James W. Dodd & George Davey Smith

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

Joshua A. Bell, David Carslake, Amanda Hughes, Kate Tilling & George Davey Smith

Academic Respiratory Unit, Southmead Hospital, University of Bristol, Bristol, UK

James W. Dodd

Intensive Care National Audit & Research Centre (ICNARC), London, UK

James C. Doidge, David A. Harrison & Kathryn M. Rowan

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Contributions

JAB, DC, AH, and GDS planned the study. JAB and DC conducted analyses presented here and JAB wrote the first draft. JCD, DAH, and KMR contributed to the curation of ICNARC data. JAB, DC, AH, KT, JWD, JCD, DAH, KMR, and GDS critically reviewed the intellectual content of manuscript drafts and read and approved the final manuscript. JAB (corresponding author) attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Corresponding author

Correspondence to Joshua A. Bell .

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Approval for the collection and use of patient-identifiable data without consent in the Case Mix Programme was obtained from the Confidentiality Advisory Group of the Health Research Authority under Sect. 251 of the NHS Act 2006 (approval number PIAG2–10[f]/2005). All data were pseudonymised (patient identifiers removed) prior to extraction for this research.

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Supplementary Information

12916_2024_3598_moesm1_esm.docx.

Additional file 1: Figure S1 Age and sex profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by admission date.

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Additional file 2: Figure S2 Ethnic group, deprivation and adiposity profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by admission date.

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Additional file 3: Figure S3 Age profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 4: Figure S4 Sex and ethnic group profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 5: Figure S5 Deprivation and adiposity profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 6: Figure S6 Adiposity, dependency and comorbidity profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by admission date.

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Additional file 7: Figure S7 Adiposity and prior dependency profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 8: Figure S8 Respiratory support, BMI reporting and mortality profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by date of admission.

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Additional file 9: Figure S9 Respiratory support and BMI reporting profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 10: Figure S10 Comorbidity and acute severity profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) or non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 11: Figure S11 Acute severity, physiological severity and respiratory severity profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by admission date.

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Additional file 12: Figure S12 Physiological and respiratory severity profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) or non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 13: Figure S13 Mortality profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 14: Figure S14 Association between BMI and mortality in COVID-19 and non-COVID-19 respiratory patients, by admission date.

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Additional file 15: Figure S15 Association between BMI and mortality in COVID-19 and non-COVID-19 respiratory patients, by geographical region.

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Additional file 16: Table S1 Associations of confounding and selection factors with BMI and 30-day all-cause mortality among ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019).

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Additional file 17: Table S2 Associations of confounding/selection factors with BMI among ICU patients with COVID-19, by admission date

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Additional file 18: Table S3 Associations of confounding/selection factors with BMI among ICU patients with COVID-19, by admission region

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Additional file 19: Table S4 Associations of confounding/selection factors with BMI among ICU patients with non-COVID-19 respiratory conditions, by admission date

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Additional file 20: Table S5 Associations of confounding/selection factors with BMI among ICU patients with non-COVID-19 respiratory conditions, by admission region

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Additional file 21: Table S6 Associations of confounding/selection factors with all-cause mortality among ICU patients with COVID-19, by admission date

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Additional file 22: Table S7 Associations of confounding/selection factors with all-cause mortality among ICU patients with COVID-19, by admission region

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Additional file 23: Table S8 Associations of confounding/selection factors with all-cause mortality among ICU patients with non-COVID-19 respiratory conditions, by admission date

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Additional file 24: Table S9 Associations of confounding/selection factors with all-cause mortality among ICU patients with non-COVID-19 respiratory conditions, by admission region

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Additional file 25: Supplementary Table S10 Main analyses of all-cause mortality and BMI, restricted to ICU patients whose BMI was measured, not estimated.

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Additional file 26: Supplementary Table S11 Main analyses of all-cause mortality and BMI, restricted to ICU patients who were not physically dependent on others for the activities of daily living prior to admission.

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Additional file 27: Table S12 Number of deaths and total sample size for each BMI category in Table 2 .

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Additional file 28: Table S13 Number of deaths and total sample size for each BMI category and admission period in Table 3 .

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Additional file 29: Table S14 Number of deaths and total sample size for each BMI category and admission region in Table 4 .

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Bell, J.A., Carslake, D., Hughes, A. et al. Adiposity and mortality among intensive care patients with COVID-19 and non-COVID-19 respiratory conditions: a cross-context comparison study in the UK. BMC Med 22 , 391 (2024). https://doi.org/10.1186/s12916-024-03598-3

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DOI : https://doi.org/10.1186/s12916-024-03598-3

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Features, evaluation, and treatment of coronavirus (covid-19).

Marco Cascella ; Michael Rajnik ; Abdul Aleem ; Scott C. Dulebohn ; Raffaela Di Napoli .

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Continuing Education Activity

Coronavirus disease 2019 (COVID-19) is a highly contagious infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 has had a catastrophic effect on the world, resulting in more than 6 million deaths worldwide. It has emerged as the most consequential global health crisis since the era of the influenza pandemic of 1918. As the virus mutates, treatment guidelines are altered to reflect the most efficacious therapies. This activity is a comprehensive review of the disease presentation, complications, and current guideline-recommended treatment options for managing this disease.

Screen individuals based on exposure and symptom criteria to identify potential COVID-19 cases.
Identify the clinical features and radiological findings expected in patients with COVID-19.
Apply the recommended treatment options for patients with COVID-19.
Create strategies with the interprofessional team for improving care coordination to care for patients with COVID-19 to help improve clinical outcomes.
Introduction

Coronavirus disease 2019 (COVID-19) is a highly contagious viral illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 has had a catastrophic effect on the world, resulting in more than 6 million deaths worldwide. After the first cases of this predominantly respiratory viral illness were reported in Wuhan, Hubei Province, China, in late December 2019, SARS-CoV-2 rapidly disseminated worldwide. This compelled the World Health Organization (WHO) to declare it a global pandemic on March 11, 2020. [1]

Even though substantial progress in clinical research has led to a better understanding of SARS-CoV-2, many countries continue to have outbreaks of this viral illness. These outbreaks are primarily attributed to the emergence of mutant variants of the virus. Like other RNA viruses, SARS-CoV-2 adapts with genetic evolution and developing mutations. This results in mutant variants that may have different characteristics than their ancestral strains. Several variants of SARS-CoV-2 have been described during the course of this pandemic, among which only a few are considered variants of concern (VOCs). Based on the epidemiological update by the WHO, 5 SARS-CoV-2 VOCs have been identified since the beginning of the pandemic:

Alpha (B.1.1.7): First variant of concern, which was described in the United Kingdom (UK) in late December 2020 [2]
Beta (B.1.351) : First reported in South Africa in December 2020 [2]
Gamma (P.1) : First reported in Brazil in early January 2021 [2]
Delta (B.1.617.2): First reported in India in December 2020 [2]
Omicron (B.1.1.529): First reported in South Africa in November 2021 [3]

Despite the unprecedented speed of vaccine development against the prevention of COVID-19 and robust global mass vaccination efforts, the emergence of new SARS-CoV-2 variants threatens to overturn the progress made in limiting the spread of this disease. This review aims to comprehensively describe the etiology, epidemiology, pathophysiology, and clinical features of COVID-19. This review also provides an overview of the different variants of SARS-CoV-2 and the guideline-recommended treatment (as of January 2023) for managing this disease.

Coronaviruses (CoVs) are positive-sense single-stranded RNA (+ssRNA) viruses with a crown-like appearance under an electron microscope ( coronam is the Latin term for crown) due to the presence of spike glycoproteins on the envelope. [1] The subfamily Orthocoronavirinae of the Coronaviridae family (order Nidovirales ) classifies into 4 genera of CoVs:

Alphacoronavirus (alphaCoV)
Betacoronavirus (betaCoV)
Deltacoronavirus (deltaCoV)
Gammacoronavirus (gammaCoV)

BetaCoV genus is further divided into 5 sub-genera or lineages. [4] Genomic characterization has shown that bats and rodents are the probable gene sources of alphaCoVs and betaCoVs. Avian species seem to be the source of deltaCoVs and gammaCoVs. CoVs have become significant pathogens of emerging respiratory disease outbreaks. Members of this large family of viruses can cause respiratory, enteric, hepatic, and neurological diseases in different animal species, including camels, cattle, cats, and bats.

These viruses can cross species barriers and infect humans as well. Seven human CoVs (HCoVs) capable of infecting humans have been identified. Some HCoVs were identified in the mid-1960s, while others were only detected in the new millennium. In general, estimates suggest that 2% of the population are healthy carriers of CoVs and that these viruses are responsible for about 5% to 10% of acute respiratory infections. [5]

Common human CoVs : HCoV-OC43 and HCoV-HKU1 (betaCoVs of the A lineage), HCoV-229E, and HCoV-NL63 (alphaCoVs). These viruses can cause common colds and self-limiting upper respiratory tract infections in immunocompetent individuals. However, in immunocompromised and older patients, lower respiratory tract infections can occur due to these viruses.
Other human CoVs : SARS-CoV and MERS-CoV (betaCoVs of the B and C lineage, respectively). These viruses are considered more virulent and capable of causing epidemics with respiratory and extra-respiratory manifestations of variable clinical severity. [1]

SARS-CoV-2 is a novel betaCoV belonging to the same subgenus as the severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV), which have been previously implicated in SARS-CoV and MERS-CoV epidemics with mortality rates up to 10% and 35%, respectively. [6] It has a round or elliptic and often pleomorphic form and a diameter of approximately 60 to 140 nm. Like other CoVs, it is sensitive to ultraviolet rays and heat. [6]

The inactivation temperature of SARS-CoV-2 is being researched. A stainless steel surface held at an air temperature of 54.5°C (130 °F) results in the inactivation of 90% of SARS-CoV-2 in approximately 36 minutes. [7] It resists lower temperatures, even those below 0°C. However, lipid solvents can effectively inactivate these viruses, including ether (75%), ethanol, chlorine-containing disinfectant, peroxyacetic acid, and chloroform (except for chlorhexidine).

Although the origin of SARS-CoV-2 is currently unknown, it is widely postulated to have a zoonotic transmission. [1] Genomic analyses suggest that SARS-CoV-2 probably evolved from a strain found in bats. The genomic comparison between the human SARS-CoV-2 sequence and known animal coronaviruses revealed high homology (96%) between the SARS-CoV-2 and the betaCoV RaTG13 of bats ( Rhinolophus affinis ). [8] Similar to SARS and MERS, it has been hypothesized that SARS-CoV-2 advanced from bats to intermediate hosts, such as pangolins and minks, and then to humans. [9] [10]

SARS-CoV-2 Variants

A globally dominant D614G variant was eventually identified and associated with increased transmissibility but without the ability to cause severe illness. [11] Another variant was attributed to transmission from infected farmed mink in Denmark but was not associated with increased transmissibility. [10] Since then, multiple variants of SARS-CoV-2 have been described, of which a few are considered variants of concern (VOCs) due to their potential to cause enhanced transmissibility or virulence. The United States Centers for Disease Control and Prevention (CDC) and the WHO have independently established a classification system for distinguishing the emerging variants of SARS-CoV-2 into variants of concern(VOCs) and variants of interest(VOIs).

SARS-CoV-2 Variants of Concern (VOCs)

Alpha (B.1.1.7 lineage)
In late December 2020, the Alpha variant, or GRY (formerly GR/501Y.V1), was reported in the UK based on whole-genome sequencing of samples from patients who tested positive for SARS-CoV-2. [12] [13]
The variant was also identified using a commercial assay characterized by the absence of the S gene (S-gene target failure, SGTF) in PCR samples. The B.1.1.7 variant includes 17 mutations in the viral genome. Of these, 8 mutations (Δ69-70 deletion, Δ144 deletion, N501Y, A570D, P681H, T716I, S982A, D1118H) are in the spike (S) protein. N501Y shows an increased affinity of the spike protein to ACE 2 receptors, enhancing the viral attachment and subsequent entry into host cells. [14] [15] [16]
This alpha variant was reportedly 43% to 82% more transmissible, surpassing preexisting variants of SARS-CoV-2 to emerge as the dominant SARS-CoV-2 variant in the UK. [15]
An initial matched case-control study reported no significant difference in the risk of hospitalization or associated mortality with the B.1.1.7 lineage variant compared to other existing variants. However, subsequent studies have reported that people infected with B.1.1.7 lineage variant had increased disease severity compared to those infected with other circulating variants. [17] [13]
A large matched cohort study in the UK reported that the mortality hazard ratio of patients infected with the B.1.1.7 lineage variant was 1.64 (95% confidence interval 1.32 to 2.04, P<0.0001) compared to patients with previously circulating strains. [18]
Another study reported that the B 1.1.7 variant was associated with increased mortality compared to other SARS-CoV-2 variants (HR= 1.61, 95% CI 1.42-1.82). [19] The risk of death was reportedly greater (adjusted hazard ratio 1.67, 95% CI 1.34-2.09) among individuals with confirmed B.1.1.7 infection compared to individuals with non-B.1.1.7 SARS-CoV-2. [20]
Beta (B.1.351 lineage)
The Beta variant, or GH501Y.V2 with multiple spike mutations, resulted in the second wave of COVID-19 infections and was first detected in South Africa in October 2020. [21]
The B.1.351 variant includes 9 mutations (L18F, D80A, D215G, R246I, K417N, E484K, N501Y, D614G, and A701V) in the spike protein, of which 3 mutations (K417N, E484K, and N501Y) are located in the receptor binding domain (RBD) and increase its binding affinity for the ACE receptors. [22] [14] [23]
SARS-CoV-2 501Y.V2 (B.1.351 lineage) was reported in the US at the end of January 2021.
This variant had an increased risk of transmission and reduced neutralization by monoclonal antibody therapy, convalescent sera, and post-vaccination sera. [24]
Gamma (P.1 lineage)
The Gamma variant, or GR/501Y.V3 , was identified in December 2020 in Brazil and was first detected in the US in January 2021. [25]
This B.1.1.28 variant harbors ten mutations in the spike protein (L18F, T20N, P26S, D138Y, R190S, H655Y, T1027I V1176, K417T, E484K, and N501Y). Three mutations (L18F, K417N, E484K) are located in the RBD, similar to the B.1.351 variant. [25]
The Delta variant was initially identified in December 2020 in India and was responsible for the deadly second wave of COVID-19 infections in April 2021 in India. In the United States, this variant was first detected in March 2021. [2]
The B.1.617.2 variant harbors ten mutations ( T19R, (G142D*), 156del, 157del, R158G, L452R, T478K, D614G, P681R, D950N) in the spike protein.
The Omicron variant was first identified in South Africa on 23 November 2021 after an uptick in the number of cases of COVID-19. [26]
Omicron was quickly recognized as a VOC due to more than 30 changes to the spike protein of the virus and the sharp rise in the number of cases observed in South Africa. [27] The reported mutations include T91 in the envelope, P13L, E31del, R32del, S33del, R203K, G204R in the nucleocapsid protein, D3G, Q19E, A63T in the matrix, N211del/L212I, Y145del, Y144del, Y143del, G142D, T95I, V70del, H69del, A67V in the N-terminal domain of the spike, Y505H, N501Y, Q498R, G496S, Q493R, E484A, T478K, S477N, G446S, N440K, K417N, S375F, S373P, S371L, G339D in the receptor-binding domain of the spike, D796Y in the fusion peptide of the spike, L981F, N969K, Q954H in the heptad repeat 1 of the spike as well as multiple other mutations in the non-structural proteins and spike protein. [28]
Many subvariants of Omicron, such as BA.1, BA.2, BA.3, BA.4, and BA.5, have been identified. [3]

Transmission of SARS-CoV-2

The primary mode of transmission of SARS-CoV-2 is via exposure to respiratory droplets carrying the infectious virus from close contact or direct transmission from presymptomatic, asymptomatic, or symptomatic individuals harboring the virus. [1]
Airborne transmission with aerosol-generating procedures has also been implicated in the spread of COVID-19. Data implicating airborne transmission of SARS-CoV-2 in the absence of aerosol-generating procedures is present; however, this mode of transmission has not been universally acknowledged.
Fomite transmission from contamination of inanimate surfaces with SARS-CoV-2 has been well characterized based on many studies reporting the viability of SARS-CoV-2 on various porous and nonporous surfaces. Under experimental conditions, SARS-CoV-2 was stable on stainless steel and plastic surfaces compared to copper and cardboard surfaces, with the viable virus being detected up to 72 hours after inoculating the surfaces with the virus. [29] The viable virus was isolated for up to 28 days at 20°C from nonporous surfaces such as glass and stainless steel. Conversely, recovery of SARS-CoV-2 on porous materials was reduced compared with nonporous surfaces. [30] In hospital settings, the SARS-CoV-2 has been detected on floors, computer mice, trash cans, sickbed handrails, and in the air (up to 4 meters from patients). [31] The Centers for Disease Control and Prevention (CDC) has stated that individuals can be infected with SARS-CoV-2 via contact with surfaces contaminated by the virus, but the risk is low and is not the main route of transmission of this virus.
Epidemiologic data from several case studies have reported that patients with SARS-CoV-2 infection have the live virus in feces implying possible fecal-oral transmission. [32]
A meta-analysis that included 936 neonates from mothers with COVID-19 showed vertical transmission is possible but occurs in a minority of cases. [33]
Epidemiology

COVID-19 was the third leading cause of death in the United States (USA) in 2020 after heart disease and cancer, with approximately 375,000 deaths. [34]

Individuals of all ages are at risk of contracting this infection. However, patients aged ≥60 and patients with underlying medical comorbidities (obesity, cardiovascular disease, chronic kidney disease, diabetes, chronic lung disease, smoking, cancer, solid organ or hematopoietic stem cell transplant patients) have an increased risk of developing severe COVID-19 infection.

According to the CDC, age remains the strongest predictor of poor outcomes and severe illness in patients with COVID-19. Data from the National Vital Statistics System (NVSS) at CDC states that patients with COVID-19 aged 50 to 64 years have a 25 times higher risk of death when compared to adults infected with this illness and aged less than 30 years. In patients 65 to 74 years old, this risk increases to 60 times. In patients older than 85, the risk of death increases to 340 times. According to the CDC, these data include all deaths in the United States throughout the pandemic, from February 2020 to July 1, 2022, including deaths among unvaccinated individuals.

The percentage of COVID-19 patients requiring hospitalization was 6 times higher in those with preexisting medical conditions than those without medical conditions (45.4% vs. 7.6%) based on an analysis by Stokes et al. of confirmed cases reported to the CDC from January 22 to May 30, 2020. [35] The study also reported that the percentage of patients who succumbed to this illness was 12 times higher in those with preexisting medical conditions than those without (19.5% vs 1.6%). [35]

Data regarding the gender-based differences in COVID-19 suggests that male patients have a higher risk of severe illness and increased mortality due to COVID-19 compared to female patients. [36] [37] Results from a retrospective cohort study from March 1 to November 21, 2020, evaluating the mortality rate in 209 United States of America (USA) acute care hospitals that included 42604 patients with confirmed SARS-CoV-2 infection, reported a higher mortality rate in male patients (12.5%) compared to female patients (9.6%). [38]

Racial and ethnic minority groups have been reported to have a higher percentage of COVID-19-related hospitalizations than White patients based on a recent CDC analysis of hospitalizations from an extensive administrative database that included approximately 300,000 COVID-19 patients hospitalized from March 2020 to December 2020. This high percentage of COVID-19-related hospitalizations among racial and ethnic groups was driven by a higher risk of exposure to SARS-CoV-2 and an increased risk of developing severe COVID-19 disease. [39] A meta-analysis of 50 studies from USA and UK researchers noted that people of Black, Hispanic, and Asian ethnic minority groups are at increased risk of contracting and dying from COVID-19 infection. [40]

COVID-19-related death rates were the highest among Hispanic persons. [34] Another analysis by the CDC evaluating the risk of COVID-19 among sexual minority adults reported that underlying medical comorbidities which increase the risk of developing severe COVID-19 were more prevalent in sexual minority individuals than heterosexual individuals within the general population and within specific racial/ethnic groups. [41]

Pathophysiology

Structurally and phylogenetically, SARS-CoV-2 is similar to SARS-CoV and MERS-CoV and is composed of 4 main structural proteins: spike (S), envelope (E) glycoprotein, nucleocapsid (N), and membrane (M) protein. It also contains 16 nonstructural proteins and 5-8 accessory proteins. [42]

The surface spike (S) glycoprotein, which resembles a crown, is located on the outer surface of the virion. It undergoes cleavage into an amino (N)-terminal S1 subunit, which facilitates the incorporation of the virus into the host cell. The carboxyl (C)-terminal S2 subunit contains a fusion peptide, a transmembrane domain, and a cytoplasmic domain responsible for virus-cell membrane fusion. [43] [44] The S1 subunit is further divided into a receptor-binding domain (RBD) and an N-terminal domain (NTD), which facilitates viral entry into the host cell and serves as a potential target for neutralization in response to antisera or vaccines . [45]

The RBD is a fundamental peptide in the pathogenesis of infection as it represents a binding site for the human angiotensin-converting enzyme 2 (ACE2) receptors. Inhibition of the renin-angiotensin-aldosterone system (RAAS) does not increase the risk of hospitalization for COVID-19 and severe disease. [46]

SARS-CoV-2 gains entry into the host cells by binding the SARS-CoV-2 spike or S protein (S1) to the ACE2 receptors in the respiratory epithelium. ACE2 receptors are also expressed by other organs such as the upper esophagus, enterocytes from the ileum, myocardial cells, proximal tubular cells of the kidney, and urothelial cells of the bladder. [47] The viral attachment process is followed by priming the spike protein S2 subunit by the host transmembrane serine protease 2 (TMPRSS2) that facilitates cell entry and subsequent viral replication. [48]

In the early phase of the infection, viral replication results in direct virus-mediated tissue damage. In the late phase, the infected host cells trigger an immune response by recruiting T lymphocytes, monocytes, and neutrophils. Cytokines such as tumor necrosis factor-α (TNF α), granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-1 (IL-1), interleukin-6 (IL-6), ), IL-1β, IL-8, IL-12 and interferon (IFN)-γ are released. In severe COVID-19 illness, a 'cytokine storm' is seen. This is due to the over-activation of the immune system and high levels of cytokines in circulation. This results in a local and systemic inflammatory response. [49] [50]

Effect of SARS-CoV-2 on the Respiratory System

Increased vascular permeability and subsequent development of pulmonary edema in patients with severe COVID-19 are explained by multiple mechanisms. [51] [52] [53] These mechanisms include:

Endotheliitis as a result of direct viral injury and perivascular inflammation leading to microvascular and microthrombi deposition
Dysregulation of RAAS due to increased binding of the virus to the ACE2 receptors
Activation of the kallikrein-bradykinin pathway, the activation of which enhances vascular permeability
Enhanced epithelial cell contraction causes swelling of cells and disturbance of intercellular junctions
The binding of SARS-CoV-2 to the Toll-Like Receptor (TLR) induces the release of pro-IL-1β, which mediates lung inflammation until fibrosis . [54]

Effect of SARS-CoV-2 on Extrapulmonary Organ Systems

Although the respiratory system is the principal target for SARS-CoV-2, other major organ systems such as the gastrointestinal tract (GI), hepatobiliary, cardiovascular, renal, and central nervous systems may also be affected. SARS-CoV-2–induced organ dysfunction is likely due to a combination of mechanisms, such as direct viral toxicity, ischemic injury caused by vasculitis, thrombosis, immune dysregulation, and renin-angiotensin-aldosterone system (RAAS) dysregulation. [55]

Cardiac involvement in COVID-19 is common and likely multifactorial. ACE2 receptors exhibited by myocardial cells may cause direct cytotoxicity to the myocardium leading to myocarditis. Proinflammatory cytokines such as IL-6 can also lead to vascular inflammation, myocarditis, and cardiac arrhythmias. [56]

Acute coronary syndrome (ACS) is a well-recognized cardiac manifestation of COVID-19. It is likely due to multiple factors, including proinflammatory cytokines, worsening of preexisting severe coronary artery disease, coronary plaque destabilization, microthrombogenesis, and reduced coronary blood flow. [57]

SARS-CoV-2 has a significant effect on the hematological and hemostatic systems as well. The mechanism of leukopenia, one of the most common laboratory abnormalities encountered in COVID-19, is unknown. Several hypotheses have been postulated that include ACE 2 mediated lymphocyte destruction by direct invasion by the virus, lymphocyte apoptosis due to proinflammatory cytokines, and possible invasion of the virus in the lymphatic organs. [58]

Thrombocytopenia is common in COVID-19 and is likely due to multiple factors, including virus-mediated suppression of platelets, autoantibodies formation, and coagulation cascade activation, resulting in platelet consumption. [59]

Thrombocytopenia and neutrophilia are considered a hallmark of severe illness. [55] Although it is well known that COVID-19 is associated with a state of hypercoagulability, the exact mechanisms that lead to the activation of the coagulation system are unknown and likely attributed to the cytokine-induced inflammatory response. The pathogenesis of this associated hypercoagulability is multifactorial. The hypercoagulability is probably induced by direct viral-mediated damage or cytokine-induced injury of the vascular endothelium leading to the activation of platelets, monocytes, and macrophages, with increased expression of von Willebrand factor and Factor VIII that results in the generation of thrombin and formation of a fibrin clot. [59] [60]

Other mechanisms that have been proposed include possible mononuclear phagocyte-induced prothrombotic sequelae, derangements in the renin-angiotensin system (RAS) pathways, and complement-mediated microangiopathy. [59]

History and Physical

Clinical Manifestations of COVID-19

The median incubation period for SARS-CoV-2 is estimated to be 5.1 days, and most patients will develop symptoms within 11.5 days of infection. [61]
The clinical spectrum of COVID-19 varies from asymptomatic or paucisymptomatic forms to clinical illness characterized by acute respiratory failure requiring mechanical ventilation, septic shock, and multiple organ failure.
It is estimated that 17.9% to 33.3% of infected patients will remain asymptomatic. [62] [63]
Most symptomatic patients present with fever, cough, and shortness of breath. Less common symptoms include sore throat, anosmia, dysgeusia, anorexia, nausea, malaise, myalgias, and diarrhea. Stokes et al. reported that among 373,883 confirmed symptomatic COVID-19 cases in the USA, 70% experienced fever, cough, and shortness of breath, 36% reported myalgia, and 34% reported headache. [35]
A large meta-analysis evaluating clinicopathological characteristics of 8697 patients with COVID-19 in China reported laboratory abnormalities that included lymphopenia (47.6%), elevated C-reactive protein levels (65.9%), elevated cardiac enzymes (49.4%), and abnormal liver function tests (26.4%). Other laboratory abnormalities included leukopenia (23.5%), elevated D-dimer (20.4%), elevated erythrocyte sedimentation rate (20.4%), leukocytosis (9.9%), elevated procalcitonin (16.7%), and abnormal renal function (10.9%). [64]
A meta-analysis of 212 published studies with 281,461 individuals from 11 countries/regions reported that severe disease course was noted in about 23% of the patients, with a mortality rate of about 6% in patients infected with COVID-19. [65]
An elevated neutrophil-to-lymphocyte ratio (NLR), an elevated derived NLR ratio (d-NLR), and an elevated platelet-to-lymphocyte ratio indicate a cytokine-induced inflammatory storm. [66]

Based on the severity of the presenting illness, which includes clinical symptoms, laboratory and radiographic abnormalities, hemodynamics, and organ function, the National Institutes of Health (NIH) issued guidelines that classify COVID-19 into 5 distinct types.[ NIH COVID-19 Treatment Guidelines ]

Asymptomatic or Presymptomatic Infection : Individuals with positive SARS-CoV-2 test without any clinical symptoms consistent with COVID-19.
Mild illness : Individuals who have symptoms of COVID-19, such as fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, anosmia, or dysgeusia but without shortness of breath or abnormal chest imaging.
Moderate illness : Individuals with clinical symptoms or radiologic evidence of lower respiratory tract disease and oxygen saturation (SpO 2 ) ≥94% on room air.
Severe illness : Individuals who have SpO 2 less than 94% on room air, a ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO 2 /FiO 2 ) of less than 300, marked tachypnea with a respiratory frequency of greater than 30 breaths/min, or lung infiltrates that are greater than 50% of total lung volume.
Critical illness : Individuals with acute respiratory failure, septic shock, or multiple organ dysfunction. Patients with severe COVID-19 illness may become critically ill with the development of acute respiratory distress syndrome (ARDS). This tends to occur approximately one week after the onset of symptoms.

ARDS is characterized by a severe new-onset respiratory failure or worsening of an already identified respiratory picture. The diagnosis requires bilateral opacities (lung infiltrates >50%), not fully explained by effusions or atelectasis. The Berlin definition classifies ARDS into 3 types based on the degree of hypoxia, with the reference parameter being PaO 2 /FiO 2 or P/F ratio: [67]

Mild ARDS : 200 mm Hg <PaO 2 /FiO 2 ≤300 mm Hg in patients not receiving mechanical ventilation or in those managed through noninvasive ventilation (NIV) by using positive end-expiratory pressure (PEEP) or a continuous positive airway pressure (CPAP) ≥5 cm H2O.
Moderate ARDS : 100 mm Hg <PaO 2 /FiO 2 ≤200 mm Hg
Severe ARDS : PaO 2 /FiO 2 ≤100 mm Hg

When PaO 2 is unavailable, a ratio of SpO 2 /FiO 2 ≤315 suggests ARDS. A multicenter prospective observational study that analyzed 28-day mortality in mechanically ventilated patients with ARDS concluded that COVID-19 patients with ARDS had features similar to other ARDS cohorts, and the risk of 28-day mortality increased with ARDS severity. [68]

Extrapulmonary Manifestations

Acute kidney injury (AKI) is the most frequently encountered extrapulmonary manifestation of COVID-19 and is associated with an increased mortality risk. [69] A large multicenter cohort study of hospitalized patients with COVID-19 that involved 5,449 patients admitted with COVID-19 reported that 1993 (36.6%) patients developed AKI during their hospitalization, of which 14.3% of patients required renal replacement therapy (RRT). [70]
Myocardial injury manifesting as myocardial ischemia/infarction (MI) and myocarditis are well-recognized cardiac manifestations in patients with COVID-19. Single-center retrospective study analysis of 187 patients with confirmed COVID-19 reported that 27.8% of patients exhibited myocardial injury indicated by elevated troponin levels. The study also noted that patients with elevated troponin levels had more frequent malignant arrhythmias and a higher mechanical ventilation frequency than patients with normal troponin levels. [71] A meta-analysis of 198 published studies involving 159698 COVID-19 patients reported that acute myocardial injury and a high burden of pre-existing cardiovascular disease were significantly associated with higher mortality and ICU admission. [72]
Lymphopenia is a common laboratory abnormality in most patients with COVID-19. Other laboratory abnormalities include thrombocytopenia, leukopenia, elevated ESR levels, C-reactive protein (CRP), lactate dehydrogenase (LDH), and leukocytosis.
COVID-19 is also associated with a hypercoagulable state, evidenced by the high prevalence of venous thromboembolic events. COVID-19 is associated with markedly elevated D-dimer and fibrinogen levels and prolonged prothrombin time (PT) and partial thromboplastin time (aPTT). [71] [55]
GI symptoms (such as diarrhea, nausea, vomiting), anorexia, and abdominal pain are common. A meta-analysis reported that the weighted pool prevalence of diarrhea was 12.4% (95% CI, 8.2% to 17.1%), nausea or vomiting was 9% (95% CI, 5.5% to 12.9%), loss of appetite was 22.3% (95% CI, 11.2% to 34.6%) and abdominal pain was 6.2% (95% CI, 2.6% to 10.3%). The study also reported that the mortality rate among patients with GI symptoms was similar to the overall mortality rate. [73] Cases of acute mesenteric ischemia and portal vein thrombosis have also been described. [74]
An acute increase in aspartate transaminase (AST) and alanine transaminase (ALT) is noted in 14% to 53% of patients with COVID-19 infection. [75]
Guillain-Barré syndrome (GBS) cases from Northern Italy have also been reported. [76] [77]
Acral lesions resembling pseudo chilblains (40.4%) are the most common cutaneous manifestation noted in patients with COVID-19. [78]
Other cutaneous manifestations include erythematous maculopapular rash (21.3%), vesicular rashes (13%), urticarial rashes (10.9%), vascular rashes (4%) resembling livedo or purpura, and erythema multiforme-like eruptions (3.7%). [78]

Diagnostic Testing in COVID-19

A nasopharyngeal swab for SARS-CoV-2 nucleic acid using a real-time PCR assay is the standard diagnostic test.[ NIH COVID-19 Treatment Guidelines ] Commercial PCR assays have been authorized by the USA Food and Drug Administration (FDA) for the qualitative detection of SARS-CoV-2 virus using specimens obtained from nasopharyngeal swabs as well as other sites such as oropharyngeal, anterior/mid-turbinate nasal swabs, nasopharyngeal aspirates, bronchoalveolar lavage (BAL) and saliva.

The sensitivity of PCR testing depends on multiple factors, including the specimen's adequacy, time from exposure, and specimen source. [79] However, the specificity of most commercial FDA-authorized SARS-CoV-2 PCR assays is nearly 100%, provided there is no cross-contamination during specimen processing. SARS-CoV-2 antigen tests are less sensitive but have a faster turnaround time than molecular PCR testing. [80]

Despite the numerous antibody tests designed to date, serologic testing has limitations in specificity and sensitivity, and results from different tests vary. According to the NIH guidelines, diagnosing acute SARS-CoV-2 infection based on serologic testing is not recommended. They also stated that there is insufficient evidence to recommend for or against using serologic testing to assess immunity, even if it is used to guide clinical decisions about COVID-19 vaccines/monoclonal antibodies.[ NIH COVID-19 Treatment Guidelines ]

Other Laboratory Assessment

Complete blood count (CBC), a comprehensive metabolic panel (CMP) that includes renal and liver function testing, and a coagulation panel should be performed in all hospitalized patients.
Additional tests, such as ESR, C-reactive protein (CRP), ferritin, lactate dehydrogenase, and procalcitonin, can be considered in hospitalized patients. However, their prognostic significance in COVID-19 is not clear.
A D-dimer level is required as it guides the use of therapeutic versus prophylactic doses of anticoagulation.

Imaging ModalitiesThis s viral illness commonly manifests as pneumonia, so radiological imaging such as chest x-rays, lung ultrasounds, and chest computed tomography (CT) are often obtained. However, there are no guidelines regarding the timing and choice of pulmonary imaging in patients with COVID-19.

When obtained, the chest X-ray usually shows bilateral multifocal alveolar opacities. Pleural effusions can also be demonstrated. The most common CT chest findings in COVID-19 are multifocal bilateral ground glass opacities with consolidation changes, usually in a patchy peripheral distribution. [81]

Radiologic imaging is not a sensitive method for detecting this disease. A retrospective study of 64 patients with documented COVID-19 reported that 20% had no abnormalities on chest radiographs during the illness. [82] A chest CT is more sensitive than a radiograph but is not specific. No finding on radiographic imaging can completely rule in or rule out COVID-19 illness. Therefore the American College of Radiology (ACR) advises against the routine use of chest CT for screening or diagnosis of COVID-19.[ ACR Position Statement for Diagnosis of COVID-19 ]

Treatment / Management

According to the National Institutes of Health (NIH), the 2 main processes driving the pathogenesis of COVID-19 include replication of the virus in the early phase of the illness and dysregulated immune/inflammatory response to SARS-CoV-2 that leads to systemic tissue damage in the later phase of the disease.[ NIH COVID-19 Treatment Guidelines ] The guidelines, therefore, advise antiviral medications to halt viral replication in the early phase of the illness and immunomodulators in the later phase.

Remdesivir is the only antiviral drug approved by the USA Food and Drug Administration (FDA) to treat COVID-19. Ritonavir-boosted nirmatrelvir, molnupiravir, and high-titer COVID-19 convalescent plasma have Emergency Use Authorizations (EUAs) for treating COVID-19. Tixagevimab 300 mg plus cilgavimab 300 mg monoclonal antibodies have received EUAs that allow them to be used as SARS-CoV-2 preexposure prophylaxis (PrEP) in certain patients.

Many other monoclonal antibodies had EUAs; however, as Omicron subvariants emerged, their EUAs were revoked as they were no longer effective.

The most recent NIH treatment guidelines for the management of COVID-19 illness (accessed on January 3rd, 2023) are outlined below:[ NIH COVID-19 Treatment Guidelines ]

Nonhospitalized Adults With Mild-to-Moderate COVID-19 Illness Who Do Not Require Supplemental Oxygen

The NIH recommends against using dexamethasone or any other systemic corticosteroids in patients who are not hypoxic. [83]
Ritonavir-boosted nirmatrelvir is a combination of oral protease inhibitors. It has been shown to reduce hospitalization and death when given to high-risk, unvaccinated, nonhospitalized patients. It must be given within 5 days of symptoms onset. [84]
It is a strong cytochrome P450 inhibitor with many drug-drug interactions that must be carefully assessed.
Some interactions can be managed by temporarily holding the medication, some may be managed with dose adjustment, but some may warrant the use of alternate COVID-19 therapy.
Ritonavir-boosted nirmatrelvir is not recommended in patients with an estimated glomerular filtration rate (eGFR) of less than 30 mL/min.
The recommended dose is nirmatrelvir 300 mg with ritonavir 100 mg orally twice daily for 5 days.
This is a nucleotide analog that inhibits the SARS-CoV-2 RNA polymerase
The recommended duration of therapy in this setting is 3 days.
The recommended dose is 200 mg IV on day 1, followed by 100 mg IV for 2 more days.
It is a mutagenic ribonucleoside antiviral agent.
Fetal toxicity has been reported in animal studies with this agent. Due to the risk of genotoxicity with this agent, it is not recommended in pregnant patients.
This agent should only be used if both therapies are unavailable or cannot be given.
The NIH guidelines recommend against using anti-SARS-CoV-2 monoclonal antibodies (mAbs) for treating COVID-19 in this cohort because the Omicron subvariants are not susceptible to these agents.
Adequate and close medical follow-up is recommended; however, the frequency and duration of follow-up depend on individual risk factors and the severity of their symptoms.
Risk factors for progression to severe disease include advanced age and underlying medical conditions. The CDC maintains an updated list of medical conditions associated with a high risk of progression.
Asthma
Cerebrovascular disease
Chronic kidney disease
Bronchiectasis
COPD (Chronic obstructive pulmonary disease)
Interstitial lung disease
Pulmonary embolism
Pulmonary hypertension
Nonalcoholic fatty liver disease
Alcoholic liver disease
Autoimmune hepatitis
Cystic fibrosis
Diabetes, type 1 and 2
Heart conditions (such as heart failure, coronary artery disease, or cardiomyopathies)
HIV (Human immunodeficiency virus)
Mental health conditions such as mood disorders and Schizophrenia spectrum disorders
Obesity (defined as body mass index (BMI) of greater than 30 kg/m 2 or greater than 95th percentile in children)
Pregnancy and recent pregnancy
Smoking, current and former
Solid organ or blood stem cell transplantation
Tuberculosis
Use of corticosteroids or other immunosuppressive medications ( CDC: Underlying Medical Conditions Associated with Higher Risk )

Therapeutic Management of Hospitalized Adults With COVID-19 Who Do Not Require Oxygen

If patients are hospitalized for reasons other than COVID-19 illness and are not on oxygen, their management is similar to nonhospitalized patients.
If they are hospitalized for COVID-19 illness but do not require oxygen, the NIH advises against the use of dexamethasone or any other systemic corticosteroid.
A prophylactic dose of anticoagulation should be given if there is no contraindication.
If they are hospitalized for COVID-19 illness, do not require oxygen, but are at high risk of progression to severe disease, they should be treated with remdesivir.
The benefit of remdesivir is greatest when given early, ideally within ten days of symptom onset.
Remdesivir should be given for 5 days or until hospital discharge.

Therapeutic Management of Hospitalized Adults With COVID-19 Who Require Conventional Oxygen

Conventional oxygen is defined as oxygen that is NOT high-flow nasal cannula, noninvasive mechanical ventilation, mechanical ventilation, or extracorporeal membrane oxygenation (ECMO)
For most patients in this cohort, the recommended treatment is dexamethasone plus remdesivir.
Dexamethasone dose is 6 mg IV or oral (PO) once daily for up to 10 days or until hospital discharge (dexamethasone should not be continued at discharge). [83]
If the patient is on minimal oxygen, remdesivir monotherapy (without dexamethasone) should be used.
If remdesivir cannot be obtained or given, dexamethasone monotherapy is recommended.
If dexamethasone is unavailable, corticosteroids such as prednisone, methylprednisolone, or hydrocortisone may be used.
If the patient is already receiving dexamethasone but has rapidly increasing oxygen needs and/or signs of systemic inflammation, oral baricitinib or intravenous (IV) tocilizumab should be added to the treatment regimen as these agents have been shown to improve outcomes in rapidly decompensating patients. [85]
Alternate immunomodulatory agents for this cohort include oral tofacitinib and IV sarilumab. These agents should only be used if baricitinib and tocilizumab are not available.
If the D-dimer level is above normal in this cohort of patients, they recommend therapeutic anticoagulation if the patient is not pregnant and has no increased risk of bleeding. Contraindications for therapeutic anticoagulation in these patients include a platelet count of less than 50 x10^9 /L, hemoglobin less than 8 g/dL, use of dual antiplatelet therapy, any significant bleeding within the past 30 days, a history of a bleeding disorder or an inherited or active acquired bleeding disorder.
For pregnant patients, a prophylactic dose of anticoagulation is recommended.

Therapeutic Management of Hospitalized Adults With COVID-19 who Require High-flow Nasal Cannula (HFNC) or Noninvasive Mechanical Ventilation (NIV)

A meta-analysis study evaluating the effectiveness of HFNC compared to conventional oxygen therapy and NIV before mechanical ventilation reported that HFNC, when used before mechanical ventilation, could improve the prognosis of patients compared to conventional oxygen therapy and NIV. [86] HFNC or NIV is associated with decreased dispersion of exhaled air, especially when used with good interface fitting, thus creating a low risk of nosocomial transmission of the infection. [87] However, these treatment modalities are associated with a greater risk of aerosolization and should be used in negative-pressure rooms. [88]
According to the NIH, dexamethasone plus oral baricitinib or dexamethasone plus IV tocilizumab are the preferred treatment regimens in these patients.
Alternate immunomodulatory agents for this cohort include oral tofacitinib and IV sarilumab.
Dexamethasone monotherapy is recommended if baricitinib, tocilizumab, or sarilumab cannot be obtained/given.
Clinicians may consider adding remdesivir to corticosteroid and immunomodulator combination regimens in immunocompromised patients who require HFNC or NIV ventilation; however, using remdesivir without immunomodulators is not recommended.
A prophylactic dose of anticoagulation is recommended in these patients.
If patients were started on a therapeutic dose of heparin while on conventional oxygen therapy, they should be switched to prophylactic dosing at this time unless they have another indication for full anticoagulation.

Therapeutic Management of Hospitalized Adults With COVID-19 who Require Mechanical Ventilation (MV)

The management of this cohort is the same as those requiring HFNC or NIV, except that remdesivir is not recommended.
Remdesivir is most effective earlier in the course of the disease and in patients not on mechanical ventilation or ECMO.
According to the NIH, one study showed a slight trend toward an increase in mortality in patients who received remdesivir while on mechanical ventilation or ECMO. [89]
With this data in mind, the NIH recommends against using remdesivir in patients receiving MV or ECMO; however, if the patient was started on remdesivir and progressed to requiring mechanical ventilation or ECMO, they recommended continuing remdesivir to complete the treatment course.

High-Titer COVID-19 Convalescent Plasma (CCP)

The United States Food and Drug Administration (FDA) approved convalescent plasma therapy under a EUA for patients with severe life-threatening COVID-19. [90] [91] Data from multiple studies evaluating the use of convalescent plasma in life-threatening COVID-19 has generated mixed results. Data from 3 small randomized control trials showed no significant differences in clinical improvement or overall mortality in patients treated with convalescent plasma versus standard therapy. [92] [93] [94]
According to the NIH, high-titer CCP is not recommended in immunocompetent individuals.
However, the NIH states that some experts consider it appropriate for use in immunocompromised individuals. Therefore, the current NIH guidelines state that there is insufficient evidence for or against the use of high-titer CCP for treating COVID-19 in hospitalized or nonhospitalized patients who are immunocompromised.

Medications/Treatments That Should NOT Be Used for the Treatment of COVID-19 According to the Latest NIH Guidelines [ NIH COVID-19 Treatment Guidelines ]

Chloroquine or hydroxychloroquine with or without azithromycin
Lopinavir/ritonavir
Azithromycin
Doxycycline
Fluvoxamine
Inhaled corticosteroids
Excess supplementation of vitamin C, vitamin D, and zinc
Interferons alfa, beta, or lambda
Nitazoxanide
Bamlanivimab plus etesevimab
Bebtelovimab
Casirivimab plus imdevimab

Preexposure Prophylaxis for SARS-CoV-2 Infection

According to the NIH guidelines, tixagevimab plus cilgavimab is authorized by the FDA for preexposure prophylaxis of SARS-CoV-2 in people who are not expected to mount an adequate immune response to COVID-19 vaccination; however, the prevalence of Omicron subvariants that are resistant to tixagevimab plus cilgavimab is noted to be increasing rapidly.
In the absence of alternative options, the NIH still recommends tixagevimab 300 mg plus cilgavimab 300 mg at this time.
Tixagevimab and cilgavimab are potent anti-spike neutralizing monoclonal antibodies obtained from antibodies isolated from B cells of patients infected with SARS-CoV-2 that have demonstrated neutralizing activity against SARS-CoV-2 virus by binding to nonoverlapping epitopes of the viral spike-protein RBD. [95] [96] [97]
The EUA authorizes its use in adult and pediatric patients with no current evidence of SARS-CoV-2 infection and no recent exposure to SARS-CoV-2-positive individuals. They must be moderately or severely immunocompromised or be on immunosuppressive medications.
Differential Diagnosis

The symptoms of the early stages of the disease are nonspecific. Differential diagnosis should include the possibility of a wide range of infectious and noninfectious respiratory disorders.

Community-acquired bacterial pneumonia
Viral pneumonia
Influenza infection
Aspiration pneumonia
Pneumocystis jirovecii pneumonia
Middle East respiratory syndrome (MERS)
Avian influenza A (H7N9) viral infection
Avian influenza A (H5N1) viral infection
Pulmonary tuberculosis

The prognosis of COVID-19 depends on various factors, including the patient's age, the severity of illness at presentation, preexisting conditions, how quickly treatment can be implemented, and response to treatment. The WHO currently estimates the global case fatality rate for COVID-19 is 2.2%. Results from a European multicenter prospective cohort study that included 4000 critically ill patients with COVID-19 reported a 90-day mortality of 31%, with higher mortality noted in geriatric patients and patients with diabetes, obesity, and severe ARDS. [98]

Complications

COVID-19 is a systemic viral illness based on its involvement in multiple major organ systems.

Patients with advanced age and comorbid conditions such as obesity, diabetes mellitus, chronic lung disease, cardiovascular disease, chronic kidney disease, chronic liver disease, and neoplastic conditions are at risk of developing severe COVID-19 and its associated complications. The most common complication of severe COVID-19 illness is progressive or sudden clinical deterioration leading to acute respiratory failure and ARDS or multiorgan failure leading to death.
Patients with COVID-19 illness are also at increased risk of developing prothrombotic complications such as pulmonary embolisms, myocardial infarctions, ischemic strokes, and arterial thrombosis. [55]
Cardiovascular system involvement results in malignant arrhythmias, cardiomyopathy, and cardiogenic shock.
GI complications such as bowel ischemia, transaminitis, gastrointestinal bleeding, pancreatitis, Ogilvie syndrome, mesenteric ischemia, and severe ileus are often noted in critically ill patients with COVID-19. [99]
Acute renal failure is the most common extrapulmonary manifestation of COVID-19 and is associated with an increased mortality risk. [69]
A meta-analysis study of 14 studies evaluating the prevalence of disseminated intravascular coagulation (DIC) in hospitalized patients with COVID-19 reported that DIC was observed in 3% (95%: 1%-5%, P <0.001) of the included patients. Additionally, DIC was noted to be associated with severe illness and was a poor prognostic indicator. [100]
More recent data have emerged regarding prolonged symptoms in patients who have recovered from COVID-19 infection, termed "post-acute COVID-19 syndrome." A large cohort study of 1773 patients performed 6 months after hospitalization with COVID-19 revealed that most exhibited at least one persistent symptom: fatigue, muscle weakness, sleep difficulties, or anxiety. Patients with severe illness also had an increased risk of chronic lung issues. [101]
A retrospective cohort study that included 236,379 patients reported substantial neurological (intracranial hemorrhage, ischemic stroke) and psychiatric morbidity (anxiety disorder, psychotic disorder) 6 months after being diagnosed with COVID-19. [102]
Secondary invasive fungal infections such as COVID-19-associated pulmonary aspergillosis and rhino-cerebro-orbital mucormycosis have increasingly been reported as complications in patients recovering from COVID-19. Risk factors for developing secondary fungal infection include comorbid conditions such as uncontrolled diabetes, associated lymphopenia, and excessive use of corticosteroids.
Deterrence and Patient Education

The NIH COVID-19 Treatment Guidelines recommend COVID-19 vaccination as soon as possible for all eligible individuals. The CDC’s Advisory Committee on Immunization Practices (AI) determines eligibility eligibility. Four vaccines are authorized or approved in the United States to prevent COVID-19. According to the NIH guidelines, the preferred vaccines include:[ NIH COVID-19 Treatment Guidelines ]

mRNA vaccine BNT162b2 (Pfizer-BioNTech)
mRNA-1273 (Moderna)
Recombinant spike protein with matrix-M1 adjuvant vaccine NVX-CoV2373 (Novavax)

The adenovirus vector vaccine Ad26.COV2.S (Johnson & Johnson/Janssen) is less preferred due to its risk of serious adverse events.[ NIH COVID-19 Treatment Guidelines ]

A primary series of COVID-19 vaccination is recommended for everyone older than 6 months in the United States. Bivalent mRNA vaccines that protect against the original SARS-CoV-2 virus strain and Omicron subvariants are recommended at least 2 months after receiving the primary vaccine series or a booster dose.[ NIH COVID-19 Treatment Guidelines ]

Enhancing Healthcare Team Outcomes

SARS-CoV-2 and its variants continue to cause significant morbidity and mortality worldwide. Prevention and management of this highly transmissible respiratory viral illness require a holistic and interprofessional approach that includes physicians' expertise across specialties, nurses, pharmacists, public health experts, and government authorities. There should be open communication among the clinical providers, pharmacists, and nursing staff while managing patients with COVID-19. Each team member should strive to keep abreast of the latest recommendations and guidelines and be free to speak up if they notice anything that does not comply with the latest tenets for managing COVID patients; there is no place for a hierarchy in communication that prohibits any team member from voicing their concerns. This open interprofessional approach will yield the best outcomes.

Clinical providers managing COVID-19 patients on the frontlines should keep themselves periodically updated with the latest clinical guidelines about diagnostic and therapeutic options available in managing COVID-19, especially considering the emergence of new SARS-CoV-2 variants, which could significantly impact morbidity and mortality. Continued viral surveillance of new variants is crucial at regular intervals with viral genomic sequencing, given the possibility that more highly transmissible, more virulent, and treatment-resistant variants could emerge that can have a more catastrophic effect on global health in addition to the current scenario. A multi-pronged approach involving interprofessional team members can improve patient care and outcomes for this potentially devastating disease and help the world end this pandemic.

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Covid 19, Corona Replication Contributed by Rohan Bir Singh, MD

Clinical Presentation of Patients with CoVID-19 Contributed by Rohan Bir Singh, MD; Made with Biorender.com

SARS- CoV 2 Structure Contributed by Rohan Bir Singh, MD; Made with Biorender.com

Transmission Cycle of SARS CoV 2 Contributed by Rohan Bir Singh, MD; Made with Biorender.com

Single-stranded RNA genome of SARS-CoV2 Contributed by Rohan Bir Singh, MD; Made with Biorender.com

Disclosure: Marco Cascella declares no relevant financial relationships with ineligible companies.

Disclosure: Michael Rajnik declares no relevant financial relationships with ineligible companies.

Disclosure: Abdul Aleem declares no relevant financial relationships with ineligible companies.

Disclosure: Scott Dulebohn declares no relevant financial relationships with ineligible companies.

Disclosure: Raffaela Di Napoli declares no relevant financial relationships with ineligible companies.

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Cite this Page Cascella M, Rajnik M, Aleem A, et al. Features, Evaluation, and Treatment of Coronavirus (COVID-19) [Updated 2023 Aug 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Data Descriptor
Open access
Published: 12 September 2024

A comprehensive IDA and SWATH-DIA Lipidomics and Metabolomics dataset: SARS-CoV-2 case control study

Ammar Tahir ORCID: orcid.org/0000-0003-3682-5680 1 , 2 ,
Agnes Draxler 3 , 4 , 5 ,
Tamara Stelzer 3 , 4 ,
Amelie Blaschke 6 ,
Brenda Laky 7 , 8 , 9 ,
Marton Széll 6 ,
Jessica Binar 2 , 3 ,
Viktoria Bartak 3 ,
Laura Bragagna 3 , 4 ,
Lina Maqboul ORCID: orcid.org/0009-0000-6080-886X 3 , 10 ,
Theresa Herzog 6 ,
Rainer Thell 6 &
Karl-Heinz Wagner ORCID: orcid.org/0000-0002-1683-7265 3 , 10

Scientific Data volume 11 , Article number: 998 ( 2024 ) Cite this article

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Metrics details

Diagnostic markers
Metabolomics

A significant hurdle in untargeted lipid/metabolomics research lies in the absence of reliable, cross-validated spectral libraries, leading to a considerable portion of LC-MS features being labeled as unknowns. Despite continuous advancement in annotation tools and libraries, it is important to safeguard, publish and share acquired data through public repositories. Embracing this trend of data sharing not only promotes efficient resource utilization but also paves the way for future repurposing and in-depth analysis; ultimately advancing our comprehension of Covid-19 and other diseases. In this work, we generated an extensive MS-dataset of 39 Covid-19 infected patients versus age- and gender-matched 39 healthy controls. We implemented state of the art acquisition techniques including IDA and SWATH-DIA to ensure a thorough insight in the lipidome and metabolome, ensuring a repurposable dataset.

A resource of lipidomics and metabolomics data from individuals with undiagnosed diseases

Q-RAI data-independent acquisition for lipidomic quantitative profiling

Integrated multiomics analysis to infer COVID-19 biological insights

Background & summary.

Covid-19 is an infection caused by the SARS-CoV-2 virus. Individuals infected with SARS-CoV-2 exhibit a range of symptoms that might include fever, cough, loss of taste or smell and shortness of breath 1 . As well as a range of other “common cold like” symptoms such as fatigue, congestion or runny nose, nausea or vomiting, and diarrhoea 2 .

On the molecular level, a Covid-19 infection can trigger many alterations in the haemostatic system 3 . Immune response, for instance, can be significantly altered; studies reported a massive upregulation of cytokines and chemokines leading to cytokine storm 4 resulting in complications that may lead to acute respiratory system complications and even multiorgan failure 5 . Moreover, blood clotting and coagulation pathways activated by the virus lead to deep vein thrombosis (DVT), pulmonary embolism (PE) as suggested by some recent studies 6 , 7 . On the metabolic level, Covid-19 infections seem to instigate alterations in glucose, lipid, and amino acids metabolism 8 , 9 , 10 .

Metabolomics and lipidomics

Omics analysis enables a deeper understanding of biological mechanisms. Although each layer of omics contains enough complexity to expound a certain mechanism, combining two or more omics datasets allows the unlocking of new insights into the cellular functionality, which in turn helps in understanding the underlying biological aspects of complex pathologies 11 , 12 . Metabolomics, for instance, is predominantly conceded to provide the match from genotype to the phenotype (given the availability of genomics data): metabolites screening allows the examination of metabolic changes, revealing the alterations in pathways 13 , 14 . Lipidomics, especially the targeted approach, stands as one of the most well-established tools to evaluate, diagnose and better understand human pathologies 15 , 16 .

Many recent studies 17 , 18 , 19 have highlighted the power of Multiomics in describing the metabolic alteration caused by a Covid-19 infection. In these studies, extensive genomics, metabolomics and lipidomics analysis revealed distinct alteration associated with the infection.

Few studies, however, considered the phenotype effect on the analysis and interpretation. By correlating phenotypic clinical readings with untargeted analysis, a new way emerges to better understand the mechanisms of a disease 13 , 20 .

One major challenge in the field of untargeted omics is the lack of substantiated, cross validated libraries 21 , 22 , 23 . A decent portion of the analysed chromatographic features in each study remains thus undeciphered and is “usually” annotated as unknowns 24 . More often than not, these unknowns are regulated (up or down) and correlate with their regulation with numerous identified targets and biomarkers 25 .

The realm of omics is developing, the percent of the undeciphered portion is consistently getting smaller, and there are lots of tools emerging annotating these unknowns. Until better tools and more conclusive libraries are available, it is very important to conserve the data that is already acquired 26 . Saving and sharing data in public repositories and repurpose them later is an advancing trend with many advantages including resources conservation, reduction of redundant clinical and animal trials 26 , 27 , 28 .

Motives and aims

In this work we describe the metabolome and lipidome profiles of 39 Covid-19 patients in contrast to 39 healthy individuals. We strived to produce a comprehensive dataset by employing, to our knowledge, state-of-the-art metabolomics and lipidomics methodologies. Furthermore, this dataset was supported by comprehensive phenotype data including clinical and co-morbidity information for each study sample. The study samples were analysed using an extensive workflow (see Fig. 1 ) to ensure the acquisition of all relevant “potential” metabolites. Specifically, we aimed to implemented both reversed phase ultra-high performance liquid chromatography (RP-UHPLC) chromatography for the separation of lipids and hydrophilic interaction liquid chromatography (HILIC) chromatography for the separation of the polar metabolites. In both chromatography modes we acquired using IDA (Independent Data Acquisition) in positive and negative ionization modes with top 3 ions mode using 2 fragmentation energies to ensure a comprehensive coverage using a quadrupole time-of-flight (QTOF) mass spectrometer. Additionally, we implemented SWATH-DIA (Sequential Window Acquisition of All Theoretical Mass Spectrometry – Data Independent Acquisition) because we aimed for a comprehensive non-biased dataset that allows for the acquisition of fragment ion spectra for all detectable metabolites 29 regardless of their parent m/z, also, we wanted to have the option of being able to re-visit this dataset with future “yet to be developed” tools in a retrospective manner 30 .

Workflow of the study.

Study design

In the period between December 2020 to February 2021, the Analyses Blood Covid DNA (ABCD) study was carried out as a prospective case control study performed at the Emergency Department at the Clinic Donaustadt (Vienna, Austria), as well as at the Department of Nutritional Sciences, University of Vienna. In this study, Covid-19 infected participants (n = 48) were enrolled at the Emergency Department of the Clinic Donaustadt. Controls were recruited based on matching for age- and gender (n = 48) in the University of Vienna and the recruiting hospital (mainly staff members). Older adults were from current projects (‘NutriAging’: ‘protein study: https://clinicaltrials.gov/ct2/show/NCT04023513 and Vitamin D study: https://clinicaltrials.gov/ct2/show/NCT04341818 ). A summary of demographics and some key clinical parameters of patients and healthy subjects groups are shown in Table 1 . The study design is illustrated in Fig. 2 , The inclusion criteria for Covid-19 patients encompassed factors such as sex male/female), age ≥ 40 years), and the ability to provide written informed consent. Additionally, Covid-19 suspected patients had to be admitted to the emergency department or hospital during the acute infection. For Covid-19 patients, the criteria excluded those not hospitalized and individuals without definitive Covid-19 confirmation. The inclusion criteria for healthy controls were as follows: age ≥ 40 years), the ability to provide written informed consent and absence of severe illnesses. Exclusion criteria for controls involved clinically significant diseases, while a shared exclusion criterion for both groups was pregnancy. All relevant clinical parameters (Ct values qPCR, leukocytes, thrombocytes, erythrocytes, hemoglobin, hematocrit, MCV, MCH, MCHC, lymphocyte abs, monocyte abs, eosinophile granulocyte abs, basophile granulocytes abs, lymphocytes rel, monocytes rel, eosinophile granulocytes rel, basophile granulocytes rel, CRP, creatinine, uric acid, ASAT got, ALAT gpt, glucose, cholesterol, HDL cholesterol, triglyceride, IL -6, alkaline phosphatase, LDH, iron blood levels, transferrin, transferrin saturation, ferritin, albumin, bilirubin, vitamin D) for the participants were checked at the day of admission, and are shown in the descriptive file in our repository (Clinical Metadata.xls) 31 . The study was approved by the Ethical Commission of the City of Vienna (No. EK_20_284_1120) and was conducted in accordance with the approved guidelines by the Declaration of Helsinki. Written informed consent to take part in the study was received prior to participation, as well as for the sharing of all relevant data. The study was registered at ClincalTrilals.gov (Identifier: NCT04784468).

Flowchart depicting the workflow of the ABCD-Covid-19 study, employing LC-MS analysis. The flowchart outlines the sequential steps involved in data acquisition, preprocessing, and analysis and serves as a guide to illustrate the methodology employed in the study, facilitating reproducibility and transparency in the research process.

Plasma collection

Venous Blood samples were collected by venipuncture into EDTA-coated vacutainers. Samples were kept at room temperature for 30 minutes prior to separation of plasma (centrifuged at 3500 RCF for 15 minutes at 4 °C) for analysis, no haemolysis was observed, and then separated into aliquots and stored in 1.5 mL Eppendorf tubes at − 80 °C until analysed.

Samples (50 μL plasma aliquots) were thawed only immediately before analysis and each sample was spiked with 10 μL of LC-MS internal standards (SPLASH TM Lipidomix® Mass Spec Standard, Avanti Polar Lipids, Inc.). Afterwards, samples were extracted using a modified Methyl tert-butyl ether (MTBE) (VWR Chemicals, Radnor, Pennsylvania USA) extraction protocol as suggested by Mataysh et al . 32 Briefly, in an 1.5 mL Eppendorf, a 50 μL plasma aliquot was homogenized with 300 µL ice cold Methanol (VWR Chemicals, Radnor, Pennsylvania USA) using an ultrasonic bath for 10 minutes. Afterwards 1 mL of MTBE was added and the mixture was vortexed vigorously. The Eppendorf tubes were incubated on a cooled shaker for 60 minutes. Afterwards, the mixture was transferred into a new Eppendorf tube and 250 µL Milli-Q H 2 0 (Merck, Darmstadt, Germany) were added. After vortexing and centrifugation (2000 g for 1 minute) 2 phases were formed: top MTBE phase containing the lipophilic compounds for the lipidomics analysis, and a bottom (water: methanol) phase for the metabolomics analysis. Both phases were dried using a speed vac (SpeedVac SPD1030 Thermofisher Scientific, Bremen, Germany) at room temperature and 5.1 torr and stored until analysis time.

QC Samples and blanks and order of acquisition

QC samples were individually prepared for lipidomics and metabolomics. For both QCs, 5 μL from each sample (healthy and covid-19) were pooled together in one vial. Blank were the same solution used to dissolve the dried samples: 100% Methanol for lipidomics and 80% Methanol for metabolomics. Each workflow was acquired in 5 analytical batches (Named B1-5 in the repository), the order of the blanks (12 Blanks per batch) and QC (5 QC samples per batch) can be seen in the sequence files tabs in the repository{Ammar Tahir, 2023 #40}.

Lipidomics using RP-UHPLC-QTOF

The dried lipidomics phases were dissolved (assisted with vortexing and sonification) in 150 µL methanol prior to analysis. Analysis was performed using an adapted 15-minute gradient as suggested by Fiehn et al . 33 using AQUITY UPLC BEH C18 Column (Waters, Milford Massachusetts, USA), 130 Å, 1.7 µm, 2.1 mm × 100 mm and the following mobile phases: (A) 60:40 (v/v) acetonitrile: water with 10 mM ammonium formate in positive mode or 10 mM ammonium acetate in negative mode (B) 90:10 (v/v) isopropanol: acetonitrile with 10 mM ammonium formate in positive mode or 10 mM ammonium acetate in negative mode. All chemicals were purchased from VWR Chemicals (Radnor, Pennsylvania USA). Gradient details are listed in Table 2 below.

Untargeted lipid profiling was performed using Sciex X500R QTOF (AB Sciex, Darmstadt, Germany). Data were acquired using the IDA method (all metabolites option) using the original Sciex OS ver 2.0.1 acquisition software with the parameters shown in Table 3 below.

Data acquired using SWATH-DIA method using the original Sciex OS ver 2.0.1 acquisition software with the parameters as shown in Table 4 and with the isolation windows shown in Table 5 below.

Metabolomics using HILIC-UHPLC-QTOF

The dried metabolomics phases were dissolved in 150 µl 80% Methanol prior to analysis. Analysis was performed using an adapted 10-minute gradient as suggested by Fiehn et al . 33 using HILIC Phenomenex, 130 Å, 1.7 µm, 2.1 mm × 100 mm and the following mobile phases: (A) 95:5 (v/v) acetonitrile: water with 10 mM ammonium formate (B) 50:50 (v/v) acetonitrile: water with 10 mM ammonium formate. Untargeted metabolites profiling was performed using Sciex X500R QTOF. Data were acquired using the IDA method (all metabolites option) with same parameters mentioned above using the original Sciex OS ver 2.0.1 acquisition software. All chemicals were purchased from VWR Chemicals (Radnor, Pennsylvania USA). Gradient minutes are listed in Table 6 .

Lipids and metabolites identification and statistics

Raw data were analyzed using MSDIAL ver.4.9.221218 Windowsx64 34 (Key MS-DIAL parameters: Peak detection parameters: min peak height = 1000 amplitude, mass slice width = 0.05 Da; MS2Dec: sigma window value = 0.5, MS/MS abundance cutoff = 10 amplitude; identification: accurate mass tolerance = 0.005 Da, identification score cutoff = 80%; Alignment: RT tolerance = 0.05 min, MS1 tolerance = 0.005 Da;), the raw dataset comprised of four groups (Healthy, Covid-19, QC and Blanks), were firstly normalized and batch corrected based on the QC sample pools using the LOESS algorithm 35 and Internal standards workflows of the MSDIAL. Afterwards. Processed data were then filtered for high-quality peaks based on the 2-Way ANOVA p-value score and their RSD (Relative Standard Deviation) values. Lipids were identified using MSDIAL via the integrated Lipidblast 36 package, the identification of the lipids was pursued in both negative and positive modes, we ensured choosing the right modifier type in MSDIAL Lipidblast MSP file tab and also we ticked all the possible adducts available in the adducts tab. Using this workflow, we were able to detect a total of 3195 features, of which 2067 with MS2 spectra and only 1095 with reference spectra in Lipidblast. In positive mode, we were able to detect a total of 4637 features, of which 2717 with MS2 spectra and only 1318 with reference spectra in Lipidblast. Metabolites were identified using MSDIAL ver.4.9.221218 Windowsx64 34 via the spectral database 36 package “ESI(+)-MS/MS from standards + bio + in silico (16,995 unique compounds), last edit 21.08.2022”. When peak annotation was not possible using the included spectra library, we used HMDB 37 , METLIN Gen2 38 (purchased 20.01.2023). The identification of the metabolites was pursued in positive modes, we ensured choosing the right modifier type in MSDIAL MSP file tab and, we ticked all the possible adducts available in the adducts tab. Using this workflow, we were able to detect a total of 3041 features, of which 1982 with MS2 spectra and only 175 with reference spectra. Finally, we manually curated: ms2 spectrum match based on top dot scores (>0.75) and adduct and duplicate removal) the identification hits and combined them into a single list (782 molecules) as shown in the descriptive file in our repository (identification peaklists.xls) 31 . Also, we included HMDB, KEGG, PubChem, ChEBI, METLIN, SMILES identifiers. We uploaded these identifiers in a separate CSV file called “HMDB_KEGG,PubChem,ChEBI,METLIN,SMILES -ID.csv” found in the others tab in the repository 31 .

It is worth mentioning, that although we strived to employ state-of-the-art libraries (as mentioned above) for lipids and metabolites and made every effort to carefully annotate our provided data, it is important to clarify that we do not assert the comprehensiveness or complete validity of our identification or annotation. These objectives were not the primary focus of our current study. Instead, our primary goal was to comprehensively acquire lipid and metabolite data in a format conducive to enhanced and more valid annotations through future tools and algorithms.

Data Records

All data are uploaded to the Center for Computational Mass Spectrometry: MASSIVE data repository. The dataset can be accessed over the identifier number MSV000092887 31 , or over the link: https://doi.org/10.25345/C5V40K90Q . All the spectra were uploaded in their native form (sciex Wiff2 file) and also as mzXML files. Moreover, we uploaded all the sequence files, result files from MS-DIAL analyses as mzdata files. Dataset folder includes following directories and subdirectories:

Metadata: f.MSV000092887/metadata/Clinical MetaData.xlsx: Includes all clinical parameters for the participants.

Peak: (mzxml files): f.MSV000092887/peak/: Includes all mzxml converted files

Raw: (sciex wiff2 files): f.MSV000092887/raw/: Includes all raw converted files

Search: f.MSV000092887/search: Includes all Ms-dial data processing parameters as well as the result siles as mzdata files.

Sequences: (Sciex OS acquisition sequence files): Includes all the sequences for the data acquisition performed in the experimental parts.

MS-DIAL settings (.med2A) Files can be opened using MS-DIAL, Also a Readme file is now also included in the data repository that explains how the repository is structured and to help reads and users to find the spectra and files.

Technical Validation

Statistical analysis.

The dataset of the identified hits was fully analyzed using Metabolanalyst 39 , 40 , 41 . First, we performed significance analyses using volcano plotting, out of 782 identified molecules (in the descriptive file in our repository (identification peaklists.xls) 31 ), 296 were down regulated, 70 were upregulated and the others were not significantly changed. The results of the analysis are shown in Fig. 3 , a detailed table of the underlying metabolites shown in the figure is also included in the data repository 31 in the others tab.

Volcano plot illustrating the differential regulation of the OMICS dataset, including metabolites and lipids. The thresholds used for significance determination are Fold Change (FC) ≥ 2 and a raw p-value of 0.05. Each point on the plot represents a unique metabolite or lipid, with those above the threshold indicating upregulation (in red) and those below indicating downregulation (in blue). For a comprehensive list of the underlying metabolites depicted in the figure, please refer to the detailed table available in the data repository 31 , accessible in the ‘Others’ tab. This plot provides a visual representation of the significant changes in metabolite and lipid expression, aiding in the identification of potential biomarkers or pathways associated with the studied conditions.

As evident, there are a considerable number of unknowns that we were not successfully able to annotate. These unidentified molecules, despite their regulatory relevance (Healthy vs. Covid-19), and despite efforts to annotate them using available libraries and in-silico methods, could not be correctly identified. These unannotated molecules, shown in in Fig. 3 and in the descriptive file in our repository (identification peaklists.xls) 31 , served as a primary motivation and driving factors for shaping the current study in this form we are presenting.

Furthermore, and to ensure the quality of the workflow, we subjected the two cohorts (Control and Covid-19) to PLSD analysis to verify the uniqueness of the identified metabolites to the relevant cohort as shown in Fig. 4 . Component 1, Component 2, and Component 3 show only 18.1%, 10.0% and 2.6% overlap, which indicates that the two cohorts contain unique compounds that are independently regulated.

PLSDA score plots illustrating the distribution of samples across the first three components. Component 1, Component 2, and Component 3 account for 18.1%, 10.0%, and 2.6% of the total variance, respectively. Each point on the plot represents an individual sample, with its position determined by its score along each component. The plot provides insight into the separation and clustering of samples based on their metabolic profiles or other relevant features These PLSDA score plots offer a visual representation of the multivariate relationships within the data, facilitating the identification of relevant patterns or trends associated with the studied conditions.

Moreover, we wanted to check our identified lipidome and metabolome profiles do match the up-to-date known described profiles; hence an examination using hierarchical analysis of the top 75 up/down regulated compounds and was carried out and plotted the results using a the heatmap analysis tool in Metaboanalyst. as shown in Fig. 5 . The up/down regulated compounds come in accordance with the recently described in literature data 17 , 42 , 43 .

Heatmap generated utilizing the Metaboanalyst platform. The heatmap provides a visual representation of the relative changes in metabolite abundance between Healthy and Covid-19 conditions, with colours ranging from red to blue indicating fold changes (FC). Red hues represent upregulated metabolites, while blue hues indicate downregulated metabolites, with intensity correlating to the magnitude of the FC. The heatmap is constructed based on the correlation between p-values and FC, allowing for the simultaneous visualization of statistical significance and biological relevance. Each row and column in the heatmap correspond to a unique metabolite, while the clustering of rows and columns enables the identification of metabolite groups or patterns associated with the experimental conditions.

Limitations of the annotation

A major known limitation of performing metabolomics and lipidomics data interpretation is how trustworthy the identification is. Usually, the quality of an identification starts with (a) a tentative annotation based on HRMS MS1 precursor ion masses with low mass drifts (±ppm). If this tentative annotation is coupled with (b) an MS2 spectra, it becomes more qualitative and trustworthy. Finally, the gold standard would be to couple these with (c) a match with an authentic analytical standard (RT and spectral match). Since the latest option “(c),” with internal standards, is very tenuous, expensive, and not realistic when trying to profile hundreds of metabolites. Most shotgun MS assays tend to base their identification workflows on HRMS MS1 “(a)” coupled to MS2 annotations “(b)” only.

In our workflow, we always had the tentative MS1 annotation with a maximum of 5 ppm drift, and we did our best to curate the data by matching the MS2 spectra to their best match. In MS-Dial, and using the Ms-find package, we were able to align the measured spectrum and match it against the possible reference library spectra, and for each spectrum alignment, there is a defined calculated dot score (0-1), with 1 being a perfect match. We strived our best to always pick the spectrum with the highest dot product. It must be clearly said that matching and generating perfect matches and annotations is not the main aim of the work, but rather to generate a well-measured dataset that could be used later to obtain better identifications. As with our usage of the up-to-date spectral libraries, this is the best we could get with manual curation of the data. That is why we included for each workflow two fragmentation energies and an “all ions” SWATH fragmentation to enhance the chances of getting a comprehensive MS2 fragmentation spectrum. We must also say that this is yet not very comprehensive, as it would require acquiring the same metabolite on different mass spectrometry architectures and different fragmentation and ionization arrangements, and then we might call the summation of all these collected spectra a comprehensive spectrum of a metabolite.

For the analytical validation of our workflow, we firstly checked for analytical variation. We hereby inspected the data before any QC samples LOESS normalization or batch correction. As shown in Fig. 6A the PCA score plot showing 2 distinct clusters Blanks vs. Others (Samples and QCs), which confirms the exclusion of any heavy carry over or high outlier possibility. By eliminating the blanks, as seen in Fig. 6B , we can observe how the QC samples cluster in relation to both groups. While not perfectly centred, which might indicate some variability and spread, we believe that considering the length of analytical batch measurements (10 days) and the frequency of sampling versus QC and blanks, as detailed in the sequence information in the repository, it still demonstrates very acceptable analytical validity.

( A ) Principal Component Analysis (PCA) score plot illustrating the distribution of control (blue), Covid-19 (red), blanks (black), and quality control (QC) (green) samples in the lipidomics analysis conducted in RP positive mode. PC1, representing the primary source of variation, explains 59.8% of the total variance observed across samples, while PC2 contributes 2.7% to the overall variance. Each point on the plot represents an individual sample, with clustering indicating similarities or differences in lipidomic profiles between sample groups. ( B ) Another PCA score plot showcasing the distribution of control, Covid-19, and QC samples in the lipidomics analysis conducted in RP positive mode. PC1 and PC2 explain 13.4% and 4.3% of the total variance, respectively. The distinct clustering of samples based on their lipidomic profiles provides insight into the metabolic differences between healthy controls, Covid-19 patients, and quality control samples.

Moreover, to verify the stability and the reproducibility of our system, we monitored 5 internal standard lipids (18:1(d7) Lyso PE, 15:0-18:1(d7) DAG, 18:1(d7) Chol Ester, d18:1-18:1(d9) SM and 18:1(d7) Lyso PC) from the spiked SPLASH TM Lipidomics® mixture and plotted their peak area over the whole samples population as shown in Fig. 7A . As seen in figure, we show only 5 lipids representing different species of the lipidome. The SPLASH TM mixture contains 14 lipids with concentrations ranging from 2ug/mL to 350 ug/mL, we spiked only 10uL which is then were diluted as explained in the extraction sections above. With this dilution it is not possible to detect all lipids in the SPLASH TM mixture, and we purposely did not inject more so avoid possible ion suppression. Hence, all shown 5 deuterated standards show a relatively stable response (in terms of peak area) which indicated batch, system and performance stability and reproducibility.

( A ) Quality control assessment of Sciex QTOF performance using selected deuterated lipids (18:1(d7) Lyso PE, 15:0-18:1(d7) DAG, 18:1(d7) Cholesterol Ester, d18:1-18:1(d9) Sphingomyelin, and 18:1(d7) Lyso PC) from the SPLASH TM Lipidomix®. The plots display the reproducibility of peak areas across all samples, including healthy control participants (nrCL) and Covid-19 patients (nrFL), ensuring consistent instrument performance and data reliability throughout the study. ( B ) Evaluation of LC-MS run quality, focusing on minimal carryover observed in blank samples analysed across the study. Total ion chromatograms (TIC) plots of QC samples (red lines) and blank samples (black lines) illustrate the absence of significant contamination or interference, validating the robustness of the LC-MS system for lipidomics analysis.

Lastly, we investigated carryover possibilities by overlaying, as shown in Fig. 7B , all “for all the batches” measured total ion chromatograms of blanks” in black” vs. all cohort QCs “in red. We detected very minimum (no significant) carry-over effect, that could affect the quality of the data through our workflow.

Study design limitations

Given that the study was conducted during the COVID-19 pandemic times, we were unable to collect certain important and vital parameters such as BMI, disease severity, symptoms, medications received, and other potential confounding factors. Although we acknowledge the significance of these parameters; however, unfortunately, they were not collected during the study period due to administrative and technical limitations.

Usage Notes

The dataset is available under Public Domain Dedication usage licence [dataset license: CC0 1.0 Universal (CC0 1.0)].

Code availability

The authors declare that no custom code was used.

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Acknowledgements

This work was supported by the University of Vienna, by funding the Research Platform Active Ageing. Open access funding provided by University of Vienna.

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Department of Pharmaceutical Sciences, Division of Pharmacognosy, University of Vienna, Vienna, Austria

Ammar Tahir

Section of Biomedical Sciences, Department of Health Sciences, FH Campus Wien, University of Applied Sciences, Vienna, Austria

Ammar Tahir & Jessica Binar

Department of Nutritional Sciences, University of Vienna, Vienna, Austria

Agnes Draxler, Tamara Stelzer, Jessica Binar, Viktoria Bartak, Laura Bragagna, Lina Maqboul & Karl-Heinz Wagner

Vienna Doctoral School for Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, Vienna, Austria

Agnes Draxler, Tamara Stelzer & Laura Bragagna

Department of Health Sciences, FH Campus Wien, University of Applied Sciences, Vienna, Austria

Agnes Draxler

Klinik Donaustadt, Emergency Department, Vienna, Austria

Amelie Blaschke, Marton Széll, Theresa Herzog & Rainer Thell

Medical University of Vienna, Vienna, Austria

Brenda Laky

Austrian Society of Regenerative Medicine, Vienna, Austria

Sigmund Freud University Vienna, Vienna, Austria

Research Platform Active Ageing, University of Vienna, Vienna, Austria

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A.T. wrote and concepted the manuscript, performed the lipidomics and metabolomics LC-MS measurements, performed the omics data and statistical analyses. K.H.W., R.T., A.D. and B.L. designed the clinical study, K.H.W. and R.T. financed the study. A. T., K.H.W. and B.L. revised the manuscript. A.D. and T.S. performed sample extraction and preparation. A.D., L.M. and L.B. performed laboratory analyses for clinical data generation. A.D., A.B., J.B., T.H., M.S.: performed blood sampling and preparation. A.T. and A.D. share equal contribution to this work.

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Correspondence to Ammar Tahir .

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Tahir, A., Draxler, A., Stelzer, T. et al. A comprehensive IDA and SWATH-DIA Lipidomics and Metabolomics dataset: SARS-CoV-2 case control study. Sci Data 11 , 998 (2024). https://doi.org/10.1038/s41597-024-03822-y

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Received : 05 October 2023

Accepted : 27 August 2024

Published : 12 September 2024

DOI : https://doi.org/10.1038/s41597-024-03822-y

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NIHR Leicester Biomedical Research Centre

Leicester’s PHOSP-COVID study

Since the immediate emergency posed by the pandemic in 2020, the PHOSP-COVID team have recruited more than 7,000 participants from 83 hospitals, obtaining 16 million data points and collecting more than 100,000 samples.

The resulting research, of which many papers have been published in the last 12 months, has highlighted large ongoing health impairments and provided insight into the possible biological mechanisms underpinning long-COVID, informing further research into potential treatments and patient care.

Two clinical trials of treatments for long-COVID are now underway. The first, PHOSP-R is a rehabilitation trial, which was completed in Spring 2024 and findings submitted for publication. In the second trial which commenced in April 2024, inflammatory long-COVID participants are taking part in a phase IIa double blind, randomised placebo-controlled trial of Tocilizumab to investigate the effect on health-related quality of life in adults with Long COVID and persistent inflammation (PHOSP-I) trial.

Working groups across all areas of physiology, including mental health and rehabilitation, with input from patient and charity groups has enabled a multimodal and holistic study of what continues to be a significant health burden on the population into 2024.

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