medical research council sum score (mrc ss)

• Open access
• Published: 01 December 2015

A new two-tier strength assessment approach to the diagnosis of weakness in intensive care: an observational study

• Selina M Parry 1 ,
• Sue Berney 2 , 3 ,
• Catherine L Granger 1 , 3 ,
• Danielle L Dunlop 2 ,
• Laura Murphy 2 ,
• Doa El-Ansary 1 ,
• René Koopman 4 &
• Linda Denehy 1 , 3  

Critical Care volume  19 , Article number:  52 ( 2015 ) Cite this article

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Introduction

Intensive care unit-acquired weakness (ICU-AW) is a significant problem. There is currently widespread variability in the methods used for manual muscle testing and handgrip dynamometry (HGD) to diagnose ICU-AW. This study was conducted in two parts. The aims of this study were: to determine the inter-rater reliability and agreement of manual muscle strength testing using both isometric and through-range techniques using the Medical Research Council sum score and a new four-point scale, and to examine the validity of HGD and determine a cutoff score for the diagnosis of ICU-AW for the new four-point scale.

Part one involved evaluation of muscle strength by two physical therapists in 29 patients ventilated >48 hours. Manual strength testing was performed by both physical therapists using two techniques: isometric and through range; and two scoring systems: traditional six-point Medical Research Council scale and a new collapsed four-point scale. Part two involved assessment of handgrip strength conducted on 60 patients. A cutoff score for ICU-AW was identified for the new four-point scoring system.

The incidence of ICU-AW was 42% (n = 25/60) in this study (based on HGD). In part one the highest reliability and agreement was observed for the isometric technique using the four-point scale (intraclass correlation coefficient = 0.90: kappa = 0.72 respectively). Differences existed between isometric and through-range scores (mean difference = 1.76 points, P  = 0.005). In part two, HGD had a sensitivity of 0.88 and specificity of 0.80 for diagnosing ICU-AW. A cutoff score of 24 out of 36 points was identified for the four-point scale.

Conclusions

The isometric technique is recommended with reporting on a collapsed four-point scale. Because HGD is easy to perform and sensitive, we recommend a new two-tier approach to diagnosing ICU-AW that first tests handgrip strength with follow-up strength assessment using the isometric technique for muscle strength testing if handgrip strength falls below cutoff scores. Whilst our results for the four-point scale are encouraging, further research is required to confirm the findings of this study and determine the validity of the four-point scoring system and cutoff score developed of less than 24 out of 36 before recommending adoption into clinical practice.

Intensive care unit-acquired weakness (ICU-AW) is a significant and prevalent problem for individuals who survive the initial insult of a critical illness [ 1 , 2 ]. Timely and accurate diagnosis is important in order for clinicians to target rehabilitation resources and select appropriate exercise modalities to minimise further muscle wasting [ 3 ]. It is important that muscle strength testing for diagnosis is reliable, valid and easily performed by different members of the multidisciplinary team.

Manual muscle testing (MMT) using the six-point Medical Research Council sum score (MRC-SS) is currently the recommended method for diagnosing ICU-AW [ 4 - 6 ]. There is criticism within the literature of this scoring system, particularly in its ability to discriminate between strength categories at the upper end of the scale (for example, between Grades 4 to 5 on the Oxford grading system) [ 7 - 9 ]. Whilst there is high inter-rater reliability for MRC-SS, the agreement levels reported for diagnosing ICU-AW have varied from poor to good in previous studies [ 8 , 10 - 13 ]. Worldwide there is variability in the testing technique utilised by clinicians, as strength testing can be assessed either isometrically (at one point in the range) or whilst moving through a joint’s range of motion [ 14 ].

Recently, a modified four-point scoring system with a transformed maximum sum score of 36 was developed in an attempt to improve the MRC grading system accuracy and to restore the ordered threshold requirements for ordinal categories [ 15 ]. This four-point scale was derived from the original six-point scoring system for muscle strength testing. The validity of this new scoring system has not been established in the intensive care unit (ICU) setting.

Thorough MMT requires clinicians to be adequately trained in the assessment procedures and it can take up to 30 minutes to test six muscle groups bilaterally. To reduce the time taken for assessment, handgrip dynamometry (HGD) has been recommended as a simple and easy surrogate measure for diagnosing ICU-AW [ 13 ]. Ali and colleagues developed cutoff scores for HGD based on gender (for males <11 kilograms (kg) and for females <7 kg is considered to be indicative of ICU-AW) [ 13 ]. However, uptake of HGD as a diagnostic assessment tool in clinical practice has been inconsistent with no published external validation of these previously developed cutoff scores.

Therefore, this study aimed to examine the methodological practices for MMT and HGD to provide a simplified standard recommendation for the clinical diagnosis of ICU-AW. This study was conducted in two parts. Parts of the results from this study have been reported in abstract format [ 16 ]. The CONsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines [ 17 ] were followed in the reporting of the evaluation of the reliability and validity of measures.

The primary aims of part one were to: (1) determine the inter-rater reliability for MMT and agreement for the diagnosis of ICU-AW using the six-point MRC-SS and the new four-point scoring systems; (2) examine differences between isometric and through-range technique and (3) determine the inter-rater reliability for HGD strength assessment. The primary aim of part two was to: (1) establish the validity of handgrip dynamometry and determine a cutoff point using the four-point scoring system for diagnosis of ICU-AW.

Materials and methods

Study design and setting.

This is a single-centre prospective study performed at a quaternary mixed medical and surgical ICU in Melbourne, Australia. The Austin Health institutional ethics committee approved the study. Written informed consent was not required from the patients for participation in this study as it involved the analysis of routinely collected data. Therapists provided verbal consent to the participation in this study.

Part one (reliability) involved two ICU physical therapists who evaluated a sample of 29 patients over a seven-month period between May and November 2012. Part two (validity) involved eight physical therapists of differing levels of expertise and took place from May 2012 to August 2013. Patients assessed in part one were included in the overall analyses performed within part two to provide data on an overall sample of 60 patients in order to enable an informed recommendation for a new two-tier approach to the clinical diagnosis of ICU-AW. The assessors were a convenience sample of physical therapists who were involved in the routine care of patients with critical illness at this centre over the study period. All assessors received training in MMT and HGD and a standardised protocol was followed at all times.

Study procedures

Data were collected on strength assessments performed by physiotherapists on adult patients (>18 years) with critical illness who were mechanically ventilated for more than 48 hours. Strength measurements were performed on the day of awakening. This was defined as the first day that the patient was alert with a Riker sedation agitation scale score between three and five [ 18 ] and ability to follow at least three of the De Jonghe five-command criteria [ 2 ]. Whilst no reliability or validity has been established for these five-point criteria, they have extensively used to evaluate awakening in the critical care research literature [ 2 , 10 , 19 ].

In part one (reliability), two physical therapists independently conducted MMT within a 24-hour period. MMT was evaluated using both techniques (isometric and through range) and scored using two scales (six-point [ 5 ] and four-point [ 15 ]) (Table  1 ). Four separate testing sessions were conducted (two per assessor) to enable determination of the reliability of different techniques and scoring systems. A 24-hour period in which all tests needed to be performed was chosen as a change in score clinically due to patient recovery would not be expected in this time frame, and thus should not influence the results obtained by the different assessors. In part two (validity) only the isometric technique was adopted based on the findings within part one and results were scored using both scales (six-point and four-point) (Table  1 ).

Screening for awakening and comprehension were evaluated on each testing occasion by each physical therapist. Both physical therapists assessed each patient in the same position in bed. At the first testing time point, assessor order and technique (isometric or through range) were randomly assigned by independent personnel not involved in the study using a random number generator and sealed opaque consecutively numbered envelopes. All assessments were performed within a 24-hour period, which enabled adequate rest in between assessments to minimise patient fatigue. Assessors were blinded to each other’s measurements. Patients were stable throughout the 24-hour testing period and testing conditions were similar at all four testing time points.

Manual muscle strength testing

Six muscle groups bilaterally were evaluated (shoulder abduction, elbow flexion, wrist extension, hip flexion, knee extension and ankle dorsiflexion). For the traditional six-point scoring system a score of less than 48 out of 60 is considered indicative of ICU-AW [ 2 ]. Muscle strength was initially assessed against gravity; if the patient was unable to perform the movement against gravity then the position was modified. In part one (reliability) MMT was evaluated both isometrically and through range. An isometric hold is when the resistance is applied at one point only and the subject pushes against this resistance. There is no movement of the joint. For example for shoulder abduction the assessor would apply resistance at 90 degrees shoulder abduction (mid range). Through-range testing means that the assessor provides resistance to the muscle whilst the joint is moving. For example, for shoulder abduction the assessor would apply resistance with the subject’s arm by their side at 0 degrees and continue to apply resistance until the patient reaches past 90 degrees abduction. The isometric technique has been well described previously in the literature [ 20 , 21 ]. Through-range testing was performed in a standardised manner through the joint’s range of motion. Therapist hand positioning and patient positioning were similar to that described in the isometric methodology for the through-range technique. Table S1 in Additional file 1 contains detailed description of the differences between isometric and through-range testing for each movement assessed.

Two scoring systems were utilised to quantify muscle strength using MMT. The traditional six-point scoring system is a six-point scale ranging from 0 = no muscle contraction to 5 = normal power against full resistance as shown in Table  1 . The modified (four-point) scoring system has four scores (0 = paralysis, 1 = severe weakness >50% loss, 2 = slight weakness <50% loss, and 3 = normal strength) as shown in Table  1 . This scoring system restores the ordered threshold requirements for ordinal categories [ 15 ]. It has been demonstrated that there is poorer agreement between assessors at the higher end of the six-point scoring system particularly in discriminating between 4 (reduced power) and 5 (normal strength) [ 7 - 9 ].

Handgrip dynamometry

A Jamar hydraulic dynamometer (Sammons Preston Rolyan, Bolingbrook, IL, USA) was used to evaluate handgrip strength. Prior to commencing this study two dynamometers were calibrated. The same two dynamometers were used throughout the study and manufacturer specifications for equipment care and storage were followed. The physiotherapist evaluated HGD if the patient had at least antigravity strength for both elbow flexor and wrist extensor muscle groups. Handgrip dynamometry was measured immediately post MMT on all occasions. Standardised instructions and encouragement were provided to patients by the physical therapist. In order to facilitate effective blinding of the physiotherapists, patients were not told the results of their efforts. The instructions during testing were similar to that previously described in the ICU literature [ 22 ] and patient set-up for assessment of HGD was conducted according to established guidelines for HGD testing [ 23 ]. Patients were given at least six seconds to generate maximum peak force, with a minimum of 60 seconds rest in between each test [ 22 ]. The highest HGD scores recorded (right and left) of three attempts were used in final analyses. In part two the cutoff scores previously developed by Ali and colleagues were used to classify presence or absence of ICU-AW based on gender [ 13 ]. The cutoff scores are <11 kg for males and <7 kg for females [ 13 ]. The overall incidence of ICU-AW was calculated based on the cutoff scores previously developed by Ali and colleagues. Handgrip strength was assessed in both part 1 (reliability) and part 2 (validity). MMT was utilised as the standard reference in part two (using the traditional six-point scoring system and isometric technique) against which HGD was compared for determining the validity and test performance of the previously developed HGD cutoff scores [ 13 ]. Although manual muscle strength testing has its limitations, it is currently recommended as the main diagnostic method for identifying ICU-AW at this point in time [ 24 ].

Other information collected

Baseline demographic information on admission diagnosis, gender, age, mechanical ventilation time, severity of illness according to the Acute Physiology and Chronic Health Evaluation Two (APACHE II) score were collected for the patients assessed in this study. Time to awakening, Physical Function in Intensive Care Test (PFIT) scores on awakening and ICU discharge, ICU and hospital length of stay (LOS), discharge destination were also recorded. Baseline demographic information (age, gender, ICU clinical experience in years) was also obtained from the physiotherapists participating in this study.

Statistical power and analyses

We calculated that 24 patient assessments were required to achieve a reliability coefficient >0.8 and P <0.05, in part one (reliability) [ 25 ]. Additionally, we calculated that 60 patients were required to test the sensitivity and specificity (chi-square analyses) of the four-point scale. In part two (validity) the sample size required was determined based on grip strength and four-point scoring system sensitivity and specificity and receiver operating characteristic (ROC) curve analyses. A sensitivity of 0.75 and specificity of 0.70 with an area under the curve (AUC) characteristic of 0.80 was determined a priori as acceptable. A score above 0.80 for AUC is considered good, and scores above 0.90 excellent [ 26 ]. Using these values we calculated that a sample size of 60 patients were required ([ 27 ] (accessed May 2012)).

In part one (reliability), both intraclass correlation coefficients (ICCs) (2, 1) and weighted linear kappa statistics were calculated to determine inter-rater reliability and agreement for diagnosing ICU-AW respectively. Paired t tests and Bland-Altman analysis were utilised to determine mean differences between isometric and through-range techniques. In part two (validity), chi-square analyses and the ROC curve were used to determine the test performance of HGD testing and to identify a cutoff score on the four-point scale, which would have the highest sensitivity and specificity for diagnosing ICU-AW. Convergent validity was examined using correlations (rho) for MMT, HGD, PFIT score, discharge destination, hospital parameters; mechanical ventilation time and ICU and hospital LOS. Parametric data were presented as mean and standard deviation (SD), and non-parametric data as median and interquartile range [IQR]. An alpha value <0.05 was considered statistically significant. SPSS for Macintosh statistical software package (SPSS Statistics version 20.0; IBM Corp, Armonk, NY, USA) was used in statistical analyses.

In part one (reliability), the assessors were two female physical therapists with five and eight years of clinical experience respectively in rehabilitation and strength testing in the ICU setting. In part two, eight physical therapists (all female) were involved. Levels of clinical experience ranged from six months to eight years, and ICU-specific clinical experience ranged from four months to five years.

Table  2 describes the demographic characteristics of the sample of 60 patients on whom assessments of muscle strength were performed. This sample includes the 29 patients tested within part one . The overall incidence of ICU-AW in this cohort was 42% (n = 25/60) based on handgrip dynamometry scores and their median [IQR] APACHE II score was 22 [ 17 - 22 , 24 - 26 , 28 , 29 ]. All physiotherapists were able to conduct a thorough strength examination including both MMT and HGD; there were no missing data from final analyses.

Part one – reliability

Inter-rater reliability for the overall scoring of manual muscle strength according to the MRC-SS was excellent regardless of the testing technique or scoring system utilised as represented by the ICCs shown in Table  3 . However, the strength of agreement for diagnosis of ICU-AW (less than 48 out of 60) was more variable with only fair agreement (kappa = 0.26) for the through-range technique using the six-point scale versus substantial agreement for the isometric technique using the six-point scale (kappa = 0.72) (Table  3 ).

There was a significant difference between isometric and through-range techniques when using the traditional six-point scoring system (mean difference = 1.76 points (out of 60), ninety-five percent confidence interval (95%CI) = 0.58 to 2.94, P  = 0.001). The mean ± SD MRC-SS out of 60 was 48 ± 6 for the isometric technique and 46 ± 8 for through-range technique using the six-point scale. The mean ± SD MRC-SS out of 36 was 27 ± 6 for the isometric technique and 26 ± 6 for the through-range technique using the new collapsed four-point scale.

There was also a significant difference between assessors in terms of mean MRC-SS with the six-point scale when using the through-range technique (Table  4 ). Bland-Altman analysis demonstrated smaller mean differences and narrower limits of agreement for the four-point scoring system compared to the six-point scoring system as shown in Table  4 . The through-range six-point testing technique had the greatest mean difference at 2.55 points with wider limits of agreement compared to the other three testing combinations (Table  4 ).

The inter-rater reliability for HGD testing between two physical therapists was almost perfect for the overall cohort including both left and right handgrip scores (Table  3 ). The reliability of HGD was slightly lower for males (although not statistically significant) than for females when examining right HGD scores as shown in Table  3 .

Part two – validation and recommendation of a standard diagnostic approach

Based on the findings of part one (reliability), in part two (validity) the isometric technique was adopted . The median [IQR] for MRC-SS was 48 [41 to 53] out of 60 with scores ranging from 10 to 60.

Validation of HGD as a surrogate measure for diagnosis of ICU-AW

The accuracy of HGD scores in the diagnosis of ICU-AW was compared to the traditional MRC six-point score (less than 48 out of 60). The sensitivity and specificity of HGD as a surrogate measure for ICU-AW was high and clinically acceptable overall with similar results for both right and left sides (Table S2 in Additional file 2 ). For females, specificity was poorest (specificity = 0.45 to 0.55), however sensitivity was perfect (sensitivity = 1.0) (Table S2 in Additional file 2 ).

The median [IQR] HGD score for the cohort was 10.5 [0 to 21.5] kg. Based on gender the median [IQR] HGD scores were for females: 0 [0 to 7.3] kg and for males: 20 [10 to 40] kg. Twenty-seven percent (n = 16/60) of patients assessed had a handgrip score of zero, with the majority of those who scored zero being female (n = 14/16, 88%). All but three patients (with grip score of zero) had a clinical diagnosis of ICU-AW based on MRC sum score (less than 48 out of 60). There was no correlation between age and HGD score (rho = 0.131, P  = 0.320). Six individuals scored zero, and had no antigravity strength in their elbow and/or wrist, and all six were classified with severe ICU-AW (less than 36 out of 60) based on the traditional six-point scoring system. Convergent validity was established with a significant large correlation between HGD score and the six-point scoring diagnosis of ICU-AW (rho = 0.86; P <0.001) and awakening PFIT score (rho = 0.56; P <0.001). Significant moderate correlations were identified for HGD and mechanical ventilation hours (rho = −0.30; P  = 0.02); hospital LOS (rho = −0.30; P  = 0.002); discharge PFIT score (rho = 0.38; P  = 0.004) and discharge to home (rho = −0.36; P <0.001).

Validating the four-point scoring system for MMT

A cutoff score of 24 out of 36 was identified for the four-point scoring system (Figure  1 ) using an isometric testing procedure with excellent discriminative ability (area under the ROC curve (95%CI) = 0.92 (0.83 to 1.0) and excellent diagnostic accuracy (sensitivity (95%CI) = 0.84 (0.64 to 0.96); specificity (95%CI) = 1.0 (0.9 to 1.0); positive predictive value (95%CI) = 1.0 (0.84 to 1.0); and negative predictive value (95%CI) = 0.90 (0.76 to 0.98)).

Determining cutoff score for the four-point scoring system from coordinates of the receiver operating curve for highest sensitivity and specificity. The graph on the right is called a receiver operating characteristic curve (ROC curve). It is a plot of the true positive rate (y-axis) against the false positive rate (x-axis) for the different possible cut-points of a diagnostic test. The closer the curve is to the left-hand border and top border of the ROC space the more accurate the test. Accuracy is measured by the area under the curve. An area of 1 = perfect test; an area of 0.5 = inadequate test. The ROC curve analysis resulted in an area under the curve of 0.92 (95%CI 0.83 to 1.0), which is almost perfect and demonstrates excellent diagnostic accuracy. The table on the left outlines each individual plotted cut-point. At 23.5 the sensitivity was 0.84, with specificity of 1.0, and at 25 the sensitivity was 0.96, and specificity was 0.86. A cutoff point of 24 would therefore result in high sensitivity and specificity. 95%CI, ninety-five percent confidence interval.

Convergent validity was identified with a significant large correlation between the four-point scoring system for diagnosis of ICU-AW (less than 24 out of 36) with awakening PFIT score (rho = 0.70; P <0.001) and handgrip strength (rho = 0.66, P <0.001). A significant moderate correlation was identified between the four-point scoring system for diagnosis of ICU-AW (less than 24 out of 36) and mechanical ventilation hours (rho = −0.42; P  = 0.001); ICU LOS (rho = −0.39; P  = 0.01); time to awakening (rho = −0.39; P  = 0.002); hospital LOS (rho = 0.45; P <0.001); and discharge to home (rho = −0.48; P <0.001).

The findings of this study have important implications for clinicians in the diagnosis of ICU-AW using volitional strength testing at the bedside. First, we found that there was excellent inter-rater reliability for overall MRC-SS regardless of testing technique or scoring system utilised. However, there was a significant difference between the use of isometric and through-range techniques. There were also greater mean differences between both assessors when using the through-range technique and the inter-rater agreement for the diagnosis of ICU-AW was less accurate (kappa = 0.26) using the through-range technique compared to the isometric technique (kappa = 0.72). Based on these findings from part one (reliability), the isometric technique is the preferred method for the evaluation of MMT in ICU. In part two (validity) a cutoff score for the four-point scoring system was identified as less than 24 out of 36 for the diagnosis of ICU-AW. Similar to the six-point scoring system, we identified that greater levels of agreement existed for the diagnosis of ICU-AW when using an isometric technique. Therefore, the four-point scoring system may provide greater inter-rater agreement between assessors in the quantification of muscle strength. Our study also demonstrated that HGD is both a highly reliable and valid measurement tool for the screening of ICU-AW with excellent test performance on this external validation.

Manual muscle strength testing is time-consuming and requires expertise and training to administer appropriately [ 14 , 20 ]. In contrast, HGD is a simple quick tool, which can be assessed with limited training in a few minutes to screen for the presence of ICU-AW, and can be incorporated easily into daily assessment practices. Service provision of therapy varies from institution to institution with many facilities not having a designated physical therapist on staff. It is also important to note that not everyone who is in ICU will develop ICU-AW. Therefore, using HGD that is simple and can be performed by any multidisciplinary member is a feasible and valid option for initial ICU-AW screening.

The incidence of ICU-AW varies across different settings but is reported to be around 25 to 50% in the general ICU setting across several studies [ 2 , 10 , 19 ]. The true incidence of ICU-AW has been challenging to elucidate due to the variability and inconsistencies in testing methodology, which have limited the generalisability and comparability of findings between studies. This study, therefore, addresses some of these inconsistencies and provides a standardised approach to the assessment of manual muscle strength testing. This study demonstrated that the isometric technique is superior to through-range measures in terms of agreement between assessors for classifying the presence or absence of ICU-AW regardless of the scoring system used. This may be due to differences in factors such as deceleration, acceleration and changes in the mechanical advantage of the limb during the through-range technique [ 28 ].

The six-point MRC-SS has been used for more than a decade as a diagnostic tool for the identification of ICU-AW [ 2 , 5 ]. Despite its widespread use there are several methodological shortcomings with this scale. Previous research has highlighted that there are greater discrepancies in scoring between assessors at higher grades (greater than Grade 3 - antigravity strength) [ 8 ]. Our study demonstrated that the new collapsed four-point scoring system with a cutoff score of less than 24 out of 36 has both excellent reliability and agreement for the diagnosis of ICU-AW between assessors compared to the six-point scoring system. The validity of the four-point scoring system was also demonstrated with significant correlations to measures of physical function, strength and parameters such as mechanical ventilation time and LOS.

The research into the four-point scale is in its embryonic development. There are two potential advantages of the four-point scale over the traditional six-point scale method. First, the collapsed four-point scale restores weighting between levels based on Rasch analytical principles as described by Vanhouette and colleagues [ 15 , 29 ]. The six-point scoring system and sum score out of 60 are ordinal based and suggest equal weighting at each grade, which is not the case [ 15 , 29 ]. Concern has been raised that this disordered threshold impacts on the accuracy of results [ 15 , 29 ]. A recent study (albeit not specifically in the ICU setting) demonstrated 80% of all muscles examined were incorrectly classified [ 15 ]. The greatest inconsistencies were observed for Grades 2 to 4 [ 15 ]. Within the ICU population, a recent study demonstrated variable agreement for individual muscle groups ranging from 35 to 75% between assessors [ 10 ]. Some studies within the ICU have demonstrated that the greatest challenge was in the differentiation between scores 4 and 5 [ 8 ]. To improve clinical applicability based on Rasch analytical principles and to restore the weighting to scores a four-point score was developed by Vanhouette and colleagues [ 15 , 29 ].

Another possible advantage of the four-point scale is that it could potentially be used by less experienced clinicians as there is less discrimination between grades required. Concerns on the potential subjectivity of the four-point scale have been raised within the literature [ 30 ]. This study presents data for the first time on the reproducibility and potential clinical relevance of the four-point scale for diagnosis of ICU-AW. Whilst our findings are promising, more work needs to be performed to examine the four-point scale before it is recommended/adopted into clinical practice.

For HGD our study demonstrated high sensitivity, specificity and predictive values that were similar to those reported by Ali and colleagues [ 13 ], which indicates that the scores maintained stability in an independent sample validation. A recent study examined HGD in a surgical ICU setting and suggested that handgrip strength had a significant floor effect with 55% of the cohort scoring zero [ 31 ]. Some individuals had a score of zero and had acceptable or normal manual muscle strength test scores [ 31 ]. It is important to note there were differences between studies in terms of screening for alertness and comprehension and also more importantly differences in HGD hold time to allow peak muscle contraction to be reached. Baldwin and colleagues found that critically ill individuals require at least six seconds to generate their peak force [ 22 ], which is twice the length of time that was applied by Lee and colleagues in their study [ 31 ]. Therefore, it is possible in the study by Lee and colleagues that patients were not given sufficient time to reach their peak muscle force levels [ 31 ].

In this study there was no correlation between age and HGD score. Based on the findings of this study and previous research, the handgrip cutoff scores have been shown to be sensitive and able to diagnose the presence of ICU-AW based on gender [ 13 ]. Normative data on handgrip dynamometry are often stratified by gender and also age [ 23 , 32 ]. Whether cutoff scores need to be considered particularly for younger individuals based on age as well as gender could be considered in the future. This may be more important when monitoring patient recovery over time in order to be able to compare to normative age/gender-matched data, rather than for the diagnosis of ICU-AW.

In our study, a floor effect was observed with strength testing using HGD with 30% scoring zero on testing, which is similar to the floor effect reported by Ali and colleagues with 26% of their cohort scoring between zero and five kilograms [ 13 ]. Although there may be a floor effect with HGD testing, the majority of patients who scored zero had a diagnosis of ICU-AW, and perhaps if used as a first tier of screening for the presence of ICU-AW it may inform the therapist that further thorough assessment of manual muscle strength testing is required. In females the sensitivity for HGD was perfect (1.0) and specificity was lower (0.45 to 0.55). A lower specificity means that there are individuals who are diagnosed with ICU-AW based on handgrip scores who on thorough manual muscle strength testing would not be identified with ICU-AW. It would be more of a concern if sensitivity were low, as this would mean that individuals who had ICU-AW would be missed, and this may have significant clinical implications for the management of the patient.

Limitations

The key limitation with this study is within part one (reliability) where there is the possibility of recall bias, as the physical therapist could not be blinded from the results of their previous testing session and that only two assessors were included. In part two (validity), the assessors had a varying range of expertise both as physical therapists and also specifically in terms of practical expertise within the ICU setting, however this may improve the generalisability of the findings. Other limitations include: small sample size and single-centre study design.

There are inherent limitations with volitional muscle strength testing using HGD and MMT. Testing requires patients to be awake and co-operative. The feasibility of strength testing in our study was limited with 15% (n = 42/283) unable to be assessed due to inattention during their ICU stay, which is consistent with the findings of previous studies [ 8 , 10 ]. The median [IQR] time to awakening in our study was 9 [ 5 - 11 ] days. This is consistent with previous studies, which reported a delay of 10 to 18 days [ 8 , 10 ]. These are important limitations of volitional strength testing in general.

Future directions

Studying non-volitional clinical measures in the future, which can easily be evaluated at the bedside on admission to identify those at risk of ICU-AW, is warranted. Whilst modalities such as neuromuscular ultrasound imaging are being investigated [ 33 , 34 ] the current method for screening and diagnosing ICU-AW will continue to be clinical strength testing.

This study is focused on developing a standard simple diagnostic screening approach to identify ICU-AW. It is important to note that there is inconclusive evidence to support the use of MMT or HGD to measure change over time in order to evaluate treatment efficacy or recovery post critical illness with one study suggesting that at least a 50% change in muscle strength score from baseline is required to reflect a true change [ 22 ]. Further research is required to determine what outcome measure/s should be utilised to monitor and measure intervention efficacy across the continuum of ICU recovery. Future research also warrants investigation of strength measures such as HGD and MMT against electrophysiological testing in order to understand further the changes in muscular control and strength generation.

The findings of this study informed the development of a new two-tier approach to screening for the presence of ICU-AW on awakening. The first tier involves assessment of handgrip strength (with a score <11 kg for males and <7 kg for females indicative of ICU-AW). Handgrip dynamometry is quick and easily administered with minimal training by any member of the multidisciplinary team. This is particularly advantageous in units where there may be limited access to physical therapists or other rehabilitation staff, as it will facilitate early identification of individuals who may benefit from therapy. If patients fall below the cutoff levels on HGD testing or are unable to perform HGD (due to lacking antigravity strength in elbow flexors/wrist extensors), a referral to a therapist is warranted to enable a more thorough strength assessment to be conducted. Manual muscle strength testing should then be assessed using an isometric technique. Further research is required to confirm the findings of this study and determine the validity of the four-point scoring system and cutoff score developed (less than 24 out of 36) before recommending adoption into clinical practice.

In the future, a third tier may be warranted where nerve conduction testing is performed to gather further information to identify neuropathy as opposed to myopathy [ 35 ]. Further research into the phenotype of muscle weakness is required. These methods will enable physical therapists and rehabilitation staff to target rehabilitation resources and select appropriate exercise modalities to minimise muscle wasting and improve the longer-term outcomes for the survivors of critical illness who will most benefit.

Key messages

There is a difference between isometric and through-range techniques for assessing manual muscle strength and the isometric technique is the preferred technique for assessing manual muscle strength (with higher accuracy and reliability).

A cutoff score of 24 out of 36 was identified for the four-point scoring system.

Handgrip dynamometry is a valid and reliable surrogate tool for diagnosing ICU-AW.

Two-tier approach to muscle strength testing is recommended (1) handgrip testing and (2) thorough manual muscle strength testing if below the handgrip cutoff levels.

Abbreviations

ninety-five percent confidence interval

Acute Physiology and Chronic Health Evaluation Two

area under the curve

CONsensus-based Standards for the selection of health Measurement INstruments

handgrip dynamometry

intraclass correlation coefficient

intensive care unit

intensive care unit-acquired weakness

interquartile range

length of stay

manual muscle testing

Medical Research Council sum score

Physical Function in Intensive Care test

receiver operating characteristic

standard deviation

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Acknowledgements

The authors would like to acknowledge and thank the staff of the physiotherapy and intensive care departments at Austin Health, Melbourne, Australia, for their ongoing support of the project. Selina M Parry was supported by a National Health and Medical Research Council PhD Scholarship (#103923).

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Sue Berney, Catherine L Granger & Linda Denehy

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SP contributed to the design of the study, was responsible for data analysis and interpretation and drafted, revised and agreed on the final manuscript version for submission. SB, CG, DE, RK, LD contributed to the design of the study, data analysis and interpretation of the results, and contributed to the manuscript revision. LM and DD contributed to data acquisition and manuscript revision. All authors read and approved the final manuscript.

Additional files

Additional file 1: table s1..

Methodology description for through-range and isometric testing.

Additional file 2: Table S2.

Individuals with a diagnosis of ICU-AW as determined by handgrip dynamometry.

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Parry, S.M., Berney, S., Granger, C.L. et al. A new two-tier strength assessment approach to the diagnosis of weakness in intensive care: an observational study. Crit Care 19 , 52 (2015). https://doi.org/10.1186/s13054-015-0780-5

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DOI : https://doi.org/10.1186/s13054-015-0780-5

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Effects of the High-Intensity Early Mobilization on Long-Term Functional Status of Patients with Mechanical Ventilation in the Intensive Care Unit

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Intensive care unit (ICU)-acquired weakness often occurs in patients with invasive mechanical ventilation (IMV). Early active mobility may reduce ICU-acquired weakness, improve functional status, and reduce disability. The aim of this study was to investigate whether high-intensity early mobility improves post-ICU discharge functional status of IMV patients.

132 adult patients in the ICU who were undergoing IMV were randomly assigned into two groups with a ratio of 1 : 1, with one group received high-intensity early mobility (intervention group, IG), while the other group received conventional treatment (control group, CG). The functional status (Barthel Index (BI)), capacity of mobility (Perme score and ICU Mobility Scale (IMS)), muscle strength (Medical Research Council sum scores (MRC-SS)), mortality, complication, length of ICU stay, and duration of IMV were evaluated at ICU discharge or after 3-month of ICU discharge.

The patient's functional status was improved (BI scores 90.6 ± 18.0 in IG vs. 77.7 ± 27.9 in CG; p =0.005), and capacity of mobility was increased (Perme score 17.6 ± 7.1 in IG vs. 12.2 ± 8.5 in CG, p < 0.001; IMS 4.7 ± 2.6 in IG vs. 3.0 ± 2.6 in CG, p < 0.001). The IG had a higher muscle strength and lower incidence of ICU-acquired weakness (ICUAW) than that in the CG. The incidence of mortality and delirium was also lower than CG at ICU discharge. However, there were no differences in terms of length of ICU stay, duration of IMV, ventilator-associated pneumonia, and venous thrombosis.

Conclusions

High-intensity early mobility improved the patient's functional status and increased capacity of mobility with IMV. The benefits to functional status remained after 3 month of ICU discharge. Other benefits included higher muscle strength, lower incidence of ICUAW, mortality, and delirium in IG.

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Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

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Early physical and occupational therapy in mechanically ventilated, critically ill patients: a randomised controlled trial, author and article information , contributors, affiliations, author notes.

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Does the Score on the MRC Strength Scale Reflect Instrumented Measures of Maximal Torque and Muscle Activity in Post-Stroke Survivors?

Pawel kiper.

1 Physical Medicine and Rehabilitation Unit, Azienda ULSS 3 Serenissima, 30126 Venice, Italy

Daniele Rimini

2 Medical Physics Department-Clinical Engineering, Salford Care Organisation, Salford M6 8HD, UK; [email protected]

Deborah Falla

3 Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham B15 2TT, UK; [email protected]

Alfonc Baba

4 Rehabilitation Unit, Azienda Ospedale Università Padova, 35128 Padua, Italy; [email protected]

Sebastian Rutkowski

5 Faculty of Physical Education and Physiotherapy, Opole University of Technology, 45-758 Opole, Poland; [email protected]

Lorenza Maistrello

6 Laboratory of Neurorehabilitation Technologies, San Camillo IRCCS, 30126 Venice, Italy; [email protected]

Andrea Turolla

Associated data.

The data presented in this study are available on request from the corresponding author.

It remains unknown whether variation of scores on the Medical Research Council (MRC) scale for muscle strength is associated with operator-independent techniques: dynamometry and surface electromyography (sEMG). This study aimed to evaluate whether the scores of the MRC strength scale are associated with instrumented measures of torque and muscle activity in post-stroke survivors with severe hemiparesis both before and after an intervention. Patients affected by a first ischemic or hemorrhagic stroke within 6 months before enrollment and with complete paresis were included in the study. The pre- and post-treatment assessments included the MRC strength scale, sEMG, and dynamometry assessment of the triceps brachii (TB) and biceps brachii (BB) as measures of maximal elbow extension and flexion torque, respectively. Proprioceptive-based training was used as a treatment model, which consisted of multidirectional exercises with verbal feedback. Each treatment session lasted 1 h/day, 5 days a week for a total 15 sessions. Nineteen individuals with stroke participated in the study. A significant correlation between outcome measures for the BB (MRC and sEMG p = 0.0177, ρ = 0.601; MRC and torque p = 0.0001, ρ = 0.867) and TB (MRC and sEMG p = 0.0026, ρ = 0.717; MRC and torque p = 0.0001, ρ = 0.873) were observed post intervention. Regression models revealed a relationship between the MRC score and sEMG and torque measures for both the TB and BB. The results confirmed that variation on the MRC strength scale is associated with variation in sEMG and torque measures, especially post intervention. The regression model showed a causal relationship between MRC scale scores, sEMG, and torque assessments.

1. Introduction

The primary aim of the post-stroke rehabilitation process is to restore and maintain the patient’s ability to perform actives of daily living. Importantly, this process starts within the first days after stroke and often continues over many years [ 1 ]. Because one of the most evident consequences of a cerebrovascular injury is hemiparesis, the rehabilitation process requires accurate assessment of residual muscle activity to define rehabilitative requirements [ 2 , 3 ]. Hemiparesis is associated with muscle weaknesses and inability to produce adequate muscle force for task execution. The ability to generate muscle force is determined by neural, muscular, and biomechanical factors. The contraction that is generated following depolarization of the nervous cells, which release calcium ions, giving start to the process of contraction in the main body of the muscle fiber [ 4 ], can be evaluated either in terms of muscular activity or the resultant muscular force.

The status or change of a clinical condition is traditionally assessed by completing assessment scales. The validity and reliability of these assessment scales are essential to monitor a patient’s recovery and are critical for determining appropriate therapies. A common and widely accepted assessment scale for muscle strength is the Medical Research Council (MRC) scale [ 5 , 6 ]. This scale is commonly used to evaluate patients with stroke suffering from muscle weakness due to hemiplegia [ 7 ]. The MRC scale for muscle strength uses manual muscle testing to grade muscle strength, ranging from 0 to 5, according to the maximum force expected for a certain muscle [ 6 ]. The grades are as follows: 0 = No contraction, 1 = Flicker or trace contraction, 2 = Active movement, with gravity eliminated, 3 = Active movement against gravity, 4 = Active movement against gravity and resistance, and 5 = Normal power. A modified version of the scale takes into account the evaluation of range of movement (ROM), and the grades are as follows: 0 = No contraction, 1 = Flicker or trace contraction, 2 = Active movement, with gravity eliminated, 2–3 = Active movement against gravity over less than 50% of the feasible ROM, 3 = Active movement against gravity over more than 50% of the feasible ROM, 3–4 = Active movement against resistance over less than 50% of the feasible ROM, 4 = Active movement against resistance over more than 50% of the feasible ROM, 4–5 = Active movement against strong resistance over the feasible ROM, but distinctly weaker than the contralateral side, 5 = Normal power. The reliability of the MRC scale and its modified versions (mMRC) have been investigated [ 8 ]. Substantial inter-rater reliability of the MRC scale and the mMRC scale as well as intra-rater reliability of the MRC and the mMRC was observed for forearm muscle evaluation [ 8 ]. Additionally, the validity of the MRC scale has been confirmed, thus supporting its use in clinical practice. Jepsen et al. examined the inter-rater reliability of manual muscle tests of maximal voluntary strength and observed that reduced strength was significantly associated with the presence of symptoms; they suggested that manual muscle testing in upper limb disorders has diagnostic potential [ 9 ]. However, some limitations exist relating to the interpretation of the MRC scale, for example, the width of the MRC grades are unequal [ 10 ]. For instance, when testing elbow flexion strength, the MRC grades overlap between grades 3 and 4, indicating that the MRC grading may be unreliable in quantifying elbow flexion strength. Furthermore, excluding patients with either grade 0 or grade 5 decreases the reliability of the MRC Scale [ 8 ]. This may indicate that the assessment of the different grades of impairment can be more difficult than the assessment of a muscle that has no contraction at all or is evaluated with its maximal strength. An additional consideration is that Dupépé et al. suggested that the inter-observer reliability of the mMRC scale has discrepancy among trained observers. Additionally, the reliability of the MRC scale varies depending on whether lower-extremity or upper-extremity muscle groups are tested [ 11 , 12 ]. Importantly, however, it remains unknown whether variation of MRC scale scores is associated with a similar variation obtained with operator-independent techniques such as strength measures obtained with dynamometry and quantitative measures of muscle activity such as surface electromyography (sEMG).

Muscle force can be directly measured with a dynamometer, an electromechanical device that can be used to measure muscular strength of a maximum isometric contraction for most major joints in the human body [ 13 , 14 ]. Baschung Pfister et al. evaluated the reliability and validity of manual muscle testing and hand-held dynamometry (HHD) by measuring maximum isometric strength in eight muscle groups across three measurement points. The correlation between the total score on manual muscle testing and HHD was not satisfactory and raised doubt as to whether manual muscle testing measures the same construct (i.e., isometric strength) as HHD [ 15 ]. The total score from manual muscle testing was considered reliable and a time-efficient assessment to consider for the detection of general muscle weakness but not for single muscle groups. On the contrary, HHD could be recommended to evaluate isometric muscle strength of single muscle groups [ 15 ]. A study by Aguiar et al. revealed that dynamometry provided adequate inter- and intra-rater reliability when used in the subacute phase of stroke [ 16 ]. Additionally, recent studies evidenced the utility and the reliability of dynamometry to evaluate force of the paretic side of post-stroke patients [ 17 ]. The available literature reports that HHD is an efficient, objective, sensitive, and affordable alternative for strength quantitation [ 18 ].

The sEMG is a non-invasive technique for recording the electrical signal generated by muscular activity [ 19 ]. Decoding and extracting information contained in this signal provides information on neuromuscular function, which is not provided by other assessment techniques in neurorehabilitation [ 20 ]. This data can enhance the characterization of neuromuscular impairments, while tracking the changes in muscle activity from baseline when neurorehabilitation interventions are administered. Clinically, sEMG is frequently used to obtain a precise and objective measure of muscle activity during motor performance [ 21 , 22 , 23 , 24 , 25 ]. EMG is useful to assess hyperactivity and inactivity of selected muscles [ 26 ] and, given that it can be used to evaluate the integrity of neuromuscular system, it is often adopted as a physiological biofeedback in physical therapy [ 27 ]. In recent decades, the limitations of analyzing EMG have emerged, including physiologically confounding factors [ 28 ]. For this reason, pattern recognition techniques have been widely adopted to classify hand gesture [ 29 , 30 , 31 ], gait analysis [ 32 ], and upper limb prosthesis control [ 33 , 34 , 35 ]. The importance of integrating kinematics and kinetics has also been highlighted [ 36 ]. The generation of muscular force assessed by the MRC scale has been associated with the electrical signal observed via sEMG recordings [ 37 ]. Furthermore, some mathematical models of motor unit with a parameterization of the electrical and mechanical components of the model were proposed. These models can highlight a physiologically meaningful EMG–force relation in the simulated signals [ 38 ]. However, the relationship between muscular force and sEMG during voluntary contractions in pathological conditions (e.g., central nervous system injury) is still poorly understood [ 39 , 40 ].

Thus, in this study we examined whether the scores of the MRC scale are associated with instrumented measures of muscular force and muscle activity pre- and post- an intervention for severe hemiparesis in post-stroke survivors.

Proprioceptive-based training (PBT) was used as a treatment model; PBT is a neuromodulatory treatment modality that has been proposed for the treatment of the upper limb to recover voluntary muscle contraction and strength in stroke survivors [ 41 ].

2. Materials and Methods

2.1. setting and participants.

This study was conducted in the neurorehabilitation hospital and research institute of San Camillo IRCCS (Venice, Italy). Inpatients affected by first ischemic or hemorrhagic stroke within 6 months before enrollment in the study and with an MRC score at baseline between 0 and 1 point for their biceps brachii and triceps brachii were included in this study. The presence of hypertonia, apraxia, global sensory aphasia, neglect, cognitive impairments, severe sensitivity disorders, stroke lesion located in the cerebellum, or refusal to participate resulted in exclusion from the study.

The local Ethics Committee of the IRCCS San Camillo Hospital approved this study (Protocollo 2012.07 BAT v.1.2), which was registered on ClinicalTrials.gov (NCT03155399). Informed, written consent was obtained prior to participation in the study.

2.2. Outcome Measures

The MRC scale for muscle strength, dynamometry measures of maximal elbow flexion and extension torque, and sEMG measures of biceps brachii and triceps brachii activity were implemented pre- and post-intervention. The positions of the upper extremity for dynamometry measurements and for the MRC scale assessment were the same. An elbow splint was used to standardize the position of the patient’s arm during sEMG signal acquisition; the elbow joint was fixed to 40° for assessment of the biceps brachii and 90° for the triceps brachii.

2.2.1. MRC Scale for Muscle Strength

Testing was performed by a physiotherapist after assessment of elbow range of motion. The physiotherapist ensured that the wrist flexors were not contracted when assessing biceps brachii and provided stabilization support with a hand placed above the patient’s elbow when assessing triceps brachii. All patients firstly underwent an assessment of the biceps brachii followed by assessment of the triceps brachii. The assessment of biceps brachii was performed in the supine (or in sitting in the case of grade 2 or more) position with the forearm supinated and elbow flexed to approximately 45 degrees as the patient was asked to “bend your elbow” ( Figure 1 ). The assessment of triceps brachii was performed in the sitting (or in prone in the case of grade 3 or more) position with the arm supported at 90 degrees of shoulder and elbow flexion [ 5 , 42 ] as the patient was asked to “straighten your arm”. For both assessments, the patients performed three attempts and the best result was considered the outcome.

Visualization of the outcome measures applied. sEMG = surface electromyography. MVC = maximal voluntary contraction. MRC Scale = Medical Research Council Scale.

2.2.2. Dynamometry

An electrical dynamometer (CITEC Hand-Held Dynamometer) was used for testing. The participant’s positions for assessment of maximal elbow flexion and extension torque were adopted from the MRC scale evaluation. The biceps brachii was assessed first in all participants. Patients were asked to perform three attempts with verbal encouragement to exceed the previous score and the mean value was considered for analysis.

2.2.3. Surface Electromyography

The sEMG was acquired with bipolar electrodes from the long head of the biceps brachii and the lateral head of the triceps brachii, according to published guidelines for electrode placement [ 43 ] after skin preparation. The sEMG signal was amplified with a gain of 1000, band-pass filtered (fifth-order Butterworth filter, bandwidth 10–500 Hz), and sampled at 2048 Hz using a multichannel EMG amplifier (EMG-USB2+ OT Bioelettronica SRL, Torino, Italy). The reference electrode was placed around the wrist of the tested arm. Each linear envelope of EMG activity was obtained by full-wave rectifying and then low-pass filtering (Fc = 6 Hz) for each sEMG channel.

The sEMG was acquired during maximal voluntary isometric contraction (MVC) of elbow flexion and extension, each repeated three times. The sEMG was recorded with the following procedure: recording of baseline activity at a resting state followed by the task itself (i.e., elbow flexion or extension MVC) recorded for 2 s each. The peak values of the amplitude of the envelopes of the sEMG of baseline and during the MVC were extracted, and the difference was computed. The mean value from the three repetitions was considered for further analysis.

2.3. Intervention

Participants underwent PBT, which consisted of multidirectional exercises executed synchronously with the unaffected limb and verbal feedback. Patients were asked to move both upper limbs synchronously performing bilateral flexion-extension at the level of their elbow joint. The PBT therapeutic session was divided into the following repetitive phases: proprioceptive stimulation for 3 min with a rest of at least 2 min between stimulations and repeated at least three times for each muscle. Additionally, all participants received individual exercises (passive, active-assisted, or active) for postural control in sitting or standing position. The training protocol lasted 1 h a day, 5 days weekly for a total of 15 sessions [ 41 ].

2.4. Statistical Analysis

Data distribution for all the variables was tested through the Shapiro–Wilk test. The Spearman’s rank correlation test was used to study potential associations between the MRC scale score and measures of elbow flexion and extension strength and sEMG amplitude of the biceps brachii and triceps brachii muscles both pre- and post-intervention and on the change scores (before–after intervention). A regression model was implemented on the post intervention data to verify the relationship between the MRC strength scale scores and dynamometry measurements of elbow flexion and extension strength and sEMG amplitude of the biceps brachii and triceps brachii. We assessed the MRC models fitting as follows: the overall significance of the regression model with the percentage of variance explained (% Variance explained); the variance of the residuals (Residuals vs. Fitted plot); the normality of the residual distribution (Shapiro–Wilk normality test and Normal QQ-Plot); the presence of outliers (Residuals vs. Leverage plot). Bland–Altman graphs were reported to evaluate the agreement between the measurements made with MRC and those made with sEMG and dynamometry. The statistical significance level was set at p < 0.05. All calculations were performed using R Statistical Computing software.

Data from 19 patients with a mean age of 61.48 ± 12.77 years (10 female and 9 male) were analyzed in this study. Patients’ mean time from stroke onset was 3.19 ± 1.80 months. Twelve patients had ischemic stroke and seven had a hemorrhagic stroke (8 right and 11 left lesion side). Descriptive characteristics of the parameters measured before and after intervention are presented in Table 1 .

Pre- and post-intervention values.

Values are expressed as mean ± standard deviation (SD); sEMG, surface electromyography; MRC, Medical Research Council scale; No, points; N, newtons; mV, millivolts.

A statistically significant relationship between the outcome measurements was observed pre-intervention between the MRC scale score and dynamometry measures (biceps brachii p = 0.0000; triceps brachii p = 0.0002) ( Table 2 ), whereas, post-intervention, the MRC scale score was significantly associated with measures of sEMG and dynamometry measures for both biceps brachii (i.e., MRC and sEMG p = 0.0177; MRC and Dynamometry p = 0.0001) and triceps brachii (i.e., MRC and sEMG p = 0.0026; MRC and Dynamometry p = 0.0001) ( Table 2 ).

Correlation between the MRC scale score and sEMG amplitude and Dynamometry measures.

ρ, correlation coefficient; *, p -value < 0.05; sEMG, surface electromyography; MRC, medical research council scale; Spearman’s rank correlation Test; A, MRC biceps brachii; B, MRC triceps brachii.

A generalized regression model was used to study the relationship between the MRC scale scores, sEMG amplitude, and dynamometry measures of maximal elbow flexion and extension torque. The regression model showed that an increase of muscular strength by one point on the MRC scale was related to an increase of 59 mV (millivolts) of biceps brachii sEMG amplitude (% of explained variance = 0.50, Figure 2 ) and 83 mV for the triceps brachii sEMG amplitude (% of explained variance = 0.31, Figure 3 ). Moreover, the results revealed that a one-point increase on the MRC scale evaluation corresponded to an increase of 20 N (newtons) of elbow flexion torque measured with dynamometry (% of explained variance = 0.70, Figure 4 ) and 24 N of elbow extension torque (% of explained variance = 0.76, Figure 5 ).

Graphical residual analysis for the MRC model and sEMG for the biceps brachii muscle. The plot on the left shows the residual errors versus their estimated values and the points on the graph should be arranged randomly. The QQ plot in the center shows the distributive normality of the residuals and the points on the plot should follow the diagonal line. The plot on the right identifies any influential data points on the model. In the plot, the Leverage’s values of the points and the Cook’s distances that measure the influence of each observation on the estimation of the model parameters are present. Cook’s distance values greater than 1 are suspect and indicate the presence of a possible outlier or poor model.

Graphical analysis of residuals for the MRC and sEMG model for Triceps Brachii muscle. The plot on the left shows the residual errors versus their estimated values; the QQ-plot in the center shows the distributive normality of the residuals; the plot on the right identifies any influential data points on the model.

Graphical analysis of residuals for the MRC and Dynamometry model for Biceps Brachii muscle. The plot on the left shows the residual errors versus their estimated values; the QQ-plot in the center shows the distributive normality of the residuals; the plot on the right identifies any influential data points on the model.

Graphical analysis of residuals for the MRC and Dynamometry model for Triceps Brachii muscle. The plot on the left shows the residual errors versus their estimated values; the QQ-plot in the center shows the distributive normality of the residuals; the plot on the right identifies any influential data points on the model.

The goodness of fit of the first and second models showed normal distribution of residuals, whereas the goodness of fit of the third and fourth models showed non-normal distribution ( Table 3 ). The QQ Plot ( Figure 2 and Figure 3 ) and the Shapiro–Wilk normality test, performed on the two MRC biceps brachii models, confirmed the hypothesis of normality, both for the residuals of the model estimated with sEMG (W = 0.96 p = 0.77) and for those estimated with dynamometry (W = 0.93 p = 0.17). On the other hand, the goodness of fit carried out on the models of MRC triceps brachii did not have a normal distribution of residuals for the model estimated with sEMG (W = 0.83 p = 0.009) or for the model estimated with dynamometry (W = 0.89 p = 0.03). In all the Residuals versus Fitted graphs ( Figure 2 , Figure 3 , Figure 4 and Figure 5 ), the points on the graph were random and did not show any evident pattern, a sign that there was no residual systematic dependence not identified from the estimated model. Some Residuals versus Leverage plots ( Figure 3 , Figure 4 and Figure 5 ) highlighted the presence of observations that could be considered outliers (they exceed the dotted line of Cook’s distance) and had an influence on the model estimation as the high Leverage values suggested. Furthermore, the Bland–Altman plots highlight the presence of a linear decreasing dependence, thus excluding the presence of significant bias. These results showed that applied assessment tools (i.e., MRC, Dynamometer, EMG) were comparable; however, differences were also present ( Figure 6 and Figure 7 ).

Differences between MRC scale and sEMG ( A ) as well as MRC and Dynamometry ( B ) for Biceps Brachii muscle versus the mean of the two measurements. The central line represents the mean difference (bias A = −2.16; B = −2.16), while the top and bottom lines represent the relative 95% CI (A = −4.93; 0.62) (B = −2.82; −1.50). The agreement between the measures is good when the differences are randomly distributed and fall within the 95% CI. The Bland–Altman plots highlight the differences of measurements performed with the two instruments. When the points (representing the observations) are scattered within the CI, the instruments can be used interchangeably. This mean that there are no significant differences between the measurements obtained from both instruments.

Differences between MRC scale and sEMG ( A ) as well as MRC and Dynamometry ( B ) for Triceps Brachii muscle versus the mean of the two measurements. The central line represents the mean difference (bias A = −1.60; B = −2.22), while the top and bottom lines represent the relative 95% CI (A = −5.23; 1.85) (B = −2.95; −1.49). The Bland–Altman plots highlight the differences of measurements performed with the two instruments. When the points (representing the observations) are scattered within the CI, the instruments can be used interchangeably. This mean that there are no significant differences between the measurements obtained from both instruments.

Relationship between the MRC scale scores, sEMG amplitude, and Dynamometry.

The outcomes are displayed with equation of the regression models. The model was estimated on post-intervention data. The Normality test was applied on model’s residuals and significance was established at p < 0.05 *.

4. Discussion

The results of this study showed that for the post-intervention data, muscular strength measured by the MRC scale was correlated to both the amplitude of muscle activity measured by sEMG, as well as measures of maximal voluntary torque assessed with dynamometry. Thus, the clinical measure of muscle strength in patients with hemiparesis increased in accordance with the changes observed in sEMG and dynamometry measures. We also observed that the MRC scale score and dynamometry measures were correlated when examining the pre-intervention data, but a similar correlation was not present between the MRC scale score and sEMG measures pre-intervention. This is likely due to the severe weaknesses of the tested muscles pre-intervention, which mostly did not present an active voluntary contraction.

In a study by Deroide et al., which investigated patients with neuropathic conditions, the EMG at baseline and during a MVC were weakly but significantly correlated to the MRC score [ 44 ]. Considering that a possible association between EMG and muscular force may help in the assessment of various aspects of muscle physiology, the addition of sEMG measurements for the evaluation of changes in muscle function following an intervention could support the interpretation of the MRC scale scores. This may also help to overcome some of the limitations of the MRC scale; for example, the original MRC scale does not include ROM. Consider an example of a person in the acute phase after stroke who can flex his/herelbow to 30° and after 1month of rehabilitation can flex to 70°. An improvement of 40° of flexion ROM is likely to be functionally significant; however, in both assessments (i.e., baseline and after rehabilitation), a grade 2 in the MRC scale will likely be obtained. Indeed, other authors have suggested that the MRC grading system should not be the sole outcome evaluation for elbow flexion strength, and quantitative measurements, such as using a dynamometer, should be included for outcome comparisons [ 45 ]. The results of our study suggest that inter-instrumental variation in muscle strength assessment can partly supplement the MRC scale outcome.

Direct measurement of muscle force using sEMG is not possible, and, although some studies have reported a linear relationship between force and sEMG amplitude [ 46 ], several others suggest a non-linear relationship [ 47 ]. In the current study, the regression model showed a linear relationship between the MRC scale score and sEMG amplitude as well as between the MRC scale score and dynamometry measures of elbow extension/flexion torque for triceps brachii and biceps brachii.The score on the MRC scale linearly increased with the amplitude observed during the sEMG acquisition and dynamometry assessment. This relationship supports the comparative outcome between the MRC scale and an instrumented assessment of muscle activity/torque during maximum voluntary contractions. Our findings provide new insights into the relationship between the measurements described above applied to plegic muscles resulting from central nervous system injury. This relationship does not explain exactly how much the muscle has recovered, but, from a clinical perspective, it can confirm the appropriateness of the interpretation of the applied MRC test. Despite the wide use of the MRC scale for strength assessment, this tool has been reported as not sufficiently sensitive and with limited accuracy to detect changes. Our results suggest that sEMG can be implemented for accurate assessment of post-stroke individuals when muscular force is evaluated. Thus, this may offer a more precise prediction of functional capabilities in patients with upper limb hemiparesis. The introduction of sEMG assessment can more easily detect and confirm muscle activity and/or residual force. This can be also helpful as a potential predictor of muscle force recovery. Collectively, our findings support the use of the MRC scale to evaluate changes in muscle strength and activity of the biceps and triceps brachii following rehabilitation in patients with severe hemiparesis.

There are some methodological considerations to note when interpreting the findings of this study. Firstly, we enrolled post-stroke patients with severe upper limb hemiparesis and, consequently, the presence of muscular fatigue and hypo-tone introduced non-linear distortions to the force–sEMG relationship, which may have limited this study [ 48 , 49 ]. A further consideration is that the catchment area of the electrode didnot extend sufficiently to detect the signal generated across the entire muscle volume.

Considering that the inter-rater reproducibility of the MRC scale had several limitations [ 50 ], future studies should also consider the correlation between MRC scores and instrumental assessments when data are collected from more than one assessor. Moreover, the residuals did not have a normal distribution for the triceps brachii and this may have been due to the small sample size of this study. Therefore, analysis of a larger sample and strength assessment of several muscle groups with both sEMG and dynamometry would provide a better understanding of the relationship between different methods of strength assessment and functional tasks. Another limitation is that we did not consider the potential effects of agonist–antagonist activation, which could have influenced the measures.

5. Conclusions

Variation in scores on the MRC scale was associated with variation in electromyographic activity as well as elbow torque measured with dynamometry. The findings of this study can be used to ensure more precise clinical assessments of patients with stroke.

Acknowledgments

Authors would like to acknowledge Aneta Kiper, Simonetta Rossi, and Michela Agostini, for their support and assistance throughout the study.

Author Contributions

Conceptualization, P.K.; methodology, P.K. and A.B.; validation, L.M., D.F. and D.R.; formal analysis, P.K., L.M., A.B., A.T., D.F., D.R. and S.R.; investigation, P.K. and A.B.; resources, A.T.; data curation, P.K., L.M. and A.B.; writing—original draft preparation, P.K., D.R., L.M. and D.F.; writing—review and editing, D.F., A.T., A.B., P.K. and S.R.; visualization, D.R. and S.R.; supervision, A.T. and D.F.; project administration, P.K. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, approved by Ethical Committee of the IRCCS San Camillo Hospital (Protocollo 2012.07 BAT v.1.2), and registered on ClinicalTrials.gov (NCT03155399).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Objective Tests and Measures: Strength Assessment and Treatment Guidance

Muscle strength decline is associated with normal aging and may be accelerated by illness, disease, or injury. Decreased physical activity resulting from COVID-19 mitigation efforts is exacerbating the incidence of muscle disuse atrophy in older adults. Strength declines may lead to functional and mobility deficits with increased risk of falls, injury, or death. According to the CDC, each year three million adults are treated in emergency departments for injuries related to falls. By 2030 fall deaths are anticipated to rise to seven per hour. Accurate assessment of strength using validated tests and objective measures is crucial to creating an appropriate treatment plan and achieving outcomes that reduce fall risk.

Assessing strength with objective tests and measures:

Overall Strength: • Medical Research Council Sum Score (MRC-SS): Measures strength bilaterally for the shoulder abductors, elbow flexors, wrist extensors, hip flexors, and foot dorsiflexors. MMT strength grades (0-5) bilaterally for the six muscle groups are added; maximum score is 60. (Vanpee et al., 2014)

Upper Extremity Strength: • Dynamometer: Grip strength is assessed using a handheld dynamometer with the individual seated, shoulder adducted, elbow to 90º, forearm in neutral, and wrist between 0-15º ulnar deviation. (Shirley Ryan, 2020) • Arm Curl Test: 30-second timed test with the individual sitting upright in an armless chair. The individual performs arm curls with weights (5# for women and 8# for men) and the number of repetitions are counted and compared to norms. (Rikli and Jones, 2013)

Lower Extremity Strength: • 30-second Sit-to-Stand: Number of full sit to stand repetitions performed in 30 seconds. (Rikli & Jones, 2013) • 5x Sit-to Stand: Individual performs five repetitions of sit to stand as quickly as possible. (Shirley Ryan, 2020)

Research related to strength improvements:

• An intervention using balance exercises or elastic resistance exercise is effective at improving the muscle strength and balance of old-old elderly. (Cho et al., 2014) • Patterned electrical neuromuscular stimulation (PENS) applied to the quadriceps and hamstrings muscles of patients with COPD resulted in 30% increased strength and 34% increased walking distance. (Bourjeily et al., 2002) • Electrical stimulation, similar to physical exercise, attenuates the functional decline associated with aging, improving muscle strength and mass, maintaining overall size of muscle fibers (decreasing during aging), activating satellite cells, and guaranteeing muscle adaptation. (Kern et al, 2014)

Upper Extremity Strength Treatments (progressive resistance exercise (PRE), PENS, cycling with or without e-stim, and MFAC) provided within the context of a comprehensive clinical treatment pathway may assist in the improvement of strength and function.

Lower Extremity Strength Treatments (PRE, PENS, cycling with or without e-stim, and MFAC) provided within the context of a comprehensive clinical treatment pathway may assist in the improvement of strength and function.

References: Bourjeily-Habr, G., Rochester, C. L., Palermo, F., Snyder, P., & Mohsenin, V. (2002). Randomized controlled trial of transcutaneous electrical nerve stimulation of the lower extremities in patients with chronic obstructive pulmonary disease. Thorax, 57(2), 1045-1049. Centers for Disease Control and Prevention (CDC). (n.d.) STEADI – Older Adult Fall Prevention. Retrieved September 18, 2020, https://www.cdc.gov/steadi Cho, S. I., & An, D. H. (2014). Effects of a Fall Prevention Exercise Program on Muscle Strength and Balance of the Old-old Elderly. Journal of Physical Therapy Science. https://doi.org/10.1589/jpts.26.1771 Kern, H., Barberi, L., Löfler, S., Sbardella, S., Burggraf, S., Fruhamann, H., Carraro, U., Mosole, S., Sarabon, N., Vogelauer, M., Mayer, W., Krenn, M., Cvecka, J., Romanello, V., Pietrangelo, L., Protasi, F., Sandri, M., Zampieri, S., & Musaro, A., (2014). Electrical Stimulation counteracts muscle decline in seniors. Frontiers in Aging Neuroscience. 189(6),1-11. Rikli, R. E., & Jones C. J. (2013). Senior Fitness Test Manual. Champaign, IL: Human Kinetics. Shirley Ryan Ability Lab. (n.d.) Rehabilitation Measures Database. Retrieved September 18, 2018, https://www.sralab.org/rehabilitation-measures Vanpee, G., Hermans, G., Segers, J., & Gosselink, R. (2014). Assessment of limb muscle strength in critically ill patients: a systematic review. Critical Care Medicine. https://doi.org/10.1097/CCM.0000000000000030

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• Published: 16 August 2023

Linking Intensive Care Unit functional scales to the International Classification of Functioning: proposal of a new assessment approach

• Juliana S. F. dos Santos 1 ,
• Gabriely A. G. Silva 2 ,
• Nubia M. F. V. Lima 1 ,
• Lucien P. Gualdi 1 ,
• Diego de S. Dantas 3 &
• Íllia N. D. F. Lima 1  

BMC Health Services Research volume  23 , Article number:  871 ( 2023 ) Cite this article

1103 Accesses

Metrics details

There are several tools to assess functional and physical status in critical ill patients. These tools can guide rehabilitation strategies in Intensive care units (ICU). However, they are not standardized, and this can compromise their applicability. The aim of the study is to identify common contents between International Classification of Functioning, Disability and Health (ICF) and Medical Research Council sum score (MRC-ss), Functional Status Score for the ICU (FSS-ICU), and Physical Function in ICU Test-scored (PFIT-s). As well as to propose a new assessment approach based on the ICF to ICU patients.

Pilot cross-sectional study. ICU in-patients, both genders, aged between 50 and 75 years were assessed with MRC-ss, FSS-ICU, PFIT-s and the linking rules used were proposed by Cieza et al. The inter-rater agreement for the linking process was performed using the Kappa coefficient.

The ICF categories identified in the tools covered a total of 14 items. Common contents were identified in 13 of the 14 and two were related to body functions, six to body structures and five to activities and participation. The inter-rater agreement was considered substantial for the linking of MRC-ss (k = 0.665) and PFIT-s (k = 0.749) to the ICF, and almost perfect for the FSS-ICU (k = 0.832).

Conclusions

This study synthesizes and categorizes commonly used tools and presents a new proposal based on the ICF to guide future studies. The proposed model combines the ICF with the contents of the most relevant instruments used in critical care.

Peer Review reports

Patients admitted to Intensive care units (ICU) are often exposed to bed restriction or immobilism. Extended duration in this condition can lead to ICU acquired weakness (ICU-AW), inflammatory response syndrome (SIRS), multiorgan dysfunction, increased risk of transitory or permanent impairment in physical function or functionality, and development of comorbidities or sequelae that may last for up to five years after hospital discharge [ 1 , 2 ]. Thus, assessing functional capacity in these patients is crucial because it can guide rehabilitation strategies and reduce length of hospital stay, morbidity, mortality and healthcare costs [ 3 ].

Assessment tools are constantly created to objectively measure functionality. Such measurement can be, for example, the ability to maintain and/or recover the performance of basic tasks. These tools also make it possible to quantify the evolution of the functional state and the response to treatments during hospitalization [ 4 , 5 , 6 , 7 ]. Currently, there are at least thirty-three assessment tools for the ICU scenario. Of these, only 20 have clinometric properties and six were created specifically to assess functionality in critically ill patients [ 8 ].

Despite the large number of instruments, their applicability is still controversial. In this context, the possibility of producing standardized and comparable data led international efforts to create the International Classification of Functioning, Disability and Health (ICF). It is a conceptual framework that comprises a biopsychosocial and holistic context and has a common language to describe and classify health and disability [ 9 , 10 ]. However, due to its huge extension (more than 1400 categories), strategies have been adopted to simplify the ICF use in different contexts and conditions [ 10 ]. In addition to the creation of core-sets [ 11 , 12 , 13 , 14 , 15 ] and questionnaires based on the ICF [ 15 ], the identification of ICF contents in different instruments has been a widespread alternative in the scientific community since it allows data interpretation within the ICF classification [ 16 , 17 , 18 ].

Shortlists of ICF categories and linkage of common instruments used by intensive care professionals to the ICF may encourage its use in these settings. This alternative may save healthcare providers’ time and improve assessment in different hospitalization moments, as well as between different centers. All this facilitates the understanding and the management of functionality in critically ill patients. Furthermore, ICF qualifiers can produce feasible indicators and enable reliable results among different centers [ 9 , 15 ].

Considering this, the present study aimed to identify common contents between the ICF and the Medical Research Council sum score (MRC-ss), the Functional Status Score for the ICU (FSS-ICU) and the Physical Function in ICU Test-scored (PFIT-s), using the ICF linkage rules proposed by Cieza et al. [ 16 , 17 , 18 ]. In addition, critically care patients were assessed and physical function was described using a new proposal based on the ICF.

The study was carried out between July and October 2019 at Hospital Regional Dr. Mariano Coelho (Brazil). The research was approved by the Research Ethics Committee of the Federal University of Rio Grande do Norte (CAAE 49235715.3.0000.5568) and conducted in accordance with Resolution 466/12 of the National Health Council and the Declaration of Helsinki of the World Medical Association. All participants signed a consent form.

This is a pilot cross-sectional study with non-probabilistic sampling. In-patients both genders aged between 50 and 75 years, who were able to understand and obey commands, and with controlled blood pressure (i.e., systolic blood pressure between 90mmHg and 180mmHg) were included [ 19 ]. Patients with previous history of hospitalizations for more than seven days, with neurological signs or diseases (e.g., stroke, Parkinson’s disease, spinal cord injury or spasticity), with orthopedic disabilities that could interfere in the assessments (e.g., amputations, leg length discrepancy, immobilizations or external fixators), or with cognitive impairments were excluded from the study.

All participants were assessed with the three instruments proposed, with a total average duration of 60 min. Each participant was evaluated between 1 and 7 days after admission to the ICU, when they were hemodynamically stable, able to respond to basic commands and out of mechanical ventilation for more than 48 h.

Instruments

The MRC-ss was used to assess peripheral muscle strength using manual resistance. This scale presents excellent inter-rater reliability in the ICU environment and is highly correlated with physical function, functionality and hospital length of stay [ 8 , 20 ]. Muscle contraction strength is scored from 0 to 5 (0 represents no perceived muscle contraction and 5 optimal muscle strength) and it is applied in six muscle groups bilaterally (shoulder abduction, elbow flexion, wrist extension, hip flexion, knee extension and ankle dorsiflexion). The total score ranges from 0 points (complete tetraparesis) to 60 points (normal muscular strength), with scores between 37 and 48 indicating significant weakness and ≤ 36 indicating severe weakness [ 20 , 21 ].

The functional status was assessed with the FSS-ICU, which is composed of 5 tasks: rolling, supine to sit transfer, sit to stand transfer, sitting on the edge of bed and walking. Each functional task is rated using a scale ranging from 0 to 7, with 0 corresponding to unable to attempt or complete task due to weakness and 7 corresponding to complete independence. The total score varies from 0 to 35 points (completely independent) [ 22 , 23 ]. This score presents high inter-rater reliability among physical therapists who routinely work in the ICU [ 24 ]. It is also an internally consistent, valid and responsive measure of physical function in critically ill patients. The minimum important difference ranges between 2.0 and 5.0 points [ 25 ].

The assessment of physical function was performed using the PFIT-s. This tool presents good inter-rater reliability [ 26 ] and consists of four-component outcome measure: sit-to-stand level of assistance (assistance); maximal marching on the spot duration and number of steps (cadence); shoulder flexion strength (shoulder); knee extension strength (knee), the strength is obtained based on the Oxford grading system [ 27 , 28 , 29 , 30 ]. Each component presents a score ranging from 0 to 3 (0 indicates the inability to perform the task or achievement with a maximum level of dependence and 3 indicates the task accomplishment without any difficulty). Its total score ranges from 0 to 12 points and can be converted to an interval scale of 0 and 10, where the minimum significant difference varies between 1.0 and 1.5 points [ 27 , 28 , 29 , 30 ]. The original 12-point version of the scale was used in this study.

Linking process

At first the contents of the tools were identified and linked to the ICF. This process was conducted by two independent experienced evaluators in this methodology following the guidelines proposed by Cieza et al. [ 18 ]. The proposed method has two updates [ 17 , 18 ] and is widely used to observe several instruments framework within the ICF [ 31 , 32 , 33 , 34 ]. The most recent version has ten rules for linking the information from the scale’s contents to the ICF. The main content definition for each tool item and its correspondence with ICF existing category are the major rules [ 18 ]. All the evaluators were instructed to independently link the instruments (MRC-ss, PFIT-s and FSS-ICU) to the ICF, while a third evaluator was available to be contacted in case of doubts and/or disagreements (supplement 1).

The ICF has two parts with two components in each: Part 1 – Functioning and Disability = body functions and structures, activities and participation; Part 2 – Contextual Factors = environmental factors, personal factors. Each component can be expressed in positive and negative terms and contains several domains. Each domain is made up of various categories that correspond to the classification units.

The ICF codes correspond to the category as a whole and the qualifiers are numeric codes that specify the extent or the magnitude of the problem in each category [ 10 ]. Qualifiers range from 0 to 4 as follows: 0 – no problem (0 to 4% impairment); 1 – mild problem (5 to 24% impairment); 2 – moderate problem (25–49% impairment); 3 – severe problem (50 to 95% impairment); and 4 – complete problem (96 to 100% impairment). Table  1 presents the established criteria for ICF qualifiers’ definition.

Data analysis

Each ICF category and qualifier was expressed in absolute and relative and frequencies. The scores of the tools were expressed as means ± standard deviation. The inter-rater agreement for the linking process was performed using the Kappa coefficient and interpreted as follows: values ≤ 0 as no agreement, 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement. Reliability was estimated using the Rosner scale [ 35 ]. Data were analyzed using the Statistical Package for Social Sciences (SPSS) software, version 23.0 (IMB Corp, USA). For all analyzes, a significance level of p < 0.05 was considered.

Linking of the instruments to the ICF

The ICF categories identified in the tools covered a total of 14 items. Common contents were identified in 13 of the 14 ICF categories (Table  2 ) and two were related to body functions, six to body structures and five to activities and participation.

As shown in Table  2 , the items of all instruments could be linked to ICF codes. However, some of the codes were suitable for more than one instrument. In these cases, only the instrument that best quantified the functional level was chosen as follows: the b7300 code was presented as the total score of the MRC-ss; the s75012 code was assessed according to the MRC-ss and only the result of the segment that obtained the highest result was used for data analysis; and the codes s7202 and d4104 were applied according to the FSS-ICU due to greater possibility of functional level specification.

As shown in Table  3 , the inter-rater agreement was considered substantial for MRC-ss and PFIT-s linking to the ICF, and almost perfect for the FSS-ICU linking process.

Functional profile

Twenty-four in-patients (14 males) with mean age of 63.1 ± 8.9 years and hospital length of stay of 4.6 ± 2.7 days were included. The functional profile of the patients is shown in Table  4 . The main causes of admission to the ICU were cardiac (62,5%), respiratory (25%) and postoperative conditions (12,5%). 100% of the sample presented comorbidities prior to hospitalization (diabetes mellitus – 100%, pneumonia – 20,8%, coronary artery disease – 62,5% and COPD – 8,3%).

As shown in Table  4 , most of the patients (58.3%) presented mild problem regarding peripheral muscle strength, while moderate and severe problems were identified in 20.8% and 12.5%, respectively. These results agree with the classification of the MRC-ss instrument and are justified by the results of the body structures domain. For example, the ICF qualifiers 2 and 3 correspond to significant and severe weakness in the MRC-ss, respectively.

A mean score of 30.5 ± 7.4 was observed in the FSS-ICU instrument, indicating that the sample presented some degree of functional impairment. When measured using the ICF activities and participation domain, most of the sample (≥ 66.6%) was categorized as no problem (i.e., qualifier 0) for the following codes: d4103, d4104, d4107, d4153 and d4500. This was probably because the qualifier 0 was assigned to both the “total independence” and “modified independence” scores (i.e., use of supports, grids and aid devices during the task).

The results about physical functioning, according to PFIT-s, presented a mean score of 8.2 ± 2.4 (functional impairment) and none of the patients scored the maximum value. The functional impairment was also observed when assessing the items in the ICF model, especially in the b7601 code (stationary gait), since only three patients (12.5%) were categorized in the qualifier 0 (no problem) and the remaining patients in the qualifiers 2 to 4 (moderate to complete problem) (Table  4 ).

The ICF structure, created and recommended by the World Health Organization, defines functionality as a generic term for the interaction between three distinct constructs: body function, body structure and activities and participation [ 10 ].

The functional impairment due to critical illness leads to significant morbidity and burden for patients, caregivers and the society. As the number of ICU survivors is growing worldwide it becomes essential to standardize the functionality and physical function measurements [ 36 ]. Currently, there is not a single measurement tool available that can be used during the entire recovery journey. Therefore, it seems essential to consider the elements assessed under the ICF domains [ 36 ].

Electronic tools that aim to operationalize the ICF qualifiers in acute care settings can provide the functional profile and define the main objectives of the treatment interventions based on the ICF categories [ 37 , 38 ]. These tools may contribute to reducing the time for filing the medical records, facilitate the registration of information in databases and share information between different medical sectors [ 38 , 39 ]. However, the usefulness and applicability of this approach need to be explored. Therefore, studies classifying the patients’ health needs according to the ICF are essential to allow comparisons based on a universal language [ 40 ].

Clinicians and researchers can use appropriate ICF outcome measures to observe changes in the patients’ level of impairment, activity limitations and participation restrictions [ 8 ]. Parry et al. [ 36 ] identified several ICF domains in 11 of the most well-known physical function instruments and the following items corroborated with our results: d4103, d4153 and d4500 for FSS-ICU, as well as b7300 and d4103 for PFIT-s. In another study [ 41 ], data from 60 physical function instruments covered 26 ICF domains and 19 mobility subdomains. The b730, d4103, d4107, d4153, d4500 codes also corroborated with our study. However, the following items differed: b749, b455, d4, d4104, and d4508 for PFIT-s; d4, d4100, d4104 and d465 for FSS-ICU. As in the study conducted by Parry et al. [ 36 ], this review [ 41 ] did not mention the MRC-ss items.

Previous studies observed that FSS-ICU and PFIT-s present excellent validity for the ICU environment. These are promising functional measures and should be considered when measuring physical function in both clinical practice and research [ 42 ]. Nevertheless, the assessment of voluntary muscle strength at bedside using the MRC-ss [ 43 , 44 ] predicts mortality, length of ICU and hospital stay and duration of mechanical ventilation [ 45 ].

The ICF categories used in our study corroborate with Paschoal et al., who identified the following relevant components and codes for acute and post-acute care in the Brazilian scenario [ 15 ]: body functions with codes b730 (muscle power functions) and b760 (control of voluntary movement functions); body structures with codes s720 (structure of shoulder region), s730 (structure of upper extremity) and s750 (structure of lower extremity); and activity and participation with codes d410 (changing basic body position), d415 (maintaining a body position) and d450 (walking). Despite this, it is important to note that it is mentioned in the recommendations that FCC-ICU and MRC-ss are used as tools in the neurological/neurosurgical and cardiovascular/cardiosurgical intensive care environments, respectively [ 41 ]. Only PFIT-s is not mentioned in these environments. Moreover, none of them were evaluated outside the ICU setting. Taking this for granted, our study is the pioneer in linking these instruments to the ICF and combining FSS-ICU, PFIT-s and MRC-ss in an assessment model based on the ICF.

Seguel et al. [ 9 ] conducted a study using FSS-ICU score with a methodology like ours, but the authors attributed the score 7 of the scale (complete independence) to the qualifier 0 (no problem) and the scores 4, 5, and 6 (from minimum assistance to modified independence) were assigned to the qualifier 1 (mild problem). In our study, the qualifier 0 (no problem) was composed of scores 7 and 6 (complete and modified independence) since the patient is considered independent in both scores, even though some walking or standing aid devices are used. Additionally, a 4% margin variation can be attributable to some possible events, such as imbalance or slow walking speed. On the other hand, both scores 4 and 5 were assigned as qualifier 1 in our study, indicating that the patient must perform at least 75% of the task alone. We believe this can be considered practicable since qualifier 1 allows a level of impairment up to 24%.

Some key considerations in choosing an instrument for application should be based on the purpose of the assessment, measurement properties, patient capability and clinical utility. Therefore, the use of MRC-ss on ICU admission and FSS-ICU and PFIT-s during hospitalization are recommended in clinical practice [ 36 ].

The MRC-ss is indicated by the most relevant studies for the clinical diagnosis of ICU-AW, as it is routinely used to screen for muscle weakness in critically ill patients [ 42 , 43 ]. In addition to being a widely used method, it also has excellent inter-rater reliability for the overall score and is considered a sensitive method to assess the progression of rehabilitation for ICU-AW in patients who do not have enough strength to overcome gravity [ 20 , 44 ].

FSS-ICU allows the assessment of physical function within 10 to 30 min of application (depending on the patient’s functional status) and is translated and validated for the Brazilian population [ 26 ]. It presents high inter-observer reliability among physical therapists working in ICU environments and excellent reliability for its use in critical care settings [ 24 , 45 ]. In addition, this instrument has a low estimated ceiling and floor effect (below the acceptable cut-off point of 15%) at awakening and discharge from the ICU time points. This brings important value in evaluating the recovery process of patients and the effectiveness of a determined intervention. Floor or high ceiling effects indicate that the instrument is too challenging or too easy, respectively, limiting its ability to detect a change in patients’ physical function. Regarding FSS-ICU, it still presents evidence for a minimum important difference [ 8 , 25 , 36 , 44 , 45 ].

PFIT-s allows the assessment of the patient in 10 to 15 min, requiring only a stopwatch and a subjective exertion perception scale (optional). The ceiling and floor effects are also considered low (below the acceptable cut-off point of 15%) at awakening and discharge from the ICU time points, showing evidence of a minimal important difference. Furthermore, a study has shown that PFIT has convergent validity (moderate to high correlation with TUG, 6MWT and muscle strength by MRC-ss); divergent validity (low correlation with body mass index); high responsiveness over time (high effect size – 0.82) and predictive validity (association with peripheral muscle strength, hospital discharge, length of stay, and need for post-discharge rehabilitation) [ 29 , 45 ].

Although this pilot study provides relevant contributions regarding the linking process of validated ICU instruments to the ICF, the categories used on our analysis were restricted to the body structures, body functions and activities and participation components without including the environmental factors. This was due to two main reasons: first, less approach is traditionally given to environmental factors in the hospital and intensive care setting; second, the tools investigated in our study concern particularly to functional tasks of bedridden patients. Even though health care professionals are aware of the potential impact of environmental factors on patients’ results and prognosis, the relevance of environmental issues in an ICU setting cannot be directly influenced by physical therapists since ICF provides broad definitions for the categories of the environmental factors component [ 39 ]. Therefore, future studies are needed to trace the functional profile of critically care patients using the environmental factors of ICF categories. Additionally, we suggest that future studies investigate the association between different categories and qualifiers and clinical outcomes.

The proposed model combines the ICF with the contents of the most relevant tools used in critical care and ICU for physical function assessment. This study synthesizes and categorizes the most used instruments and presents a new proposal based on the ICF to guide future studies.

Although ICF tool is robust in describing the functionality in acute care settings, it is not widely used in this setting and is still not feasible in terms of environmental factors since the assessment is restricted to functions, body structures and activity and participation.

Data availability

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

Abbreviations

Intensive Care Units

International Classification of Functioning, Disability and Health

Inflammatory Response Syndrome

Medical Research Council sum score

Functional Status Score for the ICU

Physical Function in ICU Test-scored

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The authors thank Probatus Academic Services for providing scientific language translation, revision, and editing.

The study did not receive financial support.

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Juliana S. F. dos Santos, Nubia M. F. V. Lima, Lucien P. Gualdi & Íllia N. D. F. Lima

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IL designed and supervised the study. JS, GS collected the data and did independently linking process between scales using Cieza’s et al. methodology and guidelines. They resolved any disagreements by consensus or consulta third review author DD.IL,DD analyzed and interpreted the data. JS, IL, DD wrote the article. IL, NM, LG revised the article.All authors read and approved the final version of the manuscript.

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Correspondence to Gabriely A. G. Silva .

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The study protocol was approved by the Research Ethics Committee of the Federal University of Rio Grande do Norte (CAAE 49235715.3.0000.5568). All participants signed an informed consent to participate and for publication, and the study was conducted in accordance with Declaration of Helsinki and Resolution 466/12 of the National Health Council – Brazil.

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dos Santos, J.S.F., Silva, G.A.G., Lima, N.M.F.V. et al. Linking Intensive Care Unit functional scales to the International Classification of Functioning: proposal of a new assessment approach. BMC Health Serv Res 23 , 871 (2023). https://doi.org/10.1186/s12913-023-09787-9

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HLA variants have different preferences to present proteins with specific molecular functions which are complemented in frequent haplotypes

Affiliations.

• 1 Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia.
• 2 Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia.
• 3 Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom.
• 4 Medical Research Council (MRC) Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom.
• 5 Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM) Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), University of Oxford, Oxford, United Kingdom.
• 6 The Jenner Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
• 7 Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia.
• PMID: 36605212
• PMCID: PMC9808399
• DOI: 10.3389/fimmu.2022.1067463

Human leukocyte antigen (HLA) genes are the most polymorphic loci in the human genome and code for proteins that play a key role in guiding adaptive immune responses by presenting foreign and self peptides (ligands) to T cells. Each person carries up to 6 HLA class I variants (maternal and paternal copies of HLA-A, HLA-B and HLA-C genes) and also multiple HLA class II variants, which cumulatively define the landscape of peptides presented to T cells. Each HLA variant has its own repertoire of presented peptides with a certain sequence motif which is mainly defined by peptide anchor residues (typically the second and the last positions for HLA class I ligands) forming key interactions with the peptide-binding groove of HLA. In this study, we aimed to characterize HLA binding preferences in terms of molecular functions of presented proteins. To focus on the ligand presentation bias introduced specifically by HLA-peptide interaction we performed large-scale in silico predictions of binding of all peptides from human proteome for a wide range of HLA variants and established which functions are characteristic for proteins that are more or less preferentially presented by different HLA variants using statistical calculations and gene ontology (GO) analysis. We demonstrated marked distinctions between HLA variants in molecular functions of preferentially presented proteins (e.g. some HLA variants preferentially present membrane and receptor proteins, while others - ribosomal and DNA-binding proteins) and reduced presentation of extracellular matrix and collagen proteins by the majority of HLA variants. To explain these observations we demonstrated that HLA preferentially presents proteins enriched in amino acids which are required as anchor residues for the particular HLA variant. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Taking into consideration that HLA alleles are inherited in haplotypes, we hypothesized that haplotypes composed of a combination of HLA variants with different presentation preferences should be more advantageous as they allow presenting a larger repertoire of peptides and avoiding holes in immunopeptidome. Indeed, we demonstrated that HLA-A/HLA-B and HLA-A/HLA-C haplotypes which have a high frequency in the human population are comprised of HLA variants that are more distinct in terms of functions of preferentially presented proteins than the control pairs.

Keywords: HLA; gene ontologies and pathways; mass spectrometry; molecular function; protein.

Copyright © 2022 Karnaukhov, Paes, Woodhouse, Partridge, Nicastri, Brackenridge, Shcherbinin, Chudakov, Zvyagin, Ternette, Koohy, Borrow and Shugay.

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Conflict of interest statement

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.

Overview of the study. (A)…

Overview of the study. (A) In silico HLA ligandomes are generated by running…

Human genes enriched and depleted…

Human genes enriched and depleted in HLA ligands and their associated Gene Ontology…

Amino acid composition of HLEPs…

Amino acid composition of HLEPs and HLDPs for different HLA alleles. (A) Comparison…

Experimental validation of biased selection…

Experimental validation of biased selection of self proteins presented by different HLA alleles…

Visualizing similarities between HLA alleles…

Visualizing similarities between HLA alleles based on enriched GO categories of genes they…

Differences in the number of…

Differences in the number of ligands coming from viral genes presented by 12…

Bias in HLA presentation of…

Bias in HLA presentation of proteins with different molecular functions is compensated in…

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