Functional ability and quality of life in critical illness survivors with intensive care unit acquired weakness: A secondary analysis of a randomised controlled trial

Sabrina Eggmann; Gere Luder; Martin L Verra; Irina Irincheeva; Caroline H G Bastiaenen; Stephan M Jakob

doi:10.1371/journal.pone.0229725

. 2020 Mar 4;15(3):e0229725. doi: 10.1371/journal.pone.0229725

Functional ability and quality of life in critical illness survivors with intensive care unit acquired weakness: A secondary analysis of a randomised controlled trial

Sabrina Eggmann ^1,^2,^*, Gere Luder ¹, Martin L Verra ¹, Irina Irincheeva ³, Caroline H G Bastiaenen ², Stephan M Jakob ⁴

Editor: Andre Scherag⁵

¹Department of Physiotherapy, Insel Group, Inselspital, Bern University Hospital, Bern, Switzerland

²Department of Epidemiology, Research Line Functioning and Rehabilitation CAPHRI, Maastricht University, Maastricht, the Netherlands

³CTU Bern, University of Bern, Bern, Switzerland

⁴Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland

⁵Universitatsklinikum Jena, GERMANY

Competing Interests: SE, GL, MLV, CHGB declare that they have no competing interests. II is affiliated with CTU Bern, University of Bern, which has a staff policy of not accepting honoraria or consultancy fees. However, CTU Bern is involved in design, conduct, or analysis of clinical studies funded by not-for-profit and for-profit organizations. In particular, pharmaceutical and medical device companies provide direct funding to some of these studies. For an up-to-date list of CTU Bern’s conflicts of interest see http://www.ctu.unibe.ch/research/declaration_of_interest/index_eng.html. SMJ reports the following potential conflicts of interest: The Department of Intensive Care Medicine has, or has had in the past, research contracts with Orion Corporation, Abbott Nutrition International, B. Braun Medical AG, CSEM SA, Edwards Lifesciences Services GmbH, Kenta Biotech Ltd, Maquet Critical Care AB, Omnicare Clinical Research AG and research & development/consulting contracts with Edwards Lifesciences SA, Maquet Critical Care AB, and Nestlé. The money was paid into a departmental fund; SMJ received no personal financial gain. The Department of Intensive Care Medicine has received unrestricted educational grants from the following organizations for organizing a quarterly postgraduate educational symposium, the Berner Forum for Intensive Care (until 2015): Fresenius Kabi, gsk, MSD, Lilly, Baxter, astellas, AstraZeneca, B | Braun, CSL Behring, Maquet, Novartis, Covidien, Nycomed, Pierre Fabre Pharma AG (formerly known as RobaPharm), Pfizer, Orion Pharma, Bard Medica S.A., Abbott AG, Anandic Medical Systems. The Department of Intensive Care Medicine has received unrestricted educational grants from the following organizations for organizing bi-annual postgraduate courses in the fields of critical care ultrasound, management of ECMO and mechanical ventilation: Pierre Fabre Pharma AG (formerly known as RobaPharm), Pfizer AG, Bard Medica S.A., Abbott AG, Anandic Medical Systems, PanGas AG Healthcare, Orion Pharma, Bracco, Edwards Lifesciences AG, Hamilton Medical AG, Fresenius Kabi (Schweiz) AG, Getinge Group Maquet AG, Dräger Schweiz AG, Teleflex Medical GmbH. No author was employed by any of these companies. None of the companies played a role in this study. Our relationship with the companies does not alter our adherence to PLOS ONE policies on sharing data and materials.

^✉

* E-mail: sabrina.eggmann@insel.ch

Roles

Sabrina Eggmann: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Gere Luder: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Writing – review & editing

Martin L Verra: Conceptualization, Investigation, Methodology, Resources, Supervision, Writing – review & editing

Irina Irincheeva: Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing – review & editing

Caroline H G Bastiaenen: Conceptualization, Methodology, Supervision, Writing – review & editing

Stephan M Jakob: Conceptualization, Investigation, Methodology, Resources, Supervision, Writing – review & editing

Andre Scherag: Editor

PMCID: PMC7056321 PMID: 32131082

Abstract

Introduction

Intensive care unit acquired weakness (ICUAW) may contribute to functional disability in ICU survivors, yet performance-based data for general ICU patients are lacking. This study explored functional outcomes of (1) and risk factors for (2) weakness at ICU discharge.

Methods

Data from a randomised controlled trial that investigated two early exercise regimes in previously independent, ventilated adults (n = 115) without any significant outcome-differences were used for the present analysis. ICUAW was clinically diagnosed in cooperative participants (n = 83) at ICU discharge with the Medical Research Council sum-score (MRC-SS) using a cut-off <48 for moderate or <36 for severe weakness. Primary outcomes were the 6-Minute Walk Test and Functional Independence Measure at hospital discharge. Secondary outcomes included health-related quality of life after six months. Risk factors during the ICU stay were explored for their effect on MRC-SS with linear regression.

Results

Functional outcomes and length of hospital stay significantly differed in patients with severe, moderate to no weakness (6-Minute Walk test: p = 0.013; 110m [IQR 75–240], 196m [90–324.25], 222.5m [129–378.75], Functional Independence Measure: p = 0.001; 91[IQR 68–101], 113[102.5–118.5], 112[97–123], length of stay after ICU discharge: p = 0.008; 20.9d [IQR 15.83–30.73], 16.86d [13.07–27.10], 11.16d [7.35–19.74]). However, after six months participants had similar values for quality of life regardless of their strength at ICU discharge (Short-Form 36 sum-scores physical health: p = 0.874, mental health: p = 0.908). In-bed immobilisation was the most significant factor associated with weakness at ICU discharge in the regression models (MRC-SS: -24.57(95%CI [-37.03 to -12.11]); p<0.001).

Conclusions

In this general, critically ill cohort, weakness at ICU discharge was associated with short-term functional disability and prolonged hospital length of stay, but not with quality of life, which was equivalent to the values for patients without ICUAW within six months. Immobilisation may be a modifiable risk factor to prevent ICUAW. Prospective trials are needed to validate these results.

Trial registration

German Clinical Trials Register (DRKS) identification number: DRKS00004347, registered on September 10, 2012.

Introduction

Recent years have seen increased survival rates for severely ill patients admitted to intensive care units (ICU) [1, 2]. However, long-term functional disability as well as cognitive and mental health impairment are common in ARDS or sepsis survivors [3–6], leading to poor quality of life and a substantial five-year mortality [7].

Muscle weakness may be a key contributor to the persisting disability of these survivors [8, 9]. However, there is a paucity of data to support a negative impact of ICU-acquired weakness (ICUAW) on physical functioning [10]. Moreover, ICUAW may also be present in less severely ill patients [11]. The few studies that investigated functional outcomes in ICUAW survivors were limited to specific subgroups (e.g. ARDS) [12, 13], lacked performance-based measurements [14–16] or were conducted after the post-acute phase thereby including only the weakest patients [17]. More research on the physical consequences of ICUAW is therefore highly needed to advance early treatment and to prevent long-term disability after critical illness.

Additionally, early identification of persons at risk for ICUAW is necessary for targeted therapeutic or preventive interventions. Postulated risk factors for ICUAW are multiorgan failure [18], increased systemic inflammation [19], female sex [20], duration of mechanical ventilation [21] or bed rest [12]. However, the strength of these risk factors’ association with ICUAW remains uncertain and requires further investigation [10].

This exploratory study therefore aimed first to investigate functional outcomes at hospital discharge and health-related quality of life after six months in critically ill patients with severe, moderate or no ICUAW at ICU discharge, and second to explore the role of early risk factors for reduced muscle strength at ICU discharge in mechanically ventilated, critically ill adults.

Methods

Design and setting

Data for this secondary analysis were collected as part of a randomised controlled trial that compared very early endurance training and mobilisation to usual care in mechanically ventilated, critically ill adults [22]. The trial was prospectively registered (DRKS00004347) and conducted in the mixed ICU of a large, tertiary academic centre in Switzerland (Department of Intensive Care Medicine, Inselspital, Bern University Hospital) between October 8, 2012 and April 5, 2016. No significant differences were found for the primary or secondary outcomes, whereby participants of the experimental group appeared to have better mental health at six months after hospital discharge. A high ICUAW incidence in the whole cohort led to the research questions of this secondary analysis, which was subsequently approved by the local ethics committee (Ethics Committee of Canton Bern) in February 2019 (ID 2019–00156). During the randomised controlled trial, written informed consent was obtained from the next of kin within 72 hours after randomisation and a written informed consent from each patient as early as possible. The need for further consent was waived by the ethics committee.

Study population and management

Eligible participants were older than 18 years, had been independent before the episode of critical illness and were expected to remain on mechanical ventilation for at least 72 hours. Participants with pre-existing muscle weakness, preceding hospital stay of more than 10 days duration, contraindications to cycling, enrolment in another trial, palliative care, diagnoses that precluded walking at hospital discharge, or insufficient command of German or French were excluded.

All patients were managed by targeted, light sedation and protocol-guided weaning [23]. Standard ICU care also included a nutrition protocol and regular assessments of energy expenditure using indirect calorimetry [24]. Physiotherapy started within 48 hours of ICU admission with two different exercise regimes that included usual early mobilisation versus early, progressive endurance and resistance training in addition to usual early mobilisation. All participants received standard therapy after ICU discharge such as cycling, walking, strengthening, breathing and functional exercises. Further details on study interventions and procedures have been published elsewhere [25].

Data collection and measurements

Baseline demographics and clinical characteristics were collected at study enrolment by the study nurse responsible. Functional measurements were administered by trained physiotherapists blinded to initial group allocation. Muscle strength was assessed with the Medical Research Council sum-score (MRC-SS) at ICU discharge. The MRC-SS evaluates strength in three bilateral muscles of the upper and lower extremities from 0 (no contraction) to 5 (normal strength) with a maximal summed score of 60 [26]. A cut-off of less than 48 points was used to clinically diagnose ICUAW [27]. To further differentiate between moderate and severe weakness, participants who scored less than 36 were considered severely weak [28]. Valid and reliable strength assessments are dependent upon sufficient cooperation by the participant [29]. Accordingly, the MRC-SS was only performed in participants who were able to follow at least 3 out of 5 standardised commands [20]. In the case of an ICU re-admission within the same hospital stay, the last available MRC-SS was used for analysis to account for subsequent weakness. ICU re-admissions were subject to the same study procedures and data collection as described previously.

The primary functional outcomes for this analysis were chosen as per the original trial: functional independence evaluated with the Functional Independence Measure (FIM) [30] and functional capacity assessed with the 6-Minute Walk Test (6MWT) [31] at hospital discharge. Additional secondary outcomes of interest were FIM at ICU discharge, Timed ‘Up & Go’ [32] at hospital discharge, hospital length of stay and discharge destination, tracheostomy incidence, ICU readmissions, hospital and 6-month mortality as well as participants’ health-related quality of life determined with the Short Form 36 (SF-36) [33] six months after hospital discharge. We further explored whether functional performance at hospital discharge might be useful to predict 6-month quality of life. See supporting information for a detailed timetable (S1 Table).

Statistical analysis

The two randomised groups did not differ with regard to the primary or secondary outcomes, consequently, we considered the cohort as one single cohort. Patient demographics and characteristics were summarised with descriptive statistics. Continuous data could not be assumed normally distributed and are therefore presented as medians with interquartile ranges (IQR: first (25%) quartile to third (75%) quartile). Categorical data are given as numbers with percentages.

We hypothesised that participants with no ICUAW would achieve better functional performance at hospital discharge and a higher quality of life after six months when compared to participants with moderate to severe ICUAW. The null hypothesis of equal distributions in functional outcomes for patients diagnosed with no (MRC-SS >48), moderate (MRC-SS 36–47) or severe ICUAW (MRC-SS <36) was tested against the alternative hypothesis of an ordered relationship with the Cuzick test [34]. For example, when applying the Cuzick test to ‘6MWT’, the null hypothesis of equal distributions was tested against the following alternative hypothesis: severe weakness < no weakness and severe weakness ≤ moderate weakness ≤ no weakness, and when applying the Cuzick test to ‘hospital length of stay’, the alternative hypothesis was severe weakness > no weakness and severe weakness ≥ moderate weakness ≥ no weakness. In baseline variables the ordered relationship among groups did not make sense, therefore, the baseline measurements for the three MRC-SS groups were compared with the non-parametric Kruskal-Wallis test. Categorical data were analysed with Pearson’s Chi-Squared test. Baseline characteristics of participants with complete versus missing MRC-SS values were investigated for baseline differences because of the prerequisite of cooperation for a valid MRC-SS assessment. Correlations were investigated with non-parametric Spearman correlation coefficients. Considering the aims of this analysis, multiple imputations were not performed as expected extrapolation would have been applied to patients who died or were unable to follow commands and thus unable to be functionally active. However, to account for the limited sample size, we conducted a sensitivity analysis to compare the data of participants without ICUAW to the pooled data of participants with severe and moderate ICUAW with the non-parametric Mann-Whitney-U tests for between-group comparisons.

The ICU risk factors for muscle weakness at ICU discharge to be explored were identified from previous evidence [35] and chosen based on the available data by favouring uncorrelated risk factors. We assumed that our analysis would reveal some influence of the chosen risk factors for reduced muscle strength at ICU discharge regardless of initial randomisation. To avoid overfitting, we restricted the factors to six (with a rule of thumb of a minimum 10 observations per factor [36]): Sequential Organ Failure Assessment (SOFA) at study inclusion [18, 19], gender [20], length of ICU stay [37], previous limitation in activities of daily living (ADL) [38], mobility level in the ICU (in-bed, edge-of-bed, out-of-bed) [12], and initial randomisation group (control or experimental) in order to exclude a confounding effect. We investigated the effect of all risk factors on MRC-SS as crude (with simple linear regression) and adjusted for the totality of considered factors (with multivariable linear regression). Two sensitivity analyses were conducted with all listed risk variables excluding either randomisation group (potential confounder) or previous ADL limitation (due to missing data).

Data analysis was performed with R version 3.5.3 (2019-03-11) and SPSS (IBM SPSS Statistics Version Premium GradPack 24). The level of significance was set at p<0.05 (two-tailed). Due to the exploratory nature of this analysis, we decided a priori that the significance threshold would not be adjusted for multiple testing.

Results

Of the 115 participants who were enrolled in the randomised controlled trial, 83 (72%) completed an MRC-SS at ICU discharge (Fig 1). Main reasons for missing MRC-SS values were ICU-death (14%) and patients’ inability to follow commands (11%), while 5 patients (4%) were missed for follow-up at ICU discharge. Among the assessed patients, ICUAW was common with 17 (20%) severe, 32 (39%) moderate, and 34 (41%) no weakness, respectively. The patients in the three MRC-SS groups did not differ significantly in their baseline demographics and characteristics, except for a higher prevalence of liver disease in the severely weak group (Table 1). In contrast, participants with missing MRC-SS values differed in respect to APACHE II scores (completed 21 [IQR 17–26]; missing 27 [21.5–30]), SOFA (completed 8 [6–10]; missing 10 [8 to14.25]), and frequency of liver disease (completed 7 (9%); missing 8 (26%)) (S2 Table and S3 Table).

Table 1. Baseline demographics for participants with complete MRC-SS assessment.

Variables	n	All with complete MRC-SS	n	Severe weakness (MRC-SS <36)	n	Moderate weakness (MRC-SS 36–48)	n	No weakness (MRC-SS >48)	p-value
ICUAW incidence (MRC-SS<48)	83	49 (59%)	17	17 (100%)	32	32 (100%)	34	0 (0%)
MRC-SS at ICU discharge (0–60)	83	45 [38.5–54]	17	26 [20–31]	32	42 [40–45]	34	55.5 [50–58]
Randomized to non-standard intervention	83	40 (48%)	17	11 (65%)	32	12 (38%)	34	17 (50%)	0.186
Age (years)	83	67.5 [55.55–75.4]	17	68.1 [65.3–74.9]	32	67.85 [55.9–74.75]	34	60.75 [45.08–76.3]	0.284
Gender (male)	83	52 (63%)	17	9 (53%)	32	17 (53%)	34	26 (76%)	0.095
BMI (kg/m²)	83	26.2 [23.6–31.35]	17	27.8 [22.2–34]	32	27 [23.98–29.85]	34	25.65 [23.9–31.5]	0.984
Weight (kg)	83	80 [66–90]	17	85 [62–95]	32	79.2 [66.5–85]	34	80 [68.25–90]	0.783
APACHE II score (0–71) ^a	83	21 [17–26]	17	23 [18–26]	32	21.5 [18–26.25]	34	20 [16.25–23.75]	0.483
SOFA score (0–24) ^b	83	8 [6–10]	17	9 [7–10]	32	8 [7–10.25]	34	6.5 [5–10]	0.065
ICU days until study inclusion	83	1.71 [0.85–2.57]	17	1.76 [1.24–2.63]	32	1.84 [1.05–2.61]	34	1.51 [0.8–2.08]	0.518
ICU length of stay at original hospital (days)	83	5.93 [4.43–10.26]	17	6.23 [4.73–14.35]	32	6.56 [4.28–11.88]	34	5.63 [3.50–7.89]	0.210
ICU diagnosis on ICU admission
Gastroenterology	83	11 (13%)	17	2 (12%)	32	6 (19%)	34	3 (9%)	0.680
Heart surgery		18 (22%)		5 (29%)		6 (19%)		7 (21%)
Hemodynamic insufficiency		16 (19%)		3 (18%)		6 (19%)		7 (21%)
Neurology / neurosurgery		4 (5%)		1 (6%)		1 (3%)		2 (6%)
Other		1 (1%)		0 (0%)		0 (0%)		1 (3%)
Other surgery		11 (13%)		3 (18%)		4 (12%)		4 (12%)
Respiratory insufficiency		20 (24%)		2 (12%)		9 (28%)		9 (26%)
Trauma		2 (2%)		1 (6%)		0 (0%)		1 (3%)
Comorbidities on ICU admission
Restricted in activities of daily living (ADL)	80	8 (10%)	16	3 (19%)	32	4 (12%)	32	1 (3%)	0.196
NYHA symptoms (stage 2 to 4)	80	36 (45%)	16	6 (38%)	32	18 (56%)	32	12 (38%)	0.256
Dyspnoea symptoms	80	20 (25%)	16	2 (12%)	32	7 (22%)	32	11 (34%)	0.223
Hematologic malignancy	80	3 (4%)	16	0 (0%)	32	0 (0%)	32	3 (9%)	0.097
Immuno-suppression	80	11 (14%)	16	3 (19%)	32	3 (9%)	32	5 (16%)	0.622
Liver disease	80	7 (9%)	16	4 (25%)	32	2 (6%)	32	1 (3%)	0.033
Chronic dialysis	80	0 (0%)	16	0 (0%)	32	0 (0%)	32	0 (0%)

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^a at ICU admission

^b at study inclusion

Data are presented as median [IQR 25% - 75%] or frequencies (%). Analysis for continuous variables was performed with the Kruskal–Wallis test for the null hypothesis of equal distributions in the three groups, and for categorical and binary variables with Pearson’s Chi-Squared test with the null hypothesis of independence between the tested condition and MRC-SS groups.

Abbreviations: NYHA = New York Heart Association, BMI = Body Mass Index, APACHE = Acute Physiology and Chronic Health Evaluation, SOFA = Sequential Organ Failure Assessment

Functional outcomes, hospital variables and quality of life

The differences between severe, moderate and no weakness for the primary and secondary outcomes are presented in Table 2. Weakness at ICU discharge was significantly associated with functional disability at hospital discharge and subsequent length of hospital stay, but not with health-related quality of life after six months. The distribution between the three MRC-SS groups for the two primary outcomes is illustrated in Fig 2, rejecting the null-hypothesis of equal distributions. Thus, for both 6MWT and FIM participants with ‘severe weakness’ performed less well compared to ‘no weakness’, performance in ‘severe weakness’ was similar or worse to ‘moderate weakness’, which was also similar to or worse than ‘no weakness’. The achieved percentages of age-predicted values for the 6MWT were also significantly different (p = 0.006: Cuzick test) with 36% (IQR 19–65), 55% (IQR 25–94) and 74% (IQR 39–89) of predicted normative values [39] for severe, moderate and no weakness, respectively. Fig 3 further illustrates the distribution between the three MRC-SS groups for the Timed ‘Up & Go’ test and hospital length of stay after ICU discharge, while Fig 4 illustrates the distribution for the SF-36 physical and mental health sum-scores. Sensitivity analysis comparing all participants with ICUAW versus non-ICUAW similarly confirmed a significant association of ICUAW with functional disability and prolonged hospital stay, and lack of association with 6-month health-related quality of life (S4 Table). Functional performance at hospital discharge was not associated with quality of life (S5 Table). Hospital discharge destinations did not significantly differ between the three MRC-SS groups (p = 0.321): Severely weak patients (n = 17) were predominantly discharged to a rehabilitation facility (65%), moderately weak patients (n = 32) to rehabilitation (47%) or an external hospital (25%) and non-weak participants (n = 34) to rehabilitation (53%) or home (29%). Similarly, there was no difference in the MRC-SS groups for ICU readmissions (p = 0.264), tracheostomy incidence (p = 0.630) or hospital and 6-month mortality (p = 0.362).

Table 2. Primary and secondary outcome-comparisons per MRC-SS group.

Variable	n	All with complete MRC-SS	n	Severe weakness	n	Moderate weakness	n	No weakness	p-value
Primary outcomes at hospital discharge
6MWT (m)	73	185 [95–320]	17	110 [75–240]	28	196 [90–324.25]	28	222.5 [129–378.75]	0.013
FIM (18–126)	73	110 [92–119]	17	91 [68–101]	27	113 [102.5–118.5]	29	112 [97–123]	0.001
Secondary ICU and hospital outcomes
FIM at ICU discharge (18–126)	83	36 [26.5–47.5]	17	24 [21–34]	32	31 [26.5–46]	34	41.5 [35–57.5]	<0.001
Timed ‘Up & Go ‘test (s) at hospital discharge	57	19 [11.4–25]	14	23.25 [20.25–34]	21	18.7 [12.6–27]	22	14 [8–23.25]	0.013
Hospital length of stay after ICU discharge (days)	83	16.87 [11.16–26.92]	17	20.9 [15.83–30.73]	32	16.86 [13.07–27.10]	34	11.16 [7.35–19.74]	0.008
SF-36: quality of life after 6 months
Physical functioning (0–100)	54	75 [46.25–85]	14	72.5 [55–80]	15	70 [37.5–85]	25	75 [45–90]	0.449
Role physical (0–100)	52	25 [0–50]	13	50 [25–75]	14	25 [0–43.75]	25	25 [0–50]	0.583
Bodily pain (0–100)	54	74 [51.25–100]	14	77 [53.75–100]	15	70 [41–92]	25	80 [62–100]	0.595
General health (0–100)	52	61 [45.75–73.25]	14	58.5 [47–70.75]	13	50 [40–57]	25	67 [52–77]	0.164
Vitality (0–100)	53	55 [40–70]	14	60 [51.25–73.75]	14	50 [30–55]	25	55 [50–70]	0.640
Social functioning (0–100)	53	75 [50–100]	14	87.5 [53.12–100]	14	75 [53.12–96.88]	25	75 [62.5–100]	0.982
Role emotional (0–100)	52	66.67 [33.33–100]	12	100 [33.33–100]	15	33.33 [0–83.34]	25	100 [33.33–100]	0.795
Mental health (0–100)	52	76 [68–85]	14	82 [69–87]	14	70 [61–79]	24	82 [71–88]	0.659
Physical health (sum-score)	49	42.6 [34.76–48.23]	12	43.19 [33.11–48.5]	13	42.92 [27.67–47.39]	24	42.18 [36.49–48.75]	0.874
Mental health (sum-score)	49	50.09 [44.4–56.19]	12	51.3 [44.56–58.08]	13	48 [37.67–51.08]	24	51.86 [46.18–56.19]	0.908

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Data are presented as median [IQR 25% - 75%] or frequencies (%). Only effectively measured data were analysed. Categorical and binary variables testing was performed with Pearson’s Chi-Squared test (the null hypothesis is independence between the tested condition and the MRC-SS groups). Continuous variables testing was performed with the non-parametric Cuzick test (the null hypothesis is equal distributions in the three groups against the alternative non-inferiority or non-superiority). SF-36 (version 2): worst score: 0, best score: 100, sum-score: T-values where the population mean is 50 and the SD is 10; based on US-population 1990. German norm-based (1994) standardized sum-scores (T-values) for SF-36 were similar to the US-population (data shown in sensitivity analysis in S4 Table).

Abbreviations: 6MWT = 6-Minute Walk Test, FIM = Functional Independence Measure, SF-36 = Short Form 36 questionnaire

Fig 2 — Illustration of the 6MWT (a) and the FIM (b) at hospital discharge with non-parametric Cuzick test clearly rejecting the null hypothesis in favour of alternative: values for severe weakness < no weakness and for severe weakness ≤ moderate weakness vales ≤ no weakness.

Fig 3 — Illustration of the Timed ‘Up & Go’ test (a) and hospital length of stay after ICU discharge (b) with non-parametric Cuzick test clearly rejecting the null hypothesis in favour of alternative: values for severe weakness > no weakness and severe weakness ≥ moderate weakness ≥ no weakness.

Fig 4 — Illustration of the SF-36 physical health sum-score (a) and mental health sum-score (b) with non-parametric Cuzick test accepting the null hypothesis of equal distributions.

Risk factors

Table 3 presents the ICU risk factors that were significantly associated with ICUAW at ICU discharge. In-bed immobilisation and female gender remained significantly associated with low MRC-SS scores in sensitivity analyses (S6 Table). When the ADL variable was removed from the model due to the reduced sample size, length of ICU stay became marginally associated with MRC-SS (-0.28 95%-CI [-0.55 to -0.01], p = 0.049).

Table 3. Univariate (crude) and multivariate (adjusted) regression models for chosen ICU risk factors with MRC-SS as response.

	n	Crude effect (95% CI)	p-value	n	Adjusted effect (95% CI)	p-value
Gender
female		Reference			Reference
male	83	6.94 (1.61 to 12.27)	0.013	80	5.51 (0.64 to 10.38)	0.030
Group allocation
control group		Reference			Reference
experimental group	83	-2.02 (-7.37 to 3.33)	0.461	80	-0.66 (-5.60 to 4.28)	0.794
Illness severity
SOFA score	83	-0.63 (-1.37 to 0.10)	0.094	80	-0.55 (-1.27 to 0.17)	0.141
Length of ICU stay
Length of ICU stay at original hospital (days)	83	-0.21 (-0.48 to 0.06)	0.126	80	-0.26 (-0.53 to 0.01)	0.065
Activities of daily living (ADL)
Not restricted in ADL		Reference			Reference
Restricted in ADL	80	-5.38 (-14.33 to 3.58)	0.243	80	-6.18 (-13.97 to 1.61)	0.124
Mobilisation level in ICU
Mobilisation level in ICU: out-of-bed ^a		Reference			Reference
Mobilisation level in ICU: in-bed	83	-23.80 (-37.05 to -10.54)	0.001	80	-24.57 (-37.03 to -12.11)	<0.001
Mobilisation level in ICU: edge-of-bed	83	-6.89 (-12.22 to -1.55)	0.013	80	-7.20 (-12.78 to -1.62)	0.014

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^a Overall p values for the factor “mobilisation level in the ICU” with Omnibus test: p = 0.001 for crude and p<0.001 for adjusted effect

Significance of the chosen variables in two regression models with MRC-SS as response. Crude regression is just response (MRC-SS) and corresponding explanatory variable. Adjusted regression includes all listed explanatory variables. The robust estimations give the same results with non-significant bias’ tests (with the null: presence of the bias due to lack of robustness or outlying observations). None of the terms revealed non-linearity when fitting a regression model allowing non-linear dependence (via penalized splines) on explanatory variables. For full regression-output see supporting information (S1 File).

Abbreviations: SOFA = Sequential Organ Failure Assessment, ADL = activities of daily living

Discussion

In this secondary analysis, participants without ICUAW at ICU discharge had better functional outcomes at hospital discharge with shorter length of hospital stays when compared to participants with moderate and, especially, severe ICUAW. However, contrary to our hypothesis, quality of life after six months was similar in participants with severe, moderate or no ICUAW and not associated with functional performance in the hospital. While mental health sum-scores reached normative values, physical health sum-scores continued to be reduced in all three MRC-SS groups. The risk factor most associated with weakness at ICU discharge was in-bed immobilisation. Female gender or reduced mobility levels were further risk factors of ICUAW. These results provide useful information about the functional ability and subsequent health-related quality of life in a general, mechanically ventilated ICU population after a critical illness period, but need to be validated in prospective studies.

There are few available studies with which to compare our findings on the functional outcomes of ICUAW patients at hospital discharge. Hermans et al. [14] reported no difference in the 6MWT in matched weak and non-weak patients. However, high dropout rates limited this conclusion and results were not confirmed in sensitivity analyses. Although not performed at hospital discharge, Fan et al. [12] investigated the 6MWT over 24 months in ARDS patients. After three months, they found no difference between ICUAW versus non-ICUAW patients, yet for all later time-points, the distance walked was significantly shorter in participants with ICUAW. Sidiras et al. [40] also described significant differences of the FIM at hospital discharge in ICUAW versus non-ICUAW patients. Yet, while values for non-ICUAW patients were comparable to our results, ICUAW patients were substantially less independent in their study (65 [IQR 53–87] in [40] versus 105.5 [88.5–117.5]). This might explain their results for 3- and 6-month follow-up, which were also significantly lower in participants with ICUAW, whereas our patients showed similar health-related quality of life. Overall our findings reinforce the rather weak evidence that ICUAW is associated with functional disability which may lead to less independence at hospital discharge. In turn, loss of independence might be the cause of prolonged length of hospital stays and increased health-care costs for patients with ICUAW [14], emphasising the need to make recovery a priority in acute care [41].

When comparing our results for health-related quality of life with the existing literature, we found some discrepancies. For example, weak and non-weak ARDS survivors differed significantly in their SF-36 physical function sum-scores at six months in a US cohort study [12]. Similarly, a Dutch study [15] reported significant differences in the SF-36 domain ‘physical functioning’ between ICUAW (45 [IQR 30–70]) and non-ICUAW (75 [50–90]) in participants from a general ICU population. In our study, regardless of initial weakness, participants presented with similar median values in the ‘physical functioning’ domain (ICUAW 70 [IQR 50–80] versus non-ICUAW 75 [45–90], p = 0.375) as the participants without ICUAW in the Dutch study [15]. Finally, in a Greek trial, participants with ICUAW scored significantly lower in the SF-36 domains of ‘general health’, ‘pain’, ‘physical functioning’ and ‘role physical’ than non-ICUAW [40]. The apparent recovery of the very weak patients in our study was therefore surprising and may imply a different pathway of recovery between countries. However, we could not distinguish a difference in discharge destinations between the three MRC-SS groups. Alternatively, our population might have been more responsive to recovery. Herridge et al. [42] describe four disability subtypes based on the FIM seven days after ICU discharge as well as by age and length of ICU stay. These four subtypes determined a 1-year trajectory of recovery. We cannot directly compare the FIM due to the different time-points of the measurement (36 at ICU discharge in our cohort versus 54 one week after ICU discharge in [42]). However, when comparing baseline characteristics, we suspect that the shorter length of ICU stay in our cohort (5.9 versus 19.0 days in [42]), despite being slightly older (68 years versus 58 in [42]), might have been beneficial for recovery. Our results might also have been confounded by non-responders who might have been too ill to return the questionnaire or influenced by false negative results especially in the ‘role physical’ domain where severely weak patients (50 [IQR 25–75]) had surprisingly higher values than non-weak patients (25 [0–50]). Lastly, our sample might have been too small and selective to reveal a relevant association of ICUAW with quality of life.

Although the investigated risk factors were not inclusive of all previously known contributors to ICUAW [35], they support the findings of Fan et al. [12] who found that with every day of bed rest muscle strength is reduced by 3–11% in critically ill ARDS patients. In-bed immobilisation is a largely modifiable risk factor [43] and might be targeted with therapeutic interventions in critically ill patients at risk. However, current evidence on the benefit of early mobilisation is conflicting and needs further study [44]. Our randomised controlled trial likewise failed to reveal a superior intervention to increase strength at ICU discharge when comparing two different exercise regimes [22]. However, early mobilisation was part of both trial arms and thus not under direct investigation. Overall, more research is needed on how to overcome this exposure outcome overlap and what kind of intervention should be prescribed. The influence of female gender on muscle strength has also previously been described [20] and might be a result of reduced muscle mass in women [35]. As a consequence, women might need different treatment from men. This could include early screening to identify weakness and might mean a prolonged or more intensive period of rehabilitation.

The strength of this study lies in the performance-based measurements—performed by trained physiotherapists—in a general ICU population with a high ICUAW incidence and adds relevant information about the functional short-term outcomes in generally, critically ill patients. Furthermore, the 6-month timeframe has a high clinical relevance because health-related quality of life appears to plateau after this period [45]. There are also several limitations. First, this secondary analysis was limited by the availability of the existing data and we did not adjust the significance threshold for multiple comparisons. Accordingly, additional risk factors may have remained undiscovered or the identified factors may have been a random association. Second, a rather high drop-out rate at six months (41%) substantially limits the possible conclusions about these patients’ subsequent quality of life. Third, generalisability is limited due to a selective and single-centre sample. Fourth, while it seemed that patients with ICUAW recovered, this observational study can draw no conclusions about possible interventions. Given that post-ICU rehabilitation presently lacks evidence to improve quality of life [46], the pathways of recovery need further investigation. A prospective mixed-methods cohort study might reveal possible interventions to target for future studies. Fifth, we may have underestimated the incidence of ICUAW because participants that could not be tested due to their inability to follow commands may have scored as weak or functionally impaired. The recommended cut-offs for a clinical ICUAW diagnosis as applied here are further limited by the absence of proper validation and the lack of a gold standard. For these reasons, to fully understand the consequences of ICUAW, more research on the diagnosis of ICUAW is needed. Finally, the retrospective and exploratory nature of our analysis requires further investigation. To the best of our knowledge, there are no publicly available datasets to validate our results on ICUAW risk factors or the functional outcome of patients after an ICUAW diagnosis. Our results should therefore be validated in a prospective, longitudinal observational study with long-term follow-up.

Conclusions

Participants without ICUAW had superior functional performance at hospital discharge and shorter length of hospital stays when compared to participants with ICUAW. The increased strength was associated with early out-of-bed mobilisations during the ICU. However, after six months, participants with ICUAW reached similar health-related quality of life to participants without ICUAW at ICU discharge. This implies that recovery for critically ill, mechanically ventilated patients with ICUAW might be at least partly achieved. Nevertheless, these findings need to be validated in a prospective cohort study.

Supporting information

S1 Table. Timetable for primary and secondary outcome measures.

(PDF)

Click here for additional data file.^{(70.7KB, pdf)}

S2 Table. Baseline differences based upon completed versus missing MRC-SS.

(PDF)

Click here for additional data file.^{(20.5KB, pdf)}

S3 Table. Baseline differences based upon reason for missingness.

(PDF)

Click here for additional data file.^{(90.8KB, pdf)}

S4 Table. Sensitivity analysis comparing ICUAW versus non-ICUAW.

This includes German norm-based (1994) standardized sum-scores (T-values) for SF-36.

(PDF)

Click here for additional data file.^{(65.1KB, pdf)}

S5 Table. Correlation-matrix for functional outcomes at hospital discharge and SF-36.

(XLSX)

Click here for additional data file.^{(18.3KB, xlsx)}

S6 Table. Sensitivity analysis for the regression model either with or without the randomisation group or with or without the ADL variable.

(PDF)

Click here for additional data file.^{(90.4KB, pdf)}

S1 File. All regressions’ output.

(PDF)

Click here for additional data file.^{(205.8KB, pdf)}

Acknowledgments

We sincerely thank all physiotherapists and ICU staff for their dedicated care; this study would not have been possible without them. We also thank Alan Haynes for the statistical advice and for reviewing the manuscript.

Data Availability

The dataset and the analysis script are available from the “Bern Open Repository and Information System” database (https://boris.unibe.ch/140381/). The source code to reproduce the publication’s results is available at https://github.com/CTU-Bern/FuncAbil_icuaw.

Funding Statement

This research has been funded with the PhD Grant 2018 from the Swiss Foundation for Physiotherapy Science received by SE. The foundation had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0229725.r001

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Functional ability and quality of life in critical illness survivors with intensive care unit acquired weakness: a secondary analysis of a randomised controlled trial

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Reviewer #1: A secondary analysis of data from a prior randomized trial in which exercise related outcomes did not differ was used to compare intensive care unit acquired weakness (ICUAW) categorized as severe, moderate or none. The primary outcomes were the 6-Minute Walk Test and Functional Independence Measure at hospital discharge. Secondary outcomes included health-related quality of life after six months. At discharge outcomes differed significantly, but at 6 months the outcomes were not statistically different.

Minor revisions:

1- Lines 223 & 225: For clarity, replace 25% and 75% with first and third quartiles or simply indicate interquartile range.

2- Table 3: (A) For better clarity in presentation, consider adding a header row for each factor. For instance, above female/male add a row with the word “gender” in the first column. (B) For factors with more than two levels include the overall p-value for each factor rather than pairwise p-values. (C) In the title, clarify that crude results imply from univariate models and adjusted results are from a multivariate model.

Reviewer #2: ICUAW might put ICU-survivors on increased risk for long-term functional impairment and increased mortality. Too little is known about risk-factors, correlates and long-term consequences of ICUAW. The authors present a prospective cohort study with a six-month follow-up, that is conducted as a secondary analysis of the data of a RCT. The study is limited by a rather small study sample and a follow-up period of only six months. While the study questions are of high importance, I see some serious flaws in the conducted analyses that require revise.

My major concern is, what measures were used as predictors, and what measures where used as primary outcomes. While the primary outcomes of the RCT were measured at hospital discharge, the primary focus of this cohort study should be on the long-term outcomes measured at six months, since these are of higher relevance for patients. Specific subscales of the SF-36 that are most likely affected by ICUAW should be regarded as outcomes of primary interest (e.g. physical functioning, physical role functioning).

Concerning MRC-test and 6-min walk test, it is hard to distinguish these two as measures of two different “illness entities”, since Jolley et al (2016, Chest) discuss the 6-Min. walk test as an indicator of ICUAW in the early recovery period. The measurement at hospital discharge might also be the better choice to identify patients that have ICUAW, since the measurement at ICU discharge might still reflect aspects of the acute critical illness that might be highly unstable till the early recovery phase is achieved.

In my understanding, there are several indicators of ICUAW in the data of the RCT (MRC test, 6 min. walk test, grip strength etc.), some measured at ICU, other at hospital discharge. All of these might be suitable to predict 6-month follow-up status in an exploratory approach. The interrelatedness of all these indicators of ICUAW should be presented (maybe as a correlation-matrix in the supplement).

Although the FIM cannot be considered a direct indicator of ICUAW it would be of interest to see, if it is able to predict respective functional subscales of the SF36 at 6 months.

Consider to also predict the 6-min walk test (as an indicator of ICUAW during early recovery) from baseline and ICU-related risk-factors.

General comments:

I would recommend not to use categorized predictor variables (e.g. three categories of MRC-SS) in such a small study sample, since this severely can reduce power. Use continuous variables when possible and use categorization only for descriptive analysis.

If hospital length of stay should be considered as an outcome, then only the days after ICU-discharge should be counted.

T-values for SF-36 are calculated based on US-norms from 1990. There must be more recent and appropriate Norms available (e.g. there do exist recent German norm values).

Given the small sample size, multivariable analysis should not use to many variables. I suggest using a method that reduces the number of variables for multivariate analysis (e.g. only significant in univariate analysis).

To help interpret the results of the study, some considerations should be added on how big the test power was to detect relations between ICUAW indicators and SF-36 scales at 6-month follow up (referring to reasonable effect sizes obtained from earlier studies). It might be, that the power of the study was too small to find effects comparable to those that were previously reported. Based on such considerations “insignificant” findings in comparison to other studies should not be over-interpreted in the discussion. All results should be interpreted with caution because of the explorative nature of the study.

Authors stated that all data are fully available but did not report where.

**********

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PLoS One. 2020 Mar 4;15(3):e0229725. doi: 10.1371/journal.pone.0229725.r002

Author response to Decision Letter 0

1 Feb 2020

Responses to Reviewers

The reviewers’ comments are shown in bold type, our responses in plain type and changed passages of the manuscript are shown in cursive type. All pages and lines are in reference to the unmarked manuscript version: titled “Manuscript”.

Reviewer #1

A secondary analysis of data from a prior randomized trial in which exercise related outcomes did not differ was used to compare intensive care unit acquired weakness (ICUAW) categorized as severe, moderate or none. The primary outcomes were the 6-Minute Walk Test and Functional Independence Measure at hospital discharge. Secondary outcomes included health-related quality of life after six months. At discharge outcomes differed significantly, but at 6 months the outcomes were not statistically different.

We thank the reviewer for taking the time to review our manuscript and are grateful for the constructive and valuable feedback. All comments have been answered below.

Minor revisions:

1- Lines 223 & 225: For clarity, replace 25% and 75% with first and third quartiles or simply indicate interquartile range.

Tables 1 and 2 have been adapted as suggested by the reviewer to indicate interquartile range from 25% to 75%. We further clarified the section in the statistical analysis:

Page 6, lines 165-167:

“Continuous data could not be assumed normally distributed and are therefore presented as medians with interquartile ranges (IQR: first (25%) quartile to third (75%) quartile).”

(A) A header row has been added for each factor in Table 3 (page 13)

(B) We added a footnote for overall p-values, analysed with the Omnibus test, for the factor “mobilisation level in the ICU” (page 14, lines 295-296):

“a Overall p values for the factor “mobilisation level in the ICU” with Omnibus test: p=0.001 for crude and p<0.001 for adjusted effect”

Page 13, lines 292-293:

“Univariate (crude) and multivariate (adjusted) regression models for chosen ICU risk factors with MRC-SS as response.”

Reviewer #2

ICUAW might put ICU-survivors on increased risk for long-term functional impairment and increased mortality. Too little is known about risk-factors, correlates and long-term consequences of ICUAW. The authors present a prospective cohort study with a six-month follow-up, that is conducted as a secondary analysis of the data of a RCT. The study is limited by a rather small study sample and a follow-up period of only six months. While the study questions are of high importance, I see some serious flaws in the conducted analyses that require revise.

We thank the reviewer for taking the time to review our manuscript and are grateful for the constructive and valuable feedback. All comments have been answered below.

We acknowledge the reviewer’s concern that we did not focus sufficiently on long-term outcomes (SF-36). All subscales of the SF-36 are reported in Table 2, but to increase their visibility, we added a graph (Fig. 4) for the physical and mental health sum-scores of the SF-36. The manuscript has been changed (page 10, lines 243-245):

“Fig 3 further illustrates the distribution between the three MRC-SS groups for the Timed ‘Up & Go’ test and hospital length of stay after ICU discharge, while Fig 4 illustrates the distribution for the SF-36 physical and mental health sum-scores.”

Fig 4 title and legend (page 12, lines 280-282):

“Fig 4. Illustration of the SF-36 physical and mental health sum-scores per MRC-SS group.

Illustration of the SF-36 physical health sum-score (a) and mental health sum-score (b) with non-parametric Cuzick test accepting the null hypothesis of equal distributions.”

We further provide a correlation-matrix on the functional outcomes measured at hospital discharge with the SF-36 in the supplement (S5 Table) as suggested by the reviewer in one of the following questions.

We agree, that in principal the focus should be more on long-term outcomes. However, we a-priori chose to use the same short-term outcomes as in the original trial, because these were the endpoints with the least drop-outs and thus the most likely to be sufficiently powered. We therefore would not feel confident to change primary outcomes at this point. We added this limitation to the manuscript:

Page 16, lines 357-358:

“Lastly, our sample might have been too small and selective to reveal a relevant association of ICUAW with quality of life.”

Page 17, lines 393-394:

“Our results should therefore be validated in a prospective, longitudinal observational study with long-term follow-up.”

We agree that measuring weakness at ICU discharge is likely to be unstable and still influenced by the critical illness, which is exactly why we were interested in the subsequent outcome. As Jolley et al. (2016) discussed, there is very few data to support the assumption that ICUAW impacts subsequent physical function [1]. We therefore set out to explore this.

We also agree that the measurements at hospital discharge might provide additional important diagnostic/predictive value. However, we think it is important to distinguish that the 6MWT and MRC-SS test measure two rather different constructs as described by the WHO framework “International Classification of Functioning, Disability and Health” (ICF) for measuring health and disability [2]. Accordingly, the 6MWT is allocated to the domains of body functions (exercise tolerance function b455) and activities and participation (mobility d4 and walking short distances d4500) [3]. In contrast the MRC-SS is allocated to the domains of body functions (muscle functions b730-b749). Our data somewhat confirms this hypothesis through a low, positive correlation between the 6MWT and the MRC-SS (Spearman rho r=0.316, p=0.006). We think the second construct (MRC-SS) a better fit to identify ICUAW given its official diagnosis as a “generalised weakness developing after the onset of critical illness” [4: page 303]. We therefore chose to use the same diagnostic approach as suggested by others [4-6]. We acknowledge that the MRC-SS has its limitations due to its reliance on patient cooperation as stated in our manuscript (page 17, lines 387-390), but we believe that identification of patients with ICUAW should occur as early as possible – not only at hospital discharge. Indeed, our results seem to support an early diagnosis given its association with worsened short-term functional outcome.

Nevertheless, the 6MWT could have some diagnostic/predictive value for long-term outcomes. We do not know of any such previous studies. However, our data is probably underpowered to sufficiently investigate this. A correlation of the 6MWT and the FIM with the physical health sum-score of the SF-36 did not reveal any relevant correlation (6MWT: r=0.199, p=0.153, n=53, FIM: r=-0.088, p=0.533, n=53). We therefore did not explore this any further.

We agree with the reviewer, that it would be interesting to explore 6-month follow-up status based on functional outcomes (6MWT, FIM, Timed ‘Up & Go’ test, grip strength, quadriceps strength and MRC-SS) measured during the hospital stay. The manuscript has been revised and as suggested we added a correlation-matrix as a supplement (new S5 Table, the previous S5 Table has been renamed S6 Table):

Page 6, lines 158-160:

“We further explored whether functional performance at hospital discharge might be useful to predict 6-month quality of life.”

Page 7, lines 182-183:

“Correlations were investigated with non-parametric Spearman correlation coefficients.”

Page 10, lines 248-249:

“Functional performance at hospital discharge was not associated with quality of life (S5 Table).”

Page 14, lines 311-313:

However, contrary to our hypothesis, quality of life after six months was similar in participants with severe, moderate or no ICUAW and not associated with functional performance in the hospital.

The results seem likely to be underpowered due to the high drop-out rates at 6-month follow-up (28% 6-month mortality). This limits our conclusions substantially and has been discussed as a limitation:

Page 16, lines 357-358:

“Lastly, our sample might have been too small and selective to reveal a relevant association of ICUAW with quality of life.”

Although the FIM cannot be considered a direct indicator of ICUAW it would be of interest to see, if it is able to predict respective functional subscales of the SF36 at 6 months.

Indeed, there are studies that found that the FIM may be an excellent indicator of recovery [7]. The findings of this study can unfortunately not be directly compared to our results, but has been discussed on page 15, lines 347-353.

Given the newly added exploratory correlation-matrix in S5 Table, our data do not indicate that the FIM is able to predict any of the functional subscales of the SF-36 (correlations non-significant with r<0.2). Again, this might be a result of the high drop-out rates at six months (28% 6-month mortality in the whole cohort), which require us to be careful in interpreting these data. To make the limitations of an insufficiently powered 6-month outcome absolutely clear to the reader, we aggravated this major limitation in the manuscript:

Page 16, lines 357-358:

“Lastly, our sample might have been too small and selective to reveal a relevant association of ICUAW with quality of life.”

Page 16, lines 379-381:

“Second, a rather high drop-out rate at six months (41%) substantially limits the possible conclusions about these patients’ subsequent quality of life.”

Consider to also predict the 6-min walk test (as an indicator of ICUAW during early recovery) from baseline and ICU-related risk-factors.

This is an interesting point. As stated previously we believe that ICUAW should be identified as early as possible, the same would of course apply to functional disability (e.g. 6MWT at hospital discharge). We therefore explored the influence of the same baseline and ICU-related risk-factors on their influence on the 6MWT with a univariate (crude) and multivariate (adjusted) regression model.

We report the results here. None of the investigated risk factors were associated with 6MWT. This is somewhat surprising because normative values for females are generally lower than for males [8]. Nevertheless, we did not report results in the manuscript because we feel that this analysis is too exploratory, has not been previously planned and did probably not consider all relevant factors from a theoretical perspective. Recent evidence for example indicates that pre-existing comorbidities and ARDS patients (versus non-ARDS) might be much better predictors [9].

Univariate (crude) and multivariate (adjusted) regression models for chosen ICU risk factors with 6MWT as response,

n Crude effect (95% CI) p-value n Adjusted effect (95% CI) p-value

Gender

female Reference Reference

male 84 56.31 (-12.00 to 124.63) 0.110 82 58.74 (-11.34 to 128.82) 0.105

Group allocation

control group Reference Reference

experimental group 84 -18.58 (-85.94 to 48.78) 0.590 82 -1.93 (-73.78 to 69.91) 0.958

Illness severity

SOFA score 84 -8.34 (-17.53 to 0.84) 0.079 82 -7.81 (-17.83 to 2.20) 0.130

Length of ICU stay

Length of ICU stay at original hospital (days) 84 -1.84 (-5.31 to 1.63) 0.301 82 -1.57 (-5.43 to 2.29) 0.427

Activities of daily living (ADL)

Not restricted in ADL Reference Reference

Restricted in ADL 82 -49.47 (-171.69 to 72.74) 0.430 82 -60.89 (-184.43 to 62.64) 0.337

Mobilisation level in ICU

Mobilisation level in ICU: out-of-bed Reference Reference

Mobilisation level in ICU: in-bed 84 -114.42 (-237.45 to 8.61) 0.072 82 -111.50 (-235.58 to 12.59) 0.082

Mobilisation level in ICU: edge-of-bed 84 -33.40 (-104.92 to 38.12) 0.363 82 -21.81 (-103.70 to 60.08) 0.603

Table legend: Significance of the chosen variables in two regression models with 6MWT as response. Crude regression is just response (6MWT) and corresponding explanatory variable. Adjusted regression includes all listed explanatory variables. The robust estimations give the same results with non-significant bias’ tests (with the null: presence of the bias due to lack of robustness or outlying observations). None of the terms revealed non-linearity when fitting a regression model allowing non-linear dependence (via penalized splines) on explanatory variables.

General comments:

We agree with the reviewer that three categories of the MRC-SS would reduce power if analysed with Kruskal-Wallis test. However, the Cuzick test can test an ordered relationship (e.g. severe is worse than moderate which is worse than no weakness plus severe is worse than no weakness) without multiple tests [10]. It is therefore more powerful than the Kruskal-Wallis test, and this test has been recommended by our statistician (I.I.). It should be acknowledged, that MRC-SS is not a truly linear construct, since the differences between any two adjacent numbers are not necessarily equal. Furthermore, the cut-off for ICUAW has not been properly validated as described in our manuscript on page 17, lines 387-389. We conducted a sensitivity analysis comparing all participants with ICUAW versus non-ICUAW (see page 7, lines 185-188). Results are presented in S4 Table and described on page 10, lines 245-248:

“Sensitivity analysis comparing all participants with ICUAW versus non-ICUAW similarly confirmed a significant association of ICUAW with functional disability and prolonged hospital stay, and lack of association with 6-month health-related quality of life (S4 Table).”

If hospital length of stay should be considered as an outcome, then only the days after ICU-discharge should be counted.

The reviewer raises an important point. We corrected this outcome to include only days after ICU discharge. Accordingly, Table 2, Fig 3 and S4 table have been changed to report the new results, which do not change our results/conclusions.

Variable n All with complete MRC-SS n Severe weakness n Moderate weakness n No weakness p-value

Hospital length of stay after ICU discharge (days) 83 16.87 [11.16 - 26.92] 17 20.9 [15.83 - 30.73] 32 16.86 [13.07 - 27.10] 34 11.16 [7.35 - 19.74] 0.008

We further made the following changes to the manuscript:

Abstract, page 2, lines 44-48:

“Functional outcomes and length of hospital stay significantly differed in patients with severe, moderate to no weakness (6-Minute Walk test: p=0.013; 110m [IQR 75-240], 196m [90-324.25], 222.5m [129-378.75], Functional Independence Measure: p=0.001; 91[IQR 68-101], 113[102.5-118.5], 112[97-123], length of stay after ICU discharge: p=0.008; 20.9d [IQR 15.83-30.73], 16.86d [13.07-27.10], 11.16d [7.35-19.74]).”

Page 10, lines 234-236:

“Weakness at ICU discharge was significantly associated with functional disability at hospital discharge and subsequent length of hospital stay, but not with health-related quality of life after six months.”

T-values for SF-36 are calculated based on US-norms from 1990. There must be more recent and appropriate Norms available (e.g. there do exist recent German norm values).

Indeed, there are German norm values from 1994 available. However, the official manual of the SF-36 version 2 [11] explicitly recommends to use US normative values for studies not from Germany or to compare study results internationally: “CAUTION: To COMPARE your own results of the SF-36 with the results in present international or German publications, you have to calculate the summary scales for the SF-36 based on the US POPULATION NORM 1990!” [cited directly from the SPSS syntax]. Moreover, US normative values are comparable to German normative values [12]. We therefore chose to report US norm values in the original trial as well as for this manuscript. Nevertheless, we re-calculated German norm-based standardized sum-scores (T-values).

Comparisons per MRC-SS group with the Cuzick-test did not change results:

Variable n All with complete MRC-SS n Severe weakness n Moderate weakness n No weakness p-value

Physical health (sum-score) based on German-norm 1994 49 42.67 [36.54 - 49.56] 12 43.79 [33.96 - 47.72] 13 42.77 [29.02 - 48.91] 24 42.32 [37.57 - 49.59] 0.922

Mental health (sum-score) based on German-norm 1994 49 47.59 [41.69 - 55.54] 12 47.49 [42.79 - 58.09] 13 43.3 [32.87 - 48.88] 24 48.55 [43.08 - 55.59] 0.884

Given the above warnings, we report German normative values only in the sensitivity analysis in the supporting information (see S4 Table).

The following changes were made to the manuscript:

Page 12, lines 264-265 (Table 2 legend)

“SF-36 (version 2): worst score: 0, best score: 100, sum-score: T-values where the population mean is 50 and the SD is 10; based on US-population 1990. German norm-based (1994) standardized sum-scores (T-values) for SF-36 were similar to the US-population (data shown in sensitivity analysis in S4 Table).”

Page 23, lines 594-595 (description of the supporting information):

“S4 Table. Sensitivity analysis comparing ICUAW versus non-ICUAW. This includes German norm-based (1994) standardized sum-scores (T-values) for SF-36.”

We acknowledge the reviewers concern and agree that we should be cautious of overfitting. However, models were defined a priori on theoretical grounds based on previous evidence. Since point estimates are largely similar and confidence intervals overlap to a large degree, we feel that our adjusted models are reasonable, although they should be interpreted with caution. Additionally, there is a rule-of-thumb in linear regression that there should be at least 10 observations per model parameter [13]. With at least 80 observations and no more than 6 model parameters, this rule-of-thumb is satisfied.

We fully agree with the reviewer that the power to detect an effect of ICUAW on 6-month outcomes was probably too low and that all results should be interpreted with caution because of the explorative nature of the study. However, we strongly believe – and are reinforced by our statistician – that power analysis is for study planning and not useful as a tool to justify nonsignificant results because the power is directly related to the p-value as discussed by several authors [14-16].

We concede that our results might have been overinterpreted and therefore revised our limitations and stressed the need to confirm these results in a prospective clinical trial. We further searched for a data set that might help to validate our results. Unfortunately, we were unable to find such a publicly available data set. We therefore re-formulated the following limitations and conclusions of our manuscript:

Abstract, page 3, lines 57:

“Prospective trials are needed to validate these results.”

Page 14, lines 316-318:

“These results provide useful information about the functional ability and subsequent health-related quality of life in a general, mechanically ventilated ICU population after a critical illness period, but need to be validated in prospective studies.”

Page 14, lines 335-336:

“When comparing our results for health-related quality of life with the existing literature, we found some discrepancies.”

Page 15-16, lines 354-258:

“Our results might also have been confounded by non-responders who might have been too ill to return the questionnaire or influenced by false negative results especially in the ‘role physical’ domain where severely weak patients (50 [IQR 25-75]) had surprisingly higher values than non-weak patients (25 [0-50]). Lastly, our sample might have been too small and selective to reveal a relevant association of ICUAW with quality of life.”

Page 16, lines 378-381:

“Accordingly, additional risk factors may have remained undiscovered or the identified factors may have been a random association. Second, a rather high drop-out rate at six months (41%) substantially limits the possible conclusions about these patients’ subsequent quality of life.”

Page 17, lines 390-394:

“Finally, the retrospective and exploratory nature of our analysis requires further investigation. To the best of our knowledge, there are no publicly available datasets to validate our results on ICUAW risk factors or the functional outcome of patients after an ICUAW diagnosis. Our results should therefore be validated in a prospective, longitudinal observational study with long-term follow-up.”

Page 17, lines 403-404:

“Nevertheless, these findings need to be validated in a prospective cohort study.”

Authors stated that all data are fully available but did not report where.

We apologise for this miscommunication. Our data availability statement has been entered separately into the Editorial Manager. All data including codes will be accessible from the “Bern Open Repository and Information System”. If the manuscript is accepted, data and codes will be uploaded. This ensures that any amendments made during the review process are included in the codes and data. The assigned DOI will subsequently be forwarded to the journal. The statement is as follows:

“The dataset and the analysis script are available from the “Bern Open Repository and Information System” database (https://boris.unibe.ch/) under the DOI XXXXX.”

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4. Stevens RD, Marshall SA, Cornblath DR, Hoke A, Needham DM, de Jonghe B, et al. A framework for diagnosing and classifying intensive care unit-acquired weakness. Critical care medicine. 2009;37(10 Suppl):S299-308. Epub 2010/02/06. doi: 10.1097/CCM.0b013e3181b6ef67. PubMed PMID: 20046114.

5. Parker AM, Sricharoenchai T, Raparla S, Schneck KW, Bienvenu OJ, Needham DM. Posttraumatic stress disorder in critical illness survivors: a metaanalysis. Critical care medicine. 2015;43(5):1121-9. Epub 2015/02/06. doi: 10.1097/ccm.0000000000000882. PubMed PMID: 25654178.

6. Cuthbertson BH, Elders A, Hall S, Taylor J, MacLennan G, Mackirdy F, et al. Mortality and quality of life in the five years after severe sepsis. Critical care (London, England). 2013;17(2):R70. Epub 2013/04/17. doi: 10.1186/cc12616. PubMed PMID: 23587132; PubMed Central PMCID: PMCPMC4057306.

7. Weijs PJ, Looijaard WG, Dekker IM, Stapel SN, Girbes AR, Oudemans-van Straaten HM, et al. Low skeletal muscle area is a risk factor for mortality in mechanically ventilated critically ill patients. Critical care (London, England). 2014;18(2):R12. Epub 2014/01/15. doi: 10.1186/cc13189. PubMed PMID: 24410863; PubMed Central PMCID: PMCPmc4028783.

8. Enright PL, Sherrill DL. Reference equations for the six-minute walk in healthy adults. American journal of respiratory and critical care medicine. 1998;158(5 Pt 1):1384-7. Epub 1998/11/17. doi: 10.1164/ajrccm.158.5.9710086. PubMed PMID: 9817683.

9. Parry SM, Nalamalapu SR, Nunna K, Rabiee A, Friedman LA, Colantuoni E, et al. Six-Minute Walk Distance After Critical Illness: A Systematic Review and Meta-Analysis. Journal of intensive care medicine. 2019:885066619885838-. doi: 10.1177/0885066619885838. PubMed PMID: 31690160.

10. Dinglas VD, Aronson Friedman L, Colantuoni E, Mendez-Tellez PA, Shanholtz CB, Ciesla ND, et al. Muscle Weakness and 5-Year Survival in Acute Respiratory Distress Syndrome Survivors. Critical care medicine. 2017;45(3):446-53. Epub 2017/01/10. doi: 10.1097/ccm.0000000000002208. PubMed PMID: 28067712; PubMed Central PMCID: PMCPmc5315580.

11. Morfeld M, Kirchberger, I., Bullinger, M. SF-36: Fragebogen zum Gesundheitszustand.

12. Ellert U, Kurth B-M. Methodische Betrachtungen zu den Summenscores des SF-36 anhand der erwachsenen bundesdeutschen Bevölkerung. Bundesgesundheitsblatt - Gesundheitsforschung -Gesundheitsschutz. 2004;47(11):1027-32. doi: 10.1007/s00103-004-0933-1.

13. Harrell FE, Jr., Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in medicine. 1996;15(4):361-87. Epub 1996/02/28. doi: 10.1002/(sici)1097-0258(19960229)15:4<361::aid-sim168>3.0.co;2-4. PubMed PMID: 8668867.

14. Hoenig JM, Heisey DM. The Abuse of Power. The American Statistician. 2001;55(1):19-24. doi: 10.1198/000313001300339897.

15. Lenth RV. The University of Iowa 2007 [01.02.2020]. Available from: https://stat.uiowa.edu/sites/stat.uiowa.edu/files/techrep/tr378.pdf.

16. Goodman SN, Berlin JA. The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results. Annals of internal medicine. 1994;121(3):200-6. Epub 1994/08/01. doi: 10.7326/0003-4819-121-3-199408010-00008. PubMed PMID: 8017747.

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PLoS One. doi: 10.1371/journal.pone.0229725.r003

Decision Letter 1

Andre Scherag

13 Feb 2020

Functional ability and quality of life in critical illness survivors with intensive care unit acquired weakness: a secondary analysis of a randomised controlled trial

PONE-D-19-28826R1

Dear Dr. Eggmann,

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

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**********

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6. Review Comments to the Author

Reviewer #1: The authors thoroughly and clearly addressed my prior comments. Thank-you!

Reviewer #2: The authors have invested a lot of effort in improving the manuscript. Thank you very much for this.

My only recommendation left is a change to the title:

"Functional ability and quality of life in critical illness survivors with intensive care unit acquired weakness: a secondary analysis of a randomised controlled trial"

This reads as if only patients with weakness were included. A possible change could be:

"Effects of intensive care unit acquired weakness on functional ability and quality of life in critical illness survivors: a secondary analysis of a randomised controlled trial"

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PLoS One. doi: 10.1371/journal.pone.0229725.r004

Acceptance letter

Andre Scherag

20 Feb 2020

PONE-D-19-28826R1

Functional ability and quality of life in critical illness survivors with intensive care unit acquired weakness: a secondary analysis of a randomised controlled trial

Dear Dr. Eggmann:

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on behalf of

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Timetable for primary and secondary outcome measures.

(PDF)

Click here for additional data file.^{(70.7KB, pdf)}

S2 Table. Baseline differences based upon completed versus missing MRC-SS.

(PDF)

Click here for additional data file.^{(20.5KB, pdf)}

S3 Table. Baseline differences based upon reason for missingness.

(PDF)

Click here for additional data file.^{(90.8KB, pdf)}

S4 Table. Sensitivity analysis comparing ICUAW versus non-ICUAW.

This includes German norm-based (1994) standardized sum-scores (T-values) for SF-36.

(PDF)

Click here for additional data file.^{(65.1KB, pdf)}

S5 Table. Correlation-matrix for functional outcomes at hospital discharge and SF-36.

(XLSX)

Click here for additional data file.^{(18.3KB, xlsx)}

S6 Table. Sensitivity analysis for the regression model either with or without the randomisation group or with or without the ADL variable.

(PDF)

Click here for additional data file.^{(90.4KB, pdf)}

S1 File. All regressions’ output.

(PDF)

Click here for additional data file.^{(205.8KB, pdf)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(90.2KB, docx)}

Data Availability Statement

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PERMALINK

Functional ability and quality of life in critical illness survivors with intensive care unit acquired weakness: A secondary analysis of a randomised controlled trial

Sabrina Eggmann

Gere Luder

Martin L Verra

Irina Irincheeva

Caroline H G Bastiaenen

Stephan M Jakob

Roles

Abstract

Introduction

Methods

Results

Conclusions

Trial registration

Introduction

Methods

Design and setting

Study population and management

Data collection and measurements

Statistical analysis

Results

Fig 1. Study flow.

Table 1. Baseline demographics for participants with complete MRC-SS assessment.

Functional outcomes, hospital variables and quality of life

Table 2. Primary and secondary outcome-comparisons per MRC-SS group.

Fig 2. Illustration of the two primary outcomes per MRC-SS group.

Fig 3. Illustration of the Timed ‘Up & Go’ test and hospital length of stay per MRC-SS group.

Fig 4. Illustration of the SF-36 physical and mental health sum-scores per MRC-SS group.

Risk factors

Table 3. Univariate (crude) and multivariate (adjusted) regression models for chosen ICU risk factors with MRC-SS as response.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Andre Scherag

Roles

Author response to Decision Letter 0

Decision Letter 1

Andre Scherag

Roles

Acceptance letter

Andre Scherag

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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