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The Functional Trajectory in Frail Compared With Non-frail Critically Ill Patients During the Hospital Stay


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Background: Long-term outcome is determined not only by the acute critical illness but increasingly by the reduced functional reserve of pre-existing frailty. The patients with frailty currently account for one-third of the critically ill, resulting in higher mortality. There is evidence of how frailty affects the intrahospital functional trajectory of critically ill patients since prehospital status is often missing. Methods: In this prospective single-center cohort study at two interdisciplinary intensive care units (ICUs) at a university hospital in Germany, the frailty was assessed using the Clinical Frailty Scale (CFS) in the adult patients with critical illness with an ICU stay >24 h. The functional status was assessed using the sum of the subdomains “Mobility” and “Transfer” of the Barthel Index (MTB) at three time points (pre-hospital, ICU discharge, and hospital discharge). Results: We included 1,172 patients with a median age of 75 years, of which 290 patients (25%) were frail. In a propensity score-matched cohort, the probability of MTB deterioration till hospital discharge did not differ in the patients with frailty (odds ratio ( OR ) 1.3 [95% CI 0.8–1.9], p = 0.301), confirmed in several sensitivity analyses in all the patients and survivors only. Conclusion: The patients with frailty have a reduced functional status. Their intrahospital functional trajectory, however, was not worse than those in non-frail patients, suggesting a rehabilitation potential of function in critically ill patients with frailty.
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published: 04 November 2021
doi: 10.3389/fmed.2021.748812
Frontiers in Medicine | 1November 2021 | Volume 8 | Article 748812
Edited by:
Radmilo J. Jankovi ´
University of Niš, Serbia
Reviewed by:
Maria Vargas,
University of Naples Federico II, Italy
Lei Zhao,
Capital Medical University, China
Stefan J. Schaller
K. E. Fuest
Bettina Jungwirth
Manfred Blobner
Stefan J. Schaller
Specialty section:
This article was submitted to
Intensive Care Medicine and
a section of the journal
Frontiers in Medicine
Received: 28 July 2021
Accepted: 01 October 2021
Published: 04 November 2021
Fuest KE, Lorenz M, Grunow JJ,
Weiss B, Mörgeli R, Finkenzeller S,
Bogdanski R, Heim M, Kapfer B,
Kriescher S, Lingg C, Martin J, Ulm B,
Jungwirth B, Blobner M and
Schaller SJ (2021) The Functional
Trajectory in Frail Compared With
Non-frail Critically Ill Patients During
the Hospital Stay.
Front. Med. 8:748812.
doi: 10.3389/fmed.2021.748812
The Functional Trajectory in Frail
Compared With Non-frail Critically Ill
Patients During the Hospital Stay
K. E. Fuest 1†, Marco Lorenz 1, 2, Julius J. Grunow 2, Björn Weiss 2, Rudolf Mörgeli 2,
Sebastian Finkenzeller 1, Ralph Bogdanski 1, Markus Heim 1, Barbara Kapfer 1,
Silja Kriescher 1, Charlotte Lingg 1, Jan Martin 1, Bernhard Ulm 1, Bettina Jungwirth 1, 3†,
Manfred Blobner 1, 3† and Stefan J. Schaller 1, 2
1Department of Anesthesiology and Intensive Care, School of Medicine, Klinikum Rechts der Isar, Technical University of
Munich, Munich, Germany, 2Department of Anesthesiology and Operative Intensive Care Medicine, Charité –
Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,
3Department of Anesthesiology, Universitätsklinikum Ulm, Ulm, Germany
Background: Long-term outcome is determined not only by the acute critical illness
but increasingly by the reduced functional reserve of pre-existing frailty. The patients with
frailty currently account for one-third of the critically ill, resulting in higher mortality. There is
evidence of how frailty affects the intrahospital functional trajectory of critically ill patients
since prehospital status is often missing.
Methods: In this prospective single-center cohort study at two interdisciplinary intensive
care units (ICUs) at a university hospital in Germany, the frailty was assessed using the
Clinical Frailty Scale (CFS) in the adult patients with critical illness with an ICU stay >24 h.
The functional status was assessed using the sum of the subdomains “Mobility” and
“Transfer” of the Barthel Index (MTB) at three time points (pre-hospital, ICU discharge,
and hospital discharge).
Results: We included 1,172 patients with a median age of 75 years, of which 290
patients (25%) were frail. In a propensity score-matched cohort, the probability of MTB
deterioration till hospital discharge did not differ in the patients with frailty (odds ratio
(OR) 1.3 [95% CI 0.8–1.9], p=0.301), confirmed in several sensitivity analyses in all the
patients and survivors only.
Conclusion: The patients with frailty have a reduced functional status. Their intrahospital
functional trajectory, however, was not worse than those in non-frail patients, suggesting
a rehabilitation potential of function in critically ill patients with frailty.
Keywords: frailty, critical illness, outcome assessment, ICU, morbidity
The number of patients admitted to intensive care units (ICUs) increased within the past years
with an ongoing upward trend and an overproportion of the patients advanced in years (1,2).
Older patients are more likely frail, which is a multifaceted condition characterized by the loss
of physiologic and cognitive reserves (3,4). The observational studies suggest that the patients
with frailty currently account for up to one-third of the critically ill (5,6). Consequently, the
Fuest et al. Intrahospital Functional Trajectory
patient outcome is determined not only by the acute critical
illness but increasingly by the reduced functional reserve of pre-
existing frailty resulting in higher 30-day mortality (5,7,8). In
accordance, the likelihood to be discharged to a nursing home
is greater in the patients with frailty (9), if the critical illness is
survived. This might be caused by the higher odds of disability
in the activities in daily living (10,11) and increased functional
dependence (12,13). Despite this finding, the factors affecting
the recovery of physical function after a critical illness remain
poorly understood. The patient-level characteristics should be
evaluated as the recovery trajectories differ between the cohorts
in both the extent and speed of recovery of physical function. A
functional trajectory is used to describe this complex process by
measuring the changes in the functional status at different time
points (14,15).
While early mobilization might be an important element to
maintain the autonomy and mobility in the prior functionally
independent patients (1618), little is known about the functional
trajectory of the patients with frailty during the hospital stay (13,
19). Since information about the functional status and mobility
of the patients before their ICU admission is typically missing
(11,20), it is unknown if the functional decline is caused by frailty
itself, the critical illness, or the combination of both. This might
have been important implications for the resource allocations in
the acute care setting if the mortality is high and the functional
decline cannot be prevented (21,22).
This study aimed to describe the influence of pre-existing
frailty on the functional trajectory of patients with a critical illness
FIGURE 1 | STROBE diagram.
during their hospital stay. We hypothesized that the patients with
frailty have a greater deterioration of function compared with the
patients with non-frailty.
Study Design, Setting, and Participants
This study is a prospective observational monocentric cohort
study of two interdisciplinary ICUs of the Department of
Anesthesiology and Intensive Care at Klinikum rechts der Isar,
School of Medicine, Technical University of Munich, Germany
between April 2017 and May 2019. The Data were extracted
from our prospective database of the patients with critical illness
who had consented to participate. This prospective analysis was
registered at the Clinical Trials and approved by the Ethics
Committee of the Faculty of Medicine, Technical University of
Munich (528/18 from 22nd Dec 2016). The adults with >24 h
stay in the ICU were included, if the consent was obtained either
by the patient or legal representative according to the legislation.
There were no additional exclusion criteria.
Outcome Variables
There is no consented outcome measure for the functional status
of the patients with critical illness (23). As a substitute, the
functional status was therefore recorded with the corresponding
subdomains of the Barthel Index, which is an ordinal scale
incorporating 10 subdomains of the activities in daily life and
the most widely used activities of daily living scale (24,25). The
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Fuest et al. Intrahospital Functional Trajectory
points of the subdomains “mobility” and “transfer” of the Barthel
Index, each ranging between 0 and 15 (“Mobility-Transfer-
Barthel”, MTB) were added and represent the functional capacity
and gait independence of the patients with a minimum of 0 points
(functionally fully dependent) and a maximum of 30 points
(functionally independent) (26).
The primary outcome was the probability not to deteriorate
in functional status during the hospital stay, i.e., change of the
Barthel Score over time, using a baseline value representing
the functional status 2 weeks before the hospital admission
and at hospital discharge. The prehospital value was obtained
retrospectively through the interviews with the patients or their
relatives. At ICU and hospital discharge, the functional status was
obtained by the study staff. This resulted in a total of three time
points to evaluate the individual course of recovery to establish a
functional trajectory. The secondary outcome variables were the
functional status using the change of MTB till ICU and hospital
discharge, the MTB at ICU and hospital discharge, ICU mortality,
hospital mortality, ICU length of stay (LOS), and hospital LOS as
well as discharge disposition to home.
The factor of interest was frailty using the Clinical Frailty
Scale (CFS) (5,2729). The CFS 9 ranges from 1 “very fit”
to 9 “terminally ill” assuming frailty in case of category 5–
9 with excellent inter-rater reliability if used in the patients
with critical illness (5,10,30). The additional factors were
age, sex, the Charlson-Comorbidity Index (CCI) (31), and the
baseline descriptors at ICU admission, i.e., Sepsis-related Organ
Failure Assessment (SOFA) score and Acute Physiology And
Chronic Health Evaluation II (APACHE) (32,33), if the patient
was considered neurocritical care (yes/no), and if an elective
postoperative admission (yes/no).
Statistical Analyses
Data analysis was performed with R version 4.0.5 (Austria). The
continuous variables were presented as median [interquartile
range (IQR)]. The categorical variables were presented using
absolute numbers and frequencies.
Propensity matching was performed to balance the
influencing factors. A logistic regression modeling was used
to calculate the propensity of being frail or non-frail with the
factors, such as the patients’ age, sex, body mass index (BMI),
admission category and department, CCI, as well as SOFA score,
APACHE, and Glasgow Coma Scale (GCS) at ICU admission.
The propensity score matching was performed using an R
package “Matching” (34). We used a 1:N matching approach
with a starting caliper of 0.0001 and repetitive matchings with
an increasing caliper (35). After each matching routine, the
selected patients with non-frailty were excluded from the further
matchings. The procedure was stopped when the necessary
sample size was reached to prove the significance (p<0.05)
with a power of 80%. The sample size was calculated using
the univariate OR of 0.524 between all the patients with frailty
and non-frailty for the deterioration of the Mobility-Transfer-
Barthel till hospital discharge. Assuming a ratio of approximately
1:2 between the patients with frailty and non-frailty, we
calculated a necessary total number of 654. With a caliper of
0.0001, 28 patients were selected; with a caliper of 0.001, we
obtained a total of 173 patients; with a caliper of 0.01, 483
patients were obtained; and with a caliper of 0.1, we exceeded
the necessary threshold obtaining 687 patients. The stepwise
resulting subcohorts were not comparable regardless of the
caliper chosen when a standardization mean difference (SMD)
<0.1 between the groups is required for all the cofactors. The
effect sizes of all the endpoints, therefore, were adjusted for these
cofactors using multivariate conditional regression.
TABLE 1 | The patient characteristics of two interdisciplinary surgical intensive
care units (ICUs).
Patient characteristics Frail (n=290) Non-frail (n=882)
Malea131 (45.2) 332 (37.6)
BMIa,b (kg/m2) 25.0 [22.5–27.9] 25.6 [23.3–27.8]
Missing 12 (4.1) 46 (5.2)
Underweight 16 (5.5) 35 (4.0)
Normal 124 (42.8) 347 (39.3)
Overweight 98 (33.8) 324 (36.7)
Obese 40 (13.8) 130 (14.7)
Age (years)a75 [66–82] 65 [54–75]
50 19 (6.6) 177 (20.1)
51–65 52 (17.9) 278 (31.5)
66–80 126 (43.4) 324 (36.7)
>80 93 (32.1) 103 (11.7)
Admission froma
Home 170 (58.6) 578 (65.5)
Hospital 97 (33.4) 298 (33.8)
Nursing home 22 (7.6) 1 (0.1)
Unknown 1 (0.3) 5 (0.6)
GCSa,b 13 [5–15] 14 [7–15]
APACHE 2a,b 18 [13–24] 14 [9–19]
SOFAa,b 7 [4.2–10] 6 [4–9]
CCIa,b 3 [1–4] 1 [0–2]
Admission reasona
Sepsis 63 (21.7) 83 (9.4)
Polytrauma 2 (0.7) 38 (4.3)
TBI 20 (6.9) 128 (14.5)
Non-traumatic brain pathology 61 (21.0) 257 (29.1)
Postoperative 49 (16.9) 163 (18.5)
Cardiac 26 (9.0) 49 (5.6)
Pulmonary 119 (41.0) 226 (25.6)
Other 48 (16.6) 134 (15.2)
Surgical 130 (44.8) 352 (39.9)
Neurological and neurosurgical 124 (42.8) 461 (52.3)
Medical 29 (10.0) 41 (4.6)
Other 7 (2.4) 28 (3.2)
aData are n (%) or median [interquartile range (IQR)].
bBMI indicates body mass index; SOFA, Sequential Organ Failure Assessment; APACHE
II, Acute Physiology and Chronic Health Evaluation; ICU, intensive care unit; GCS,
Glasgow Coma Scale; CCI, Charlson Comorbidity Index; TBI, Traumatic brain injury.
Frontiers in Medicine | 3November 2021 | Volume 8 | Article 748812
Fuest et al. Intrahospital Functional Trajectory
We performed several sensitivity analyses: first, the primary
analysis was repeated in the survivors only in the propensity
matched cohort. Second, a logistic regression model for the
primary endpoint was used with all the patients. As an
exploratory analysis, MTB over time using a multivariate mixed
model with the clinically relevant covariates was applied. The
independent variables were the same factors as in the propensity
score matching, the points in time obtaining the MTB, and the
interactions terms of the factor frailty and these points in time.
The covariates used were tested for collinearity calculating the
variance inflation factor. Missing collinearity was assumed with
a variance inflation factor <5; otherwise, one of the factors had
to be omitted in the adjusted analysis.
Between April 1, 2017, and May 31, 2019, we included 1,172
patients (Figure 1). The median age was 68 [56–77] years,
of which 290 patients (25%) were assessed as frail (CFS
levels 5–9). Further patient characteristics are presented in
TABLE 2 | Characteristics of the propensity score-matched patients.
Not selected Selected Not selected
SMD pFrail
Malea141 (34.0) 189 (41.5) 98 (42.2) 0.014 0.924 26 (53.1)
BMI (kg/m2)a,b 25.4 [23.3–27.8] 25.7 [23.2–28.5] 25.4 [22.7–28.4] 0.017 0.309 24.2 [22.2–27.4]
Missing 14 (3.4) 21 (4.6) 13 (5.6) 3 (6.1)
Underweight 173 (41.7) 170 (37.4) 95 (40.9) 25 (51.0)
Normal 156 (37.6) 162 (35.6) 81 (34.9) 15 (30.6)
Overweight 58 (14.0) 70 (15.4) 37 (15.9) 3 (6.1)
Obese 14 (3.4) 32 (7.0) 6 (2.6) 3 (6.1)
Age (years)a56.0 [43.0–69.0] 71.0 [62.0–78.0] 74.0 [64.8–81.2] 0.170 0.003 80.0 [72.0–84.0]
50 145 (34.9) 31 (6.8) 18 (7.8) 1 (2.0)
51–65 140 (33.7) 131 (28.8) 47 (20.3) 4 (8.2)
66–80 104 (25.1) 216 (47.5) 101 (43.5) 20 (40.8)
>80 26 (6.3) 77 (16.9) 66 (28.4) 24 (49.0)
Admission froma0.099 0.619
Home 275 (66.3) 299 (65.7) 148 (63.8) 15 (30.6)
Hospital 138 (33.3) 152 (33.4) 81 (34.9) 15 (30.6)
Nursing home 0 (0.0) 1 (0.2) 2 (0.9) 19 (38.8)
Unknown 2 (0.5) 3 (0.7) 1 (0.4) 0 (0.0)
GCSa,b 14.0 [7.0–15.0] 14.0 [7.0–15.0] 13.0 [6.0–15.0] 0.079 0.066 9.0 [4.0–15.0]
APACHE 2a,b 11.0 [7.0–17.0] 17.0 [11.0–21.0] 18.0 [12.0–23.2] 0.188 0.038 20.0 [15.0–24.0]
SOFAa,b 6.0 [3.0–8.0] 7.0 [4.0–9.0] 7.0 [4.0–10.0] 0.094 0.228 9.0 [5.0–11.0]
CCIa,b 0.0 [0.0–1.0] 2.0 [0.0–3.0] 2.0 [1.0–4.0] 0.234 <0.001 4.0 [2.0–6.0]
Admission reasona*
Sepsis 25 (6.0) 54 (11.9) 49 (21.1) 0.251 0.002 14 (28.6)
Polytrauma 25 (6.0) 13 (2.9) 1 (0.4) 0.192 0.065 1 (2.0)
TBIb70 (16.9) 58 (12.7) 13 (5.6) 0.249 0.005 6 (12.2)
Non-traumatic brain pathology 153 (36.9) 102 (22.4) 48 (20.7) 0.042 0.674 11 (22.4)
Postoperative 73 (17.6) 85 (18.7) 44 (19.0) 0.007 1.000 4 (8.2)
Cardiac 16 (3.9) 33 (7.3) 20 (8.6) 0.051 0.628 3 (6.1)
Pulmonary 79 (19.0) 144 (31.6) 91 (39.2) 0.159 0.058 21 (42.9)
Other 54 (13.0) 78 (17.1) 40 (17.2) 0.003 1.000 7 (14.3)
Specialty* 0.078 0.818
Surgical 133 (32.0) 211 (46.4) 105 (45.3) 22 (44.9)
Neurocritical 255 (61.4) 203 (44.6) 104 (44.8) 18 (36.7)
Medical 11 (2.7) 30 (6.6) 19 (8.2) 7 (14.3)
Other 16 (3.9) 11 (2.4) 4 (1.7) 2 (4.1)
aData are n (%)—mean ±SD or median [IQR] SMD—standardized mean difference. Propensity matching was performed with the factors mentioned below in the cohort of all the
patients. Reference for sex is male.
bBMI indicates body mass index; SOFA, Sequential Organ Failure Assessment; APACHE II, Acute Physiology and Chronic Health Evaluation; ICU, intensive care unit; GCS, Glasgow
Coma Scale; CCI, Charlson Comorbidity Index; TBI, Traumatic brain injury. *Not used for matching.
Frontiers in Medicine | 4November 2021 | Volume 8 | Article 748812
Fuest et al. Intrahospital Functional Trajectory
TABLE 3 | Primary and secondary outcomes in the propensity score matched cohort.
Variable Non-frail
patients (n=455)
patients (n=232)
Univariate analysis Multivariate analysis
P-value Effect size P-value Effect size
Primary outcome
MTB deterioration till hospital discharge 360 (79.1) 192 (82.8) 0.301 1.3 [0.8–1.9] 0.675 1.1 [0.7–1.7]
Secondary outcome
MTB deterioration till ICU discharge 439 (98.0) 203 (90.2) <0.001 0.2 [0.1–0.4] <0.001 0.2 [0.1–0.4]
1MTB points till ICU discharge 20 [30 to 5] 15 [25 to 5] 0.012 5 [5 to 5]
1MTB points till hospital discharge 25 [30 to 20] 20 [25 to 10] <0.001 5 [5 to 0]
ICU length of stay (days) 10 [5–22] 11 [6–23] 0.184 1 [2 to 0]
Hospital length of stay (days) 27 [16–44] 31 [17–47] 0.102 4 [6 to 1]
Mortality (ICU) 99 (21.8) 63 (27.2) 0.139 1.3 [0.9–1.9] 0.663 1.1 [0.7–1.6]
Mortality (hospital) 136 (29.9) 104 (44.8) <0.001 1.9 [1.4–2.6] 0.003 1.7 [1.2–2.4]
Discharge home 128 (28.1) 32 (13.8) <0.001 0.4 [0.3–0.6] 0.675 0.4 [0.3–0.7]
MTB, Mobility-Transfer-Barthel, sum score of the subdomain Mobility and Transfer of the Barthel score.
Table 1. Using the propensity score matching, 687 patients were
selected, of which 232 were frail and 455 non-frail (Table 2 and
Supplementary Table 1 in the Appendix). Applying the same
criteria to the survivors, only lead to 393 patients of which 125
were frail (Supplementary Tables 2,3in the Appendix).
Primary Outcome
Deterioration in MTB occurred in 79% of patients with non-
frailty vs. 83% of patients with frailty, an unaltered probability
of deterioration in the patients with frailty (OR 1.3 [0.8–1.9], p
<0.301; Table 3) in the propensity matched cohort (as shown in
Figure 2). The sensitivity analysis in that cohort of the survivors
revealed similar results (OR 1.0 [0.6–1.6], p=1.0), as shown in
Supplementary Table 4 in the Appendix. This was confirmed in
a further sensitivity analysis using the logistic regression in all the
patients (OR adj. 0.9 [0.6–1.4], p=0.614), as shown in Figure 2
and Supplementary Table 5 in the Appendix and survivors only
(OR adj. 1.1 [0.7–1.8], p=0.642), as shown in Figure 2 and
Supplementary Table 6 in the Appendix.
Secondary Outcomes
The probability of MTB deterioration till ICU discharge was
significantly reduced in the patients with frailty (OR 0.2 [0.1–
0.4], p<0.001, Table 3, and Supplementary Tables 4,7,8in
the Appendix). The functional trajectory, i.e., the decrease of
the MTB till ICU (20 [95% CI 30 to 5] vs. 15 [25
to 5], p<0.012) and hospital discharge (25 [30 to 20]
vs. 20 [25 to 10], p0.0001) was significantly more
pronounced in the patients with non-frailty vs. patients with
frailty, respectively (Table 3 and Supplementary Table 4 in the
Appendix). ICU and hospital LOS did differ significantly between
the patients with frailty and non-frailty in the propensity matched
cohort [10 (5–22) vs. 11 (6–23) days, p=0.184 and 27 (16–
44) vs. 31 (17–47) days, p=0.102, respectively-Table 3 and
Supplementary Table 4 in the Appendix] and in the entire
cohort (10 vs. 10 days, p=0.19 and 28 vs. 25 days, p=
0.142, respectively-Supplementary Table 9 in the Appendix).
This effect could not be validated in the adjusted multivariate
analysis. The overall ICU mortality was 29.5% (281/1172). In
the propensity matched cohort, there was no difference in the
ICU mortality between the non-frail and patients with frailty
(22 vs. 27%, p=0.139, Table 3), while there was a significant
difference in the complete cohort (18 vs. 30%, OR 1.9 [1.4–2.6], p
<0.001, Supplementary Table 9 in the Appendix). The results of
hospital mortality were similar, with an overall mortality of 48%
(361/1,172) and with a significant difference in the propensity
matched cohort (30 vs. 44%, p0.001, Table 3). The patients
with non-frailty were discharged home more often (28 vs. 14%,
p0.001, Table 3 and Supplementary Table 9 in the Appendix).
Exploratory Analysis
Our exploratory analysis using MTB over time confirmed the
primary analysis, i.e., the patients with frailty had a lower
functional status, however, the decrease of the MTB over time
till ICU and hospital discharge was significantly less pronounced
in the propensity score cohort (Supplementary Table 10 in the
Appendix) and in all the patients (Supplementary Table 11
in the Appendix) and survivors (Supplementary Table 12 in
the Appendix).
This prospective observational study refutes the assumption that
pre-existing frailty deteriorates the functional status to a greater
extent, i.e., the functional trajectory of critical care patients with
frailty was not worse compared with the patients with non-
frailty when adjusted for age, comorbidity, and the triggering
reason for intensive care. Actually, the pre-existing differences
in the functional status of patients with frailty and non-frailty
converged at hospital discharge, indicating that intensive care is
justified in patients with pre-existing frailty as well.
Approximately 25% of the patients were frail when admitted
to our ICU for at least 24 h. They had more comorbidities as
defined by the CCI, had more insufficient organ systems as
indicated by a higher SOFA score, and the overall severity of
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Fuest et al. Intrahospital Functional Trajectory
FIGURE 2 | The intrahospital trajectory of the functional status in the patients
with frailty vs. non-frailty at three timepoints (“Hospital Admission,” “ICU
Discharge,” and “Hospital Discharge”). The functional status is measured as
Barthel-Mobility-Index consisting of the two subdomains “Mobility” and
“Transfer” of the Barthel-Index, ranging from 0 to 30
(“Mobility-Transfer-Barthel”, MTB). Three patient groups are presented: all
patients (n=1,172), survivors only (n=361) and after selection the
propensity-matched cohort (n=687). The red boxplots mark the patients with
non-frailty and blue the patients with frailty.
their diseases was more profound as scored by the APACHE
II. Accordingly, the mortality was higher in this patient sub-
cohort. These data are in accordance with the German sub
analysis of the VIP1 Trial, which included only patients >80
years of age (5). Accounting for more than 50% of patients with
frailty indicated a strong relationship between age and reduction
of the physiological reserve. Notwithstanding, LOS in the ICU
was longer in our cohort when compared with the subcohort
of the German VIP Trial (9 vs. 3 days) most likely due to our
inclusion criteria of >24 h ICU stay as well as a higher portion of
neurocritical care patients.
The patients with frailty expectedly had a reduced functional
status during the complete trajectory, i.e., significantly lower
MTB. This is in accordance with the other observational
studies, which additionally demonstrated increased peri- and
postoperative complication rates, morbidity, and mortality (10,
12,27,36). Furthermore, Bagshaw et al. suggested that the pre-
existing frailty impaired the long-term outcome of ICU survivors,
as one-third of their 421 patients reported a reduced health
related quality of life with reduced mobility in the physical
component score at 6 and 12 months after critical illness (20).
In another study in more than 1,000 patients, frailty was again
associated with an increased disability after critical illness (11).
In both studies, the parameters of functionality were obtained
before the onset of critical illness. Brummel et al. investigated
the functional status at 3- and 12-month after critical illness,
which did not evaluate the influence of intensive care on the
clinical outcome. Similarly, in the study from Bagshaw et al.,
the outcome was assessed at 6 and 12 months. Furthermore,
the authors mentioned a limitation that they were not able to
integrate the baseline functional measures, such as mobility.
Therefore, the conclusion that the functional status deteriorated
during intensive care is not justified due to frailty based on
those data.
More recently, Ferrante et al. also reported that the patients
with frailty 70 years old had higher mortality when becoming
critically ill (8). Those patients with frailty had an increased
disability when compared with the patients with non-frailty.
However, the trajectory showed no difference between the
patients with frailty and non-frailty. This is similar to our
observation in the patients with frailty, whose functional
status was also decreased but their trajectory was at least
not worse compared with the patients with non-frailty. This
also accounts for the long-term outcome study of the same
group (37).
The observation of partly improved or unaffected functional
status in the patients with frailty who survived their critical illness
demands critical consideration. It can be speculated that in older
patients, depression, and social isolation often lead to reduced
daily activities and accumulation of disability (37). In intensive
care, those patients are exposed to stimuli by the caregivers and to
early mobilization therapy resulting in the improvement of their
functional status (19).
The strength of this study is its prospective approach and the
high number of patients included, limited by in-hospital data
only. Although this was a single-center study, our cohort showed
a heterogeneous group of adult patients regarding diagnosis or
prognosis of the disease suggesting generalizability. Assessing the
prehospital functional status makes this study unique compared
with others and offers new perspectives in understanding the
trajectory of critically ill patients with frailty. However, the
prehospital frailty status was assessed retrospectively which is
a limitation. Although we implemented detailed and recurrent
training of our study staff, the assessment depends on the
ability to either correctly recall the prehospital status by the
patients or to adequately know the status by proxy (memory and
information bias). This important problem is not satisfactorily
answered yet. The upcoming results of the ASTON study
(NCT03785444) will likely improve our insight on assessing the
prehospital functional status in patients with critical illness. Until
then we must accept this as a limitation. Since the majority of our
Frontiers in Medicine | 6November 2021 | Volume 8 | Article 748812
Fuest et al. Intrahospital Functional Trajectory
patients are surgical or trauma, the results should be validated in
the medical ICU patients as well.
Addressing the functional outcome is a current focus after
surviving the critical illness. Due to the scaling and granularity
of the chosen MTB, subtle nuances of the functional outcome
might be missed. Since there is no defined core outcome set for
the functional outcomes in critical illness yet, we considered the
subdomains of the Barthel-Index a suitable option, as it is easy
to assess, reproducible for the caregivers, and relevant for the
patients (24,25).
Performing propensity score matching reduced the cohort of
the primary analysis considerably. The factors leading to non-
selection in propensity scoring were admission from a nursing
home, an advanced age, a low GCS, a high APACHE II score, and
a high level of comorbidities represented by CCI in the patients
with frailty. In the patients with non-frailty, younger age and a
low CCI (showing a healthy overall status) lead to non-selection.
Nevertheless, this approach strengthens the conclusion that the
effect is due to frailty itself and reduces the likelihood of bias. The
assumed risk of limited generalizability is countervailed by the
confirmation of all the results in the sensitivity and exploratory
analysis performed in the complete cohort.
In conclusion, the patients with frailty have a reduced functional
status. Their intrahospital functional trajectory, however, is no
worse than those in the patients with non-frailty. Even more, our
data suggests a significant rehabilitation potential of functional
mobility in the patients with frailty if they survive.
Data can be obtained from the corresponding author
on reasonable scientific request and as long as German
data protection law can be complied with. Requests to
access the datasets should be directed to Stefan Schaller,
The studies involving human participants were reviewed and
approved by Ethics Committee of the Faculty of Medicine,
Technical University of Munich. The patients/participants
provided their written informed consent to participate in
this study.
SJS is the principal investigator and developed the protocol and
BU is the study statistician. SJS and MB were involved in the
ethical approval. KEF, JJG, BW, RM, BU, BJ, MB, and SJS were
involved in the analysis and interpretation of the data. KEF, SF,
ML, RB, MH, BK, SK, CL, JM, and SJS were involved in the data
acquisition and quality assurance. All authors critically revised
the manuscript and approved its final version.
We would like to thank the nurses, physiotherapists, and
physicians working at the ICUs of the Department of
Anesthesiology and Intensive Care, Klinikum rechts der Isar,
School of Medicine, Technical University of Munich, Germany
for their support in conducting the study. Furthermore, we
appreciate the support of Prof. Dr. G. Schneider, Head of
the Department.
The Supplementary Material for this article can be found
online at:
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Conflict of Interest: BW reports personal fees from Orion Pharma Ltd and
national (DAAD) and international grants (ESICM) outside the submitted
work. BJ received honoraria for giving lectures from Pulsion Medical Systems
SE (Feldkirchen, Germany). MB received research support from MSD (Haar,
Germany) not related to this manuscript, received honoraria for giving lectures
from GE Healthcare (Helsinki, Finland) and Grünenthal (Aachen, Germany).
SS reports grants and non-financial support from ESICM (Brussels, Belgium),
Fresenius (Germany), Liberate Medical LLC (Crestwood, USA), Reactive Robotics
GmbH (Munich, Germany), STIMIT AG (Nidau, Switzerland) as well as from
Technical University of Munich, Germany, from national (e.g. DGAI) and
international (e.g. ESICM) medical societies (or their congress organizers) in the
field of anesthesiology and intensive care, personal fees and non-financial support
from Bavarian Medical Association, all outside the submitted work; SS holds
stocks in small amounts from Alphabeth Inc., Bayer AG, Rhön-Klinikum AG, and
Siemens AG. These did not have any influence on this study.
The remaining authors declare that they have no competing interests.
Publisher’s Note: All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations, or those of
the publisher, the editors and the reviewers. Any product that may be evaluated in
this article, or claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Copyright © 2021 Fuest, Lorenz, Grunow, Weiss, Mörgeli, Finkenzeller, Bogdanski,
Heim, Kapfer, Kriescher, Lingg, Martin, Ulm, Jungwirth, Blobner and Schaller. This
is an open-access article distributed under the terms of the Creative Commons
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Frontiers in Medicine | 8November 2021 | Volume 8 | Article 748812
... We found a higher incidence of ICU-AW in older patients, but it does not show a significant difference in the regression analysis. Early rehabilitation therapy has been shown to be an important mean of reducing the occurrence of ICU-AW, but there are no differences in the ICU-AW and no ICU-AW populations in terms of participation in rehabilitation, initiation and duration of rehabilitation, and patterns of rehabilitation in our experiments (19)(20)(21)(22). It might be due to the very small number of people involved in rehabilitation and the fact that we started rehabilitation late in most patients. ...
Full-text available
Background Intensive care unit-acquired weakness (ICU-AW) is common in critical illness patients and is well described. Extracorporeal membrane oxygenation (ECMO) is used as a life-saving method and patients with ECMO support often suffer more risk factors of ICU-AW. However, information on the frequency and clinical characteristics of ICU-AW in patients with ECMO support is lacking. Our study aims to clarify the frequency and characteristics of ICU-AW in ECMO patients. Methods We conducted a retrospective study, ICU-AW was diagnosed when patients were discharged with a Medical Research Council (MRC) sum score <48. Clinical information was collected from the case report forms. Univariable analysis, LASSO regression analysis, and logistic regression analysis were used to analyze the clinical data of individuals. Results In ECMO population, 40 (80%) patients diagnosed with ICU-AW. On univariable analysis, the ICU-AW group had higher Acute Physiology and Chronic Health Evaluation II (APACHE II) [13.9 (6.5–21.3) versus 21.1 (14.3–27.9), p = 0.005], longer deep sedation time [2 (0–7) versus 6.5 (3–11), p = 0.005], longer mechanical ventilation time [6.8 (2.6–9.3) versus 14.3 (6.6–19.3), p = 0.008], lower lowest albumin [26.7 (23.8–29.5) versus 22.1 (18.5–25.7), p < 0.001]. The LASSO analysis showed mechanical ventilation time, deep sedation time, deep sedation time during ECMO operation, APACHE II, and lowest albumin level were independent predictors of ICU-AW. To investigate whether ICU-AW occurs more frequently in the ECMO population, we performed a 1:1 matching with patients without ECMO and found there was no difference in the incidence of ICU-AW between the two groups. Logistic regression analysis of combined cohorts showed lowest albumin odds ratio (OR: 1.9, p = 0.024), deep sedation time (OR: 1.9, p = 0.022), mechanical ventilation time (OR: 2.0, p = 0.034), and APACHE II (OR: 2.3, p = 0.034) were independent risk factors of ICU-AW, but not ECMO. Conclusion The ICU-AW was common with a prevalence of 80% in the ECMO population. Mechanical ventilation time, deep sedation time, deep sedation time during ECMO operation, APACHE II, and lowest albumin level were risk factors of ICU-AW in ECMO population. The ECMO wasn’t an independent risk factor of ICU-AW.
Full-text available
In view of the globally evolving Coronavirus Disease (COVID-19) pandemic, German hospitals rapidly expanded their intensive care capacities. However, it is possible that even with an optimal use of the increased resources, these will not suffice for all patients in need. Therefore, recommendations for the allocation of intensive care resources in the context of the COVID-19 pandemic have been developed by a multidisciplinary authors group with support of eight scientific medical societies. The recommendations for procedures and criteria for prioritisations in case of resource scarcity are based on scientific evidence, ethico-legal considerations and practical experience. Medical decisions must always be based on the need and the treatment preferences of the individual patient. In addition to this patient-centred approach, prioritisations in case of resource scarcity require a supra-individual perspective. In such situations, prioritisations should be based on the criterion of clinical prospect of success in order to minimize the number of preventable deaths due to resource scarcity and to avoid discrimination based on age, disabilities or social factors. Assessment of the clinical prospect of success should take into account the severity of the current illness, severe comorbidities and the patient’s general health status prior to the current illness.
Full-text available
Purpose: Gait independence is one of the most important factors related to returning home from the hospital for patients treated in the intensive care unit (ICU), but the factors affecting gait independence have not been clarified. This study aimed to determine the factors affecting gait independence at hospital discharge using a standardized early mobilization protocol that was shared by participating hospitals. Materials and methods: Patients who entered the ICU from January 2017 to March 2018 were screened. The exclusion criteria were mechanical ventilation < 48 hours, age < 18, loss of gait independence before hospitalization, being treated for neurological issues, unrecoverable disease, unavailability of continuous data, and death during ICU stay. Basic attributes, such as age, ICU length of stay, information on early mobilization while in the ICU, Medical Research Council (MRC) sum-score at ICU discharge, incidence of ICU-acquired weakness (ICU-AW) and delirium, and the degree of gait independence at hospital discharge, were collected. Gait independence was determined using a mobility scale of the Barthel Index, and the factors that impaired gait independence at hospital discharge were investigated using a Cox proportional hazard regression analysis. Results: One hundred thirty-two patients were analyzed. In the univariate analysis, age, APACHE II score, duration of mechanical ventilation, ICU length of stay, incidence of delirium, and MRC sum-score at ICU discharge were extracted as significant. In the multivariate analysis, age (p = 0.014), MRC sum-score < 48 (p = 0.021), and delirium at discharge from ICU (p < 0.0001) were extracted as significant variables. Conclusions: We found that age and incidence of ICU-AW and delirium were significantly related to impaired gait independence at hospital discharge.
Full-text available
Background In intensive care units (ICU) octogenarians become a routine patients group with aggravated therapeutic and diagnostic decision-making. Due to increased mortality and a reduced quality of life in this high-risk population, medical decision-making a fortiori requires an optimum of risk stratification. Recently, the VIP-1 trial prospectively observed that the clinical frailty scale (CFS) performed well in ICU patients in overall-survival and short-term outcome prediction. However, it is known that healthcare systems differ in the 21 countries contributing to the VIP-1 trial. Hence, our main focus was to investigate whether the CFS is usable for risk stratification in octogenarians admitted to diversified and high tech German ICUs. Methods This multicentre prospective cohort study analyses very old patients admitted to 20 German ICUs as a sub-analysis of the VIP-1 trial. Three hundred and eight patients of 80 years of age or older admitted consecutively to participating ICUs. CFS, cause of admission, APACHE II, SAPS II and SOFA scores, use of ICU resources and ICU- and 30-day mortality were recorded. Multivariate logistic regression analysis was used to identify factors associated with 30-day mortality. ResultsPatients had a median age of 84 [IQR 82–87] years and a mean CFS of 4.75 (± 1.6 standard-deviation) points. More than half of the patients (53.6%) were classified as frail (CFS ≥ 5). ICU-mortality was 17.3% and 30-day mortality was 31.2%. The cause of admission (planned vs. unplanned), (OR 5.74) and the CFS (OR 1.44 per point increase) were independent predictors of 30-day survival. Conclusions The CFS is an easy determinable valuable tool for prediction of 30-day ICU survival in octogenarians, thus, it may facilitate decision-making for intensive care givers in Germany. Trial registrationThe VIP-1 study was retrospectively registered on (ID: NCT03134807) on May 1, 2017.
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Purpose: To document and analyse the decision to withhold or withdraw life-sustaining treatment (LST) in a population of very old patients admitted to the ICU. Methods: This prospective study included intensive care patients aged ≥ 80 years in 309 ICUs from 21 European countries with 30-day mortality follow-up. Results: LST limitation was identified in 1356/5021 (27.2%) of patients: 15% had a withholding decision and 12.2% a withdrawal decision (including those with a previous withholding decision). Patients with LST limitation were older, more frail, more severely ill and less frequently electively admitted. Patients with withdrawal of LST were more frequently male and had a longer ICU length of stay. The ICU and 30-day mortality were, respectively, 29.1 and 53.1% in the withholding group and 82.2% and 93.1% in the withdrawal group. LST was less frequently limited in eastern and southern European countries than in northern Europe. The patient-independent factors associated with LST limitation were: acute ICU admission (OR 5.77, 95% CI 4.32-7.7), Clinical Frailty Scale (CFS) score (OR 2.08, 95% CI 1.78-2.42), increased age (each 5 years of increase in age had a OR of 1.22 (95% CI 1.12-1.34) and SOFA score [OR of 1.07 (95% CI 1.05-1.09 per point)]. The frequency of LST limitation was higher in countries with high GDP and was lower in religious countries. Conclusions: The most important patient variables associated with the instigation of LST limitation were acute admission, frailty, age, admission SOFA score and country. Trial registration: (ID: NTC03134807).
Full-text available
Purpose: Very old critical ill patients are a rapid expanding group in the ICU. Indications for admission, triage criteria and level of care are frequently discussed for such patients. However, most relevant outcome studies in this group frequently find an increased mortality and a reduced quality of life in survivors. The main objective was to study the impact of frailty compared with other variables with regards to short-term outcome in the very old ICU population. Methods: A transnational prospective cohort study from October 2016 to May 2017 with 30 days follow-up was set up by the European Society of Intensive Care Medicine. In total 311 ICUs from 21 European countries participated. The ICUs included the first consecutive 20 very old (≥ 80 years) patients admitted to the ICU within a 3-month inclusion period. Frailty, SOFA score and therapeutic procedures were registered, in addition to limitations of care. For measurement of frailty the Clinical Frailty Scale was used at ICU admission. The main outcomes were ICU and 30-day mortality and survival at 30 days. Results: A total of 5021 patients with a median age of 84 years (IQR 81-86 years) were included in the final analysis, 2404 (47.9%) were women. Admission was classified as acute in 4215 (83.9%) of the patients. Overall ICU and 30-day mortality rates were 22.1% and 32.6%. During ICU stay 23.8% of the patients did not receive specific ICU procedures: ventilation, vasoactive drugs or renal replacement therapy. Frailty (values ≥ 5) was found in 43.1% and was independently related to 30-day survival (HR 1.54; 95% CI 1.38-1.73) for frail versus non-frail. Conclusions: Among very old patients (≥ 80 years) admitted to the ICU, the consecutive classes in Clinical Frailty Scale were inversely associated with short-term survival. The scale had a very low number of missing data. These findings provide support to add frailty to the clinical assessment in this patient group. Trial registration: (ID: NCT03134807).
Purpose Premorbid conditions affect prognosis of acutely-ill aged patients. Several lines of evidence suggest geriatric syndromes need to be assessed but little is known on their relative effect on the 30-day survival after ICU admission. The primary aim of this study was to describe the prevalence of frailty, cognition decline and activity of daily life in addition to the presence of comorbidity and polypharmacy and to assess their influence on 30-day survival. Methods Prospective cohort study with 242 ICUs from 22 countries. Patients 80 years or above acutely admitted over a six months period to an ICU between May 2018 and May 2019 were included. In addition to common patients’ characteristics and disease severity, we collected information on specific geriatric syndromes as potential predictive factors for 30-day survival, frailty (Clinical Frailty scale) with a CFS > 4 defining frail patients, cognitive impairment (informant questionnaire on cognitive decline in the elderly (IQCODE) with IQCODE ≥ 3.5 defining cognitive decline, and disability (measured the activity of daily life with the Katz index) with ADL ≤ 4 defining disability. A Principal Component Analysis to identify co-linearity between geriatric syndromes was performed and from this a multivariable model was built with all geriatric information or only one: CFS, IQCODE or ADL. Akaike’s information criterion across imputations was used to evaluate the goodness of fit of our models. Results We included 3920 patients with a median age of 84 years (IQR: 81–87), 53.3% males). 80% received at least one organ support. The median ICU length of stay was 3.88 days (IQR: 1.83–8). The ICU and 30-day survival were 72.5% and 61.2% respectively. The geriatric conditions were median (IQR): CFS: 4 (3–6); IQCODE: 3.19 (3–3.69); ADL: 6 (4–6); Comorbidity and Polypharmacy score (CPS): 10 (7–14). CFS, ADL and IQCODE were closely correlated. The multivariable analysis identified predictors of 1-month mortality (HR; 95% CI): Age (per 1 year increase): 1.02 (1.–1.03, p = 0.01), ICU admission diagnosis, sequential organ failure assessment score (SOFA) (per point): 1.15 (1.14–1.17, p < 0.0001) and CFS (per point): 1.1 (1.05–1.15, p < 0.001). CFS remained an independent factor after inclusion of life-sustaining treatment limitation in the model. Conclusion We confirm that frailty assessment using the CFS is able to predict short-term mortality in elderly patients admitted to ICU. Other geriatric syndromes do not add improvement to the prediction model. Since CFS is easy to measure, it should be routinely collected for all elderly ICU patients in particular in connection to advance care plans, and should be used in decision making.
Mobility can be defined as the ability to move or be moved freely and easily. In older adults, mobility impairments are common and associated with risk for additional loss of function. Mobility loss is particularly common in these individuals during acute illness and hospitalization, and it is associated with poor outcomes, including loss of muscle mass and strength, long hospital stays, falls, declines in activities of daily living, decline in community mobility and social participation, and nursing home placement. Thus, mobility loss can have a large effect on an older adult's health, independence, and quality of life. Nevertheless, despite its importance, loss of mobility is not a widely recognized outcome of hospital care, and few hospitals routinely assess mobility and intervene to improve mobility during hospital stays. The Quality and Performance Measurement Committee of the American Geriatrics Society has developed a white paper supporting greater focus on mobility as an outcome for hospitalized older adults. The executive summary presented here focuses on assessing and preventing mobility loss in older adults in the hospital and summarizes the recommendations from that white paper. The full version of the white paper is available as Text S1.
Background: Frailty is a strong indicator of vulnerability among older persons, but its association with ICU outcomes has not been evaluated prospectively (i.e., with objective measurements obtained prior to ICU admission). Our objective was to prospectively evaluate the relationship between frailty and post-ICU disability, incident nursing home (NH) admission, and death. Methods: The parent cohort included 754 adults aged ≥70, who were evaluated monthly for disability in 13 functional activities and every 18 months for frailty (1998-2014). Frailty was assessed using the Fried index, where frailty, pre-frailty and non-frailty were defined, respectively, as ≥3, 1-2, and 0 criteria (of 5). The analytic sample included 391 ICU admissions. Results: The mean age was 84.0 years. Frailty and pre-frailty were present prior to 213 (54.5%) and 140 (35.8%) of the 391 admissions, respectively. Relative to non-frailty, frailty was associated with 41% greater disability over the 6 months following a critical illness (adjusted RR 1.41, 95%CI 1.12, 1.78); pre-frailty conferred 28% greater disability (adjusted RR 1.28, 95%CI 1.01, 1.63). Frailty (OR 3.52, 95% CI 1.23, 10.08), but not pre-frailty (OR 2.01, 95% CI 0.77, 5.24), was associated with increased NH admission. Each 1-point increase in frailty count (0-5) was associated with double the likelihood of death (HR 2.00, 95% CI 1.33, 3.00) through 6 months of follow-up. Conclusions: Pre-ICU frailty status was associated with increased post-ICU disability and new nursing home admission among ICU survivors, and death among all admissions. Pre-ICU frailty status may provide prognostic information about outcomes after a critical illness.
Purpose of review: To examine the benefits of early mobilization and summarize the results of most recent clinical studies examining early mobilization in critically ill patients followed by a presentation of recent developments in the field. Recent findings: Early mobilization of ICU patients, defined as mobilization within 72 h of ICU admission, is still uncommon. In medical and surgical critically ill patients, mobilization is well tolerated even in intubated patients. In neurocritical care, evidence to support early mobilization is either lacking (aneurysmal subarachnoid hemorrhage), or the results are inconsistent (e.g. stroke). Successful implementation of early mobilization requires a cultural change; preferably based on an interprofessional approach with clearly defined responsibilities and including a mobilization scoring system. Although the evidence for the majority of the technical tools is still limited, the use of a bed cycle ergometer and a treadmill with strap system has been promising in smaller trials. Summary: Early mobilization is well tolerated and feasible, resulting in improved outcomes in surgical and medical ICU patients. Implementation of early mobilization can be challenging and may need a cultural change anchored in an interprofessional approach and integrated in a patient-centered bundle. Scoring systems should be integrated to define daily goals and used to verify patients' achievements or identify barriers immediately.