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The association of the Activities of Daily Living and the outcome of old intensive care patients suffering from COVID-19

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Abstract

Purpose: Critically ill old intensive care unit (ICU) patients suffering from Sars-CoV-2 disease (COVID-19) are at increased risk for adverse outcomes. This post hoc analysis investigates the association of the Activities of Daily Living (ADL) with the outcome in this vulnerable patient group. Methods: The COVIP study is a prospective international observational study that recruited ICU patients ≥ 70 years admitted with COVID-19 (NCT04321265). Several parameters including ADL (ADL; 0 = disability, 6 = no disability), Clinical Frailty Scale (CFS), SOFA score, intensive care treatment, ICU- and 3-month survival were recorded. A mixed-effects Weibull proportional hazard regression analyses for 3-month mortality adjusted for multiple confounders. Results: This pre-specified analysis included 2359 patients with a documented ADL and CFS. Most patients evidenced independence in their daily living before hospital admission (80% with ADL = 6). Patients with no frailty and no disability showed the lowest, patients with frailty (CFS ≥ 5) and disability (ADL < 6) the highest 3-month mortality (52 vs. 78%, p < 0.001). ADL was independently associated with 3-month mortality (ADL as a continuous variable: aHR 0.88 (95% CI 0.82-0.94, p < 0.001). Being "disable" resulted in a significant increased risk for 3-month mortality (aHR 1.53 (95% CI 1.19-1.97, p 0.001) even after adjustment for multiple confounders. Conclusion: Baseline Activities of Daily Living (ADL) on admission provides additional information for outcome prediction, although most critically ill old intensive care patients suffering from COVID-19 had no restriction in their ADL prior to ICU admission. Combining frailty and disability identifies a subgroup with particularly high mortality. Trial registration number: NCT04321265.
Brunoetal. Annals of Intensive Care (2022) 12:26
https://doi.org/10.1186/s13613-022-00996-9
RESEARCH
The association oftheActivities ofDaily
Living andtheoutcome ofold intensive care
patients suering fromCOVID-19
Raphael Romano Bruno1, Bernhard Wernly2,3, Hans Flaatten4, Jesper Fjølner5, Antonio Artigas6,
Philipp Heinrich Baldia1, Stephan Binneboessel1, Bernardo Bollen Pinto7, Joerg C. Schefold8, Georg Wolff1,
Malte Kelm1, Michael Beil9, Sigal Sviri9, Peter Vernon van Heerden9, Wojciech Szczeklik10, Muhammed Elhadi11,
Michael Joannidis12, Sandra Oeyen13, Eumorfia Kondili14, Brian Marsh15, Jakob Wollborn16,
Finn H. Andersen17,18, Rui Moreno19,20, Susannah Leaver21, Ariane Boumendil22, Dylan W. De Lange24,
Bertrand Guidet22,23, Christian Jung1* and COVIP study group
Abstract
Purpose: Critically ill old intensive care unit (ICU) patients suffering from Sars-CoV-2 disease (COVID-19) are at
increased risk for adverse outcomes. This post hoc analysis investigates the association of the Activities of Daily Living
(ADL) with the outcome in this vulnerable patient group.
Methods: The COVIP study is a prospective international observational study that recruited ICU patients 70 years
admitted with COVID-19 (NCT04321265). Several parameters including ADL (ADL; 0 = disability, 6 = no disability),
Clinical Frailty Scale (CFS), SOFA score, intensive care treatment, ICU- and 3-month survival were recorded. A mixed-
effects Weibull proportional hazard regression analyses for 3-month mortality adjusted for multiple confounders.
Results: This pre-specified analysis included 2359 patients with a documented ADL and CFS. Most patients evi-
denced independence in their daily living before hospital admission (80% with ADL = 6). Patients with no frailty and
no disability showed the lowest, patients with frailty (CFS 5) and disability (ADL < 6) the highest 3-month mortality
(52 vs. 78%, p < 0.001). ADL was independently associated with 3-month mortality (ADL as a continuous variable: aHR
0.88 (95% CI 0.82–0.94, p < 0.001). Being disable” resulted in a significant increased risk for 3-month mortality (aHR 1.53
(95% CI 1.19–1.97, p 0.001) even after adjustment for multiple confounders.
Conclusion: Baseline Activities of Daily Living (ADL) on admission provides additional information for outcome pre-
diction, although most critically ill old intensive care patients suffering from COVID-19 had no restriction in their ADL
prior to ICU admission. Combining frailty and disability identifies a subgroup with particularly high mortality.
Trial registration number: NCT04321265.
© The Author(s) 2022. Open Access This ar ticle is licensed under a Creative Commons Attribution 4.0 International License, which
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Open Access
*Correspondence: christian.jung@med.uni-duesseldorf.de
1 Medical Faculty, Department of Cardiology, Pulmonology and Vascular
Medicine, Heinrich-Heine-University Duesseldorf, Moorenstraße 5,
40225 Duesseldorf, Germany
Full list of author information is available at the end of the article
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Brunoetal. Annals of Intensive Care (2022) 12:26
Introduction
Older patients admitted to an intensive care unit are at
significantly increased risk for adverse outcome [1, 2].
Old patients make up the subgroup of intensive care
unit patients with the highest mortality [3]. However, the
chronological age is a worse parameter for the outcome
prediction of critically ill older patients [4, 5]. is is par-
ticularly true for SARS-CoV-2 and its disease COVID-
19, which challenge intensive care units worldwide [6].
During the peaks of pandemic, the sheer volume of
patients with COVID-19 has overwhelmed intensive
care resources in many hospitals. In some countries,
age cut-offs for ICU admission had been discussed [7],
but soon medical societies recommend other instru-
ments for triage and outcome prediction. However, the
evidence for some components of the triage decision-
making was relatively scarce. A well-established tool is
the Clinical Frailty Scale (CFS) which assesses the func-
tioning of the old patient regarding fitness and frailty.
Its association with ICU and 30-day mortality showed
its importance for outcome prediction with and with-
out Covid-19 [1, 2, 6, 8]. Apart from CFS, other instru-
ments have been proposed. e Israeli Position Paper, for
example, named the Activities of Daily Living (ADL) as
a candidate for the medical assessment tools that should
be considered for assessing function during triage situ-
ations [9]. ADL had been introduced in the early 1980s.
It is a tool to evaluate individual independence in daily
living. Initially, it was intended to assess the performance
of older patients determining life expectancy [10]. e
scale includes routine tasks, which patients perform dur-
ing their daily routines, such as basic feeding, bathing,
movement (transferring and getting out of bed), sphinc-
ter control, and bathroom use. For non-COVID patients,
it has been found that old patients requiring mechanical
ventilation, low ADL scores—meaning higher grades of
dependence—were independently associated with worse
outcome [11]. Currently, the value of ADL for outcome
prediction of severe COVID-19 and its sequelae remains
unclear. To address this lack of evidence, we performed
Graphical Abstract
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Brunoetal. Annals of Intensive Care (2022) 12:26
a post hoc analysis of the COVIP-Study (COVID-19 in
very old intensive care patients).
is multicentre study investigates the association of
pre-existing disability regarding the Activities of Daily
Living (ADL) with and without pre-existing frailty (CFS)
on the one hand and the outcome on the other hand in a
large prospectively recruited cohort of old ICU patients
( 70years) with COVID-19.
Methods
Design andsettings
COVIP aimed to identify predictors for mortality in older
patients suffering from severe COVID-19. is multicen-
tre study was part of the Very old Intensive care Patients
(VIP) project and was endorsed by the European Soci-
ety of Intensive Care Medicine (ESICM) (www. vipst udy.
org). COVIP was registered on ClinicalTrials.gov (ID:
NCT04321265) and followed the European Union Gen-
eral Data Privacy Regulation (GDPR) directive applied
in most participating countries. As in the previous VIP-
studies [1, 2], national coordinators recruited the ICUs,
coordinated national and local ethical permissions, and
supervised patient recruitment at national level. For all
centres ethical approval was mandatory for study par-
ticipation. In most countries, informed consent was
obligatory for inclusion depending on local legal regula-
tions. is study used patient data from 151 ICUs from
26 independent countries, including European ICUs, and
the Asian, African, and Americas.
Study population
Patients with proven (PCR diagnosed) COVID-19 aged
70years or older who were admitted to an intensive care
unit were recruited. e dataset for this subgroup analy-
sis was extracted from the study database on the 15th of
July 2021. us, the database included patients who were
admitted from the 19th of March 2020 to the 15th of July
2021. Data collection for each patient commenced at ICU
admission. e admission day was defined as day one,
and all consecutive days were numbered sequentially
from the admission date. For this analysis, only patients
with a documented CFS and ADL were included.
Data collection andstorage
All centres used a uniform online electronic case report
form (eCRF). As previously analysis, only patients with
a documented ADL were included. As previously, the
Sequential Organ Failure Assessment (SOFA) score on
admission was calculated either manually or using an
online calculator in the eCRF [1, 2]. Furthermore, COVIP
assessed the need for non-invasive or invasive ventilation,
prone positioning, tracheostomy, vasopressor use and
renal replacement therapy. e eCRF also asked about
any limitation of life-sustaining treatment during the
ICU stay. e eCRF and database ran on a secure server
composed and stored in Aarhus University, Denmark.
The activity ofdaily living, frailty, andcomorbidities
e Katz Activities of Daily Living (ADL) score assessed
the patient’s independence in daily living before hospital
admission. ADL is a commonly utilised graded instru-
ment to evaluate disability and the level of dependence
in chronically ill or older patients. It assesses six pri-
mary and psychosocial functions: bathing, dressing,
going to the toilet, transferring, feeding, and continence.
Every patient receives 1 point for each independent and
0 for every dependent activity (6 = independent patient,
0 = very dependent patient). Depending on the trial and
context, the cut-off defining “disability” varies. ADL
could be obtained by the patient himself, by caregivers/
family, hospital records, or other sources. To charac-
terise the cohort more precisely, we divided it into two
groups: an ADL score of 6 was defined as “no disabil-
ity”, < 6 as “disable” [12]. e Clinical Frailty Scale (CFS)
was evaluated as described previously [1, 2]. e respec-
tive visual and simple description for this assessment tool
was used with permission [10, 13, 14] and distinguished
nine classes of frailty from very fit (CFS 1) to terminally
ill (CFS 9). A CFS 5 was considered as “frailty”. e
SOFA score was recorded on admission; it was calculated
manually or using an online calculator. In the next step,
patients were divided into three groups according to their
CFS and ADL: Patients without frailty and without dis-
ability (CFS < 5, ADL 6), patients with either frailty or dis-
ability CFS 5 or ADL < 6, and patients with both frailty
and disability (CFS 5 and ADL < 6).
Statistical analysis
We did not perform a formal sample size calculation
prior to this purely observational study. e analysis
plan was finalised prior to any analysis. e primary
exposure were disability (ADL) and frailty (CFS), the
primary outcome was 30-month survival, and the sec-
ondary outcomes were overall survival up to discharge
from ICU, survival 30days after ICU admission, organ
support (vasoactive drugs, invasive mechanical ventila-
tion, non-invasive ventilation, and renal replacement
therapy) and treatment limitation. Continuous data
points are expressed as median ± interquartile range.
Differences between independent groups were calcu-
lated using the Mann–Whitney U-test. Categorical
data are expressed as numbers (percentage). e Chi-
square test was applied to calculate differences between
groups. A mixed-effects Weibull proportional hazard
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Brunoetal. Annals of Intensive Care (2022) 12:26
regression was performed using ADL as a categorical
(ADL 6 or ADL 5 meaning a patient with independ-
ence in daily living) and continuous variable (ADL 0
to 6) and 3-month mortality (primary outcome). We
fitted models for the dependent variables with robust
standard errors. e regression analyses were con-
ducted using only robust estimators of the standard
errors and not in the sense of robustness against vio-
lations of normality assumptions as for the robust
methods (e.g., Mann–Whitney tests) used for the uni-
variate analyses [15]. ree models was performed
[16]. Model-2 added age, gender und SOFA. To adjust
the effect for ICU capacities and COVID-19 incidence,
model-3 additionally comprises ICU beds per 100.000
per country and the local COVID incidence on the day
of ICU admission. We chose the independent variables
based on differences in the baseline characteristics,
previous reports, and our own clinical experience. e
adjusted hazard ratios (HR) with respective 95% confi-
dence intervals (95% CI) were calculated: HR describes
the change in risk of death per each unit increase for
continuous variables and for one specific category vs.
a reference category for categorical variables. A HR > 1
suggests an increase in the risk of death, HR < 1 suggest
a decrease in the risk of death. All tests were two-sided,
and a p-value of < 0.05 was considered statistically
significant. Since not all parameters were available
for all categories, patients had to be excluded for the
Table 1 Baseline characteristics of patients with disability (ADL < 6) and without (ADL 6)
ADL Activities of Daily Living, CFS Clinical Frailty Scale, CAD coronary artery disease, SOFA Sequential Organ Failure Assessment; p-value comparing all groups.
[Numbers do not add up to 100% due to missing values]
No disability (ADL 6) Disability (ADL < 6) p-value
N = 1884 N = 475
Male sex ([%], n) 74% (1,394) 58% (276) < 0.001
Age (years) 75 (4) 78 (5) < 0.001
SOFA 5 (3) 7 (4) < 0.001
CFS 3 (1) 5 (2) < 0.001
Diabetes mellitus ([%], n) 31% (589) 52% (246) < 0.001
CAD ([%], n) 20% (380) 34% (160) < 0.001
Chronic renal failure ([%], n) 12% (230) 29% (139) < 0.001
Arterial hypertension ([%], n) 64% (1,212) 77% (364) < 0.001
Pulmonary disease ([%], n) 21% (398) 32% (151) < 0.001
Chronic heart failure ([%], n) 12% (219) 28% (133) < 0.001
Table 2 Baseline characteristics of patients without frailty (CFS < 5) and disability (ADL 6), patients with either disability (ADL < 6) or
Frailty (CFS 5), or both frailty and disability
ADL Activities of Daily Living, CFS Clinical Frailty Scale, CAD coronary artery disease, SOFA Sequential Organ Failure Assessment, p-value comparing all groups.
[Numbers do not add up to 100% due to missing values]
Non-frailty (CFS < 5)/no
disability (ADL 6) Frailty (CFS 5) or disability
(ADL < 6) Frailty and disability p-value
n = 1829 n = 260 n = 270
Male sex ([%], n) 73% (1342) 67% (173) 57% (155) < 0.001
Age (years) 75 (4) 77 (6) 78 (5) < 0.001
SOFA 5 (3) 6 (3) 8 (4) < 0.001
CFS 3 (1) 5 (1) 6 (1) < 0.001
Diabetes mellitus ([%], n) 31% (557) 48% (125) 57% (153) < 0.001
CAD ([%], n) 20% (358) 30% (78) 39% (104) < 0.001
Chronic renal failure ([%], n) 11% (206) 27% (71) 34% (92) < 0.001
Arterial hypertension ([%], n) 64% (1167) 75% (194) 80% (215) < 0.001
Pulmonary disease ([%], n) 21% (377) 29% (74) 36% (98) < 0.001
Chronic heart failure ([%], n) 11% (196) 29% (73) 31% (83) < 0.001
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Brunoetal. Annals of Intensive Care (2022) 12:26
subgroup analyses. For this reason, not all patient num-
bers add up to 100% (see Tables1, 2, 3, and 4). Stata
16 was used for all statistical computations (StataCorp
LLC, 4905 Lakeway Drive, College Station, Brownsville,
Texas, USA). GraphPad Prism 5 (GraphPad Software,
San Diego, CA 92108, USA) was used for figures.
Results
Study population
In total, this subgroup analysis included 2359 patients
from the COVIP study with a documented ADL and
CFS (see Fig.1). Most of the patients did not show any
dependence in their daily living prior to hospital admis-
sion (80% ADL 6, Fig.2A), although frailty in CFS was
distributed more heterogeneously (Fig.2B), most patients
lived without severe frailty (81% CFS < 5, Fig.2).
Baseline characteristics ofpatients withdisability
compared topatients withoutdisability
Patients without significant impairment in the Activi-
ties of Daily Living (ADL 6) were predominantly male
(74%, p < 0.001), younger (75 years (IQR 4) vs. 78years
(IQR 5), p < 0.001), less frail (CFS 3 (IQR 1) vs. 5 (IQR 2),
p < 0.001) and significantly less affected by comorbidities
(Table2). In contrast, with increasing disability patients
were older, more frail and had significantly more comor-
bidities. SOFA score on admission was significantly lower
in patients with high ADL (ADL 6: 5 (IQR 3); ADL < 6: 7
(IQR: 4), p < 0.001) (Fig.3).
Intensive care treatment andoutcome ofpatients
withdisability compared topatients withoutdisability
During intensive care treatment, patients with pre-exist-
ing disability received significantly less invasive mechani-
cal ventilation (67 vs. 75%, p = 0.001), tracheostomy
Table 3 Outcome and intensive care treatment of patients without frailty (CFS < 5) and disability (ADL 6), patients with either disability
(ADL < 6) or frailty (CFS 5), or both frailty and disability
ADL Activities of Daily Living, CFS Clinical Frailty Scale, RRT renal replacement therapy, p-value comparing all groups. [Numbers do not add up to 100% due to missing
values]
Non-frailty (CFS < 5)/no
disability (ADL 6) Frailty (CFS 5) or
disability (ADL < 6) Frailty and disability p-value
Invasive mechanical ventilation 76% (1388) 62% (162) 68% (183) < 0.001
Non-invasive mechanical ventilation 23% (414) 29% (75) 31% (84) 0.002
Tracheostomy 21% (376) 12% (31) 10% (28) < 0.001
Vasoactive drugs 72% (1312) 58% (149) 67% (178) < 0.001
RRT 14% (255) 16% (41) 23% (61) < 0.001
Life sustaining care withheld 30% (540) 42% (106) 31% (83) < 0.001
Life sustaining care withdrawn 20% (369) 21% (52) 16% (44) 0.32
ICU mortality 45% (810) 55% (140) 67% (180) < 0.001
30-day mortality 46% (848) 63% (162) 72% (195) < 0.001
3-month mortality 52% (945) 68% (176) 78% (210) < 0.001
Table 4 Mixed-effects Weibull proportional hazard regression analyses for 3-month mortality (aHR (95% CI, p-value))
aHR adjusted hazard ratio, ADL Activities of Daily Living, CFS Clinical Frailty Scale, ICU intensive care unit, SOFA Sequential Organ Failure Assessment
Model-1: Individual ICU as random eect, and ADL/CFS as xed eects
Model-2: Model-1 plus SOFA, gender, age
Model-3: Model-2 plus ICU beds per 100.000 per country and the local COVID-19 incidence on the day of ICU admission
Model-1 Model-2 Model-3
ADL continuous 0.84 (0.80–0.89, p < 0.001) 0.91 (0.87–0.96, p 0.001) 0.88 (0.82–0.94, p < 0.001)
ADL binary (ADL < 6) 1.83 (1.50–2.21, p < 0.001) 1.34 (1.09–1.65, p 0.006) 1.53 (1.19–1.97, p 0.001)
ADL binary (ADL < 5) 1.75 (1.44–2.13, p < 0.001) 1.23 (0.99–1.52, p 0.060) 1.57 (1.21–2.03, p 0.001)
Frailty or disability (ADL < 6 or
CFS 5) 1.77 (1.39–2.25, p < 0.001) 1.51 (1.22–1.88, p < 0.001) 1.88 (1.47–2.40, p < 0.001)
Frailty and disability 2.43 (1.87–3.16, p < 0.001) 1.58 (1.19–2.10, p 0.002) 1.94 (1.39–2.71, p < 0.001)
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Brunoetal. Annals of Intensive Care (2022) 12:26
(12 vs. 20%, p < 0.001), vasoactive drugs (62 vs. 72%,
p < 0.001), but more renal replacement therapies (19 vs.
14%, p = 0.006), and non-invasive ventilation (31 vs.
23%, p < 0.001). Limitations of life-sustaining therapy
occurred significantly more often in patients without dis-
ability. Patients with disability suffered from significantly
increased crude ICU- (62 vs. 45%, p < 0.001), 30-day (66
vs. 47%, p < 0.001), and 3-month mortality (71 vs. 53%,
p < 0.001, Figs. 4 and 5). Using an ADL of less than 5 as
cut-off resulted in similar outcomes (see Fig.6).
Fig. 1 Consort diagram
Fig. 2 A Distribution of documented ADL on admission (6 = no
disability; 0 = fully dependend). B Distribution of CFS (1 = no frailty;
9 = terminally frail)
Fig. 3 Kaplan–Meier for patients with a disability (ADL < 6, red line)
compared to patients without a disability (ADL 6, blue line) (3-month
mortality, ± 95% CI). p < 0.001 log-rank test
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In the mixed-effects Weibull proportional hazard
regression, ADL was associated with 3months mortality
as a continuous variable (aHR 0.88 (0.82–0.94, p < 0.001)).
is means that with rising ADL (= declining disability),
the risk for mortality decreased. As binary variable, an
ADL < 6 (“disability”) was associated with an increased
3months mortality (aHR 1.53 (1.19–1.97, p 0.001), and
an ADL of < 5 (aHR 1.57 (1.21–2.03, p 0.001), Table3).
Baseline characteristics ofpatients withfrailty compared
topatients withoutfrailty
Patients with high frailty (CFS 5) were as well pre-
dominantly male (61%, p < 0.001), older (78 years (IQR
5), p < 0.001) and had significantly more comorbidities
(Additional file1: TableS1). erefore, with decreasing
frailty patients were younger and had less comorbidities.
Like patients without disability, SOFA score was lower
in patients with a low frailty score (CFS < 5: 5 (IQR: 3);
CFS 5: 7 (IQR: 4, p < 0.001). After admission to the ICU,
invasive mechanical ventilation (66 vs. 75%, p < 0.001)
and tracheostomy (11 vs. 20%, p < 0.001) occurred sig-
nificantly more often in patients without pre-existing
frailty. ere was no difference regarding the use of vaso-
active drugs. Patients with pre-existing frailty received
significantly more non-invasive ventilation (30 vs. 23%,
p = 0.005) and more renal replacement therapies (21 vs.
14%, p < 0.001). Frail patients evidenced a significantly
increased crude ICU- (65 vs. 45%, p < 0.001), 30-day (71
vs. 46%, p < 0.001), and 3-month mortality (77 vs. 52%,
p < 0.001).
Comparison ofpatients withoutdisability andfrailty,
withdisability orfrailty, andofpatients withfrailty
anddisability
When dividing into the three groups no disability/frailty,
either frailty or disability and frailty and disability, the
results were similar: patients with frailty and disabil-
ity were older (78years (IQR 5), p < 0.001) and had sig-
nificantly more comorbidities compared to the former
groups (Table2). Even though there was no difference
in SOFA score between patients without disability and
frailty and patients with disability and frailty (non-frailty,
no disability: 5 (IQR: 3); frailty and disability: 8 (IQR: 4),
Table2), the study shows individually that patients with
high independence in daily living evidenced lower scores
of organ failure on admission and patients without frailty
evidenced lower scores of organ failure on admission.
Fig. 4 ICU-, 30-day and 3-month mortality [%] for patients with
neither frailty (CFS 5) nor disability (ADL 6), frailty or disability, or
frailty and disability. **p < 0.001
Fig. 5 Kaplan–Meier for patients without a disability and frailty (ADL
6 and CFS < 5, blue line) compared to patients with a disability or
frailty (ADL < 6 or CFS 5), red line), and patients with disability and
frailty (green line) (3-month mortality, ± 95% CI). p < 0.001 log-rank
test
Fig. 6 Kaplan–Meier for patients with a disability (ADL < 5, red
line) compared to patients without a disability (ADL 5, blue line)
(3-month mortality, ± 95% CI). p < 0.001 log-rank test
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Brunoetal. Annals of Intensive Care (2022) 12:26
ere were significant differences both in short- and
long-term outcome: combining ADL and CFS to three
groups (no disability/frailty, either frailty or disability,
frailty and disability) resulted into the following.
e two variables frailty and disability as well as either
frailty or disability were significantly associated with
the 3-month mortality (Table 4): suffering from frailty
or disability, was an independent risk factor (aHR 1.88
(1.47–2.40, p < 0.001)) but the highest risk was found for
patients with both frailty and disability (aHR 1.94 (1.39–
2.71, p < 0.001)). Patients with no frailty and no disability
evidenced a significantly lower mortality (ICU-mortal-
ity 45%, 30-day mortality 46%, 3-month mortality 52%,
p < 0.001), patients with frailty and disability the high-
est mortality (ICU-mortality 67%, 30-day mortality 72%,
3-month mortality 78%, p < 0.001, Table 3, Figs. 5 and
6). Patients who suffered either from frailty or disability
were in-between (ICU-mortality 55%, 30-day mortality
63%, 3-month mortality 68%, p < 0.001). erefore, with
the detection of both ADL and CFS a subset of patients
with an almost 80% 3months mortality can be identified.
Discussion
e Activities of Daily Living before acute illness have been
proposed as a tool for outcome prediction in triage during
the COVID-pandemic. e present study is based on the
large international COVIP database and reveals that ADL
is an independent prognosticator for outcome in critically
ill old patients admitted to the ICU and who suffer from
COVID-19. However, most patients evidenced a high degree
of independence on admission, which might be interpreted
as consequence of triage. It is well known that pre-existing
disability is a risk factor in intensive care patients, but the
crucial finding of our study is that combining of pre-existing
disability with pre-existing frailty identifies a subgroup with
extraordinary high mortality.
Historically, there are different approaches to assessing
the functional status of old patients before ICU admis-
sion. Many studies in intensive care used functional
assessments of survivors of critical illness with respira-
tory failure such as ADL [1720]. ere are insufficient
data on what ADL scores should be considered "nor-
mal" in a selected population of critically ill and old ICU
patients. Level et al. conducted a prospective cohort
study with 188 patients aged 75years or older admitted
to the ICU. ey found a median ADL of 4.2 ± 1.6 on
admission. Furthermore, ADL on admission was inde-
pendently predictive for one-year mortality [21]. Maziere
etal. investigated a similar cohort with 223 critically ill,
old patients. In their investigation, ADL at admission
was 3.8 ± 2.2. An ADL from 0 to 3 was defined as severe
dependence/disability and was significantly associated
with nosocomial infection (p < 0.05) [22]. In a very small
cohort of 16 intensive care patients, pre-admission ADL
was 6 (IQR 5–6) [23].
ere is no commonly accepted cut-off for ADL that
distinguishes "dependence" from "independence". Gian-
nasi etal. defined every patient with an ADL below 6 as
"dependent". eir prospective cohort study included 249
patients aged 65years or older who were admitted to the
ICU and required mechanical ventilation for more than
48h. e logistic regression analysis with adjustment for
APACHEII score and age revealed an independent asso-
ciation of ADL with mortality (OR: 2.35, 95% CI: 1.16–
4.75) [24]. Demesielle etal. used an ADL cut-off of 5 in
their prospective multicentric observational cohort study
with 501 patients aged 75 years or older who required
mechanical ventilation. ey found that an ADL 5 was
not associated with increased in-hospital mortality (OR
0.88; 95% CI 0.54–1.42, p = 0.598), but increased 1-year
mortality (aOR 0.53, 95% CI 0.30–0.96, p = 0.038) [11].
In 123 ICU patients with severe pneumonia, Sangla e
al. defined three groups of dependence for the 1-year
follow-up: ADL of 6, from 5 to 3, and below 3 [25]. Sch-
weikert et al. defined a cut-off below 6 as dependence
in a prospective interventional intensive care study in
patients aged 18years or older [26]. Langlet etal. used
an ADL score of 6 to define a full function, an ADL of 5
a low degree of impairment, an ADL 4–3 for moderate
impairment, and two or less for severe functional impair-
ment. eir study compared 26 patients with chronic
obstructive pulmonary disease undergoing mechanical
ventilation. In their study, the ADL score was a significant
predictor of 6-month mortality [12]. VIP-2 used a cut-off
of an ADL of less than 5 defining “disability” [1]. In the
present study, there was no relevant difference between
the ADL cut-offs 5 and 4 (for ADL 5, see Fig.6).
e timing of ADL use also differs between studies.
While many use the pre-acute condition as a reference,
other studies use the ADL score at the time of discharge
from acute care. MacDonald etal. scored 42 patients who
were discharged after being treated with veno-venous
extra-corporal life support for acute respiratory failure.
ey found high ADL scores, indicating high independ-
ence and functionality in 62% of patients [27]. In a pro-
spective, multicentre cohort study that recruited patients
who were admitted to the ICU with respiratory failure or
shock, a relevant dependence could be found in 23% of
the patients 12months after discharge. Of note, in this
study, disability was defined as ADL < 6 [28].
In VIP-2, survivors had significantly higher ADL values
than non-survivors (6 (5–6) vs. 6 (3–6), p < 0.001); 27.7%
Page 9 of 11
Brunoetal. Annals of Intensive Care (2022) 12:26
(962/3473) of the patients had an ADL 4; and 59.6% an
ADL of 6 [1]. By contrast, in the present COVIP-study,
only 16% (430/2692) patients had an ADL 4; and 84%
had an ADL > 5 (2262/2692). However, it should be noted
that VIP-2 included patients aged 80years and older, but
COVIP included patients aged 70years and older. In the
pandemic of SARS-Cov-2, CFS provides valuable and
reliable information for outcome prediction [6]. Com-
pared to CFS, the assessment of ADL might be more
time-consuming [29] (compare Additional file1: TableS2
and Fig. S1, Additional file2).
Limitations
is is not the first study showing that pre-existing dis-
ability is an independent risk factor for ICU outcome,
but it is the first investigating its value in a selected
high-risk population of critically ill old patients suf-
fering from COVID-19. Furthermore, to our best
knowledge, it is the first investigation that analyses the
overlap between frailty and disability in this particularly
vulnerable cohort. Our study has some methodologi-
cal limitations. For example, we did not have a control
group of younger COVID-19 patients for comparison
or a comparable age cohort of patients who were not or
could not be admitted to the ICU. In addition, COVIP
does not capture information on pre-ICU care and tri-
age. us, it might be hypothesised that during pan-
demic peaks patients with low ADL might not have
been admitted to the ICU, and therefore do not appear
in COVIP. Participating countries varied widely in their
care structure. is results in a large degree of het-
erogeneity. e fact that CFS was recorded more fre-
quently overall than the ADL is probably also due to the
study design of the COVIP group. COVIP, and its pre-
decessors, focused on the role of CFS for outcome pre-
diction. It may be argued that there is a strong overlap
between frailty and disability, so that both scales meas-
ure the same thing. However, it is argued that the mor-
tality of the group who are both frail and dependent are
significantly more at risk than patients who suffered
only either from frailty or disability. For this reason,
this study supports that both scales allow a comple-
mentary analysis of the patient. Last, the time frame of
pre-existing ADL had not been defined in detail by the
study. us, we do not know, if ADL reflects one month
or one year before acute COVID-19.
Conclusion
In critically ill old intensive care patients suffering from
COVID-19, most patients evidenced high degrees of
independence in Activities of Daily Living before ICU
admission. Combining pre-existing frailty with pre-
existing disability identifies a subgroup that evidences
extremely high mortality rates. us, the initial assess-
ment of ADL might offer an additional value for outcome
prediction.
Evidence beforethis study
e value of Activities of Daily Living (ADL) could
be used for outcome prediction of critically ill elderly
patients.
Added value ofthis study
is study with 2359 patients investigated the role of
ADL in outcome prediction in severe cases of COVID-
19. e combination of ADL with frailty might provide
additional prognostic information.
Implications ofall theavailable evidence
ADL offers additional information on intensive care
and 3-month mortality, although most patients evi-
denced normal degrees of independence prior to ICU
admission. e combination of an increased frailty
(according to Clinical Frailty Scale) with reduced inde-
pendence (= increased disability) in the Activities of
Daily Living identifies a subgroup with mortality rates
up to 80%.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s13613- 022- 00996-9.
Additional le1: TableS1: Baseline characteristics of patients with
Frailty (CFS 5) and without (CFS < 5). TableS2: Baseline characteristics
of patients who survived 3 months after ICU admission. Figure S1. Katz
Index of Independence in Activities of Daily Living (ADL) from the COVIP-
CRF, with permission.
Additional le2. COVIP study group.
Acknowledgements
Filiz Demirtas and Helene Mathilde Emilie Moecke helped with formatting,
writing, and improving the manuscript.
COVIP-study group (see Supplement 3, covip@med.uni-duesseldorf.de).
Authors’ contributions
RRB, BW, GW and CJ analysed the data and wrote the first draft of the
manuscript. HF and BG and DL and IS contributed to statistical analysis and
improved the paper. PD and SB and BBP and JCS and MK and MB and MJ and
SO and EK and BM and FHA and RM and FD and SL and AB gave guidance,
contributed data, and improved the paper. All authors read and approved the
final manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL. This study was
endorsed by the ESICM. Free support for running the electronic database and
was granted from the dep. of Epidemiology, University of Aarhus, Denmark.
Page 10 of 11
Brunoetal. Annals of Intensive Care (2022) 12:26
The support of the study in France by a grant from Fondation Assistance Pub-
lique-Hôpitaux de Paris pour la recherche is greatly appreciated. In Norway,
the study was supported by a grant from the Health Region West. In addition,
the study was supported by a grant from the European Open Science Cloud
(EOSC). EOSCsecretariat.eu has received funding from the European Union’s
Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant agreement
number 831644. This work was supported by the Collaborative Research
Center SFB 1116 (German Research Foundation, DFG) and by the Forschun-
gskommission of the Medical Faculty of the Heinrich-Heine-University
Düsseldorf and No. 2020–21 to RRB for a Clinician Scientist Track. No (industry)
sponsorship has been received for this investigator-initiated study.
Availability of data and materials
Individual participant data that underlie the results reported in this article are
available to investigators whose proposed use of the data has been approved
by the COVIP steering committee. The anonymised data can be requested
from the authors if required.
Declarations
Ethics approval and consent to participate
The primary competent ethics committee was the Ethics Committee of the
University of Duesseldorf, Germany. Institutional research ethic board approval
was obtained from each study site.
Consent for publication
The manuscript does not contain any individual person’s data in any form.
Competing interests
The authors declare that they have no competing interests. JCS reports
grants (full departmental disclosure) from Orion Pharma, Abbott Nutrition
International, B. Braun Medical AG, CSEM AG, Edwards Lifesciences Services
GmbH, Kenta Biotech Ltd, Maquet Critical Care AB, Omnicare Clinical Research
AG, Nestle, Pierre Fabre Pharma AG, Pfizer, Bard Medica S.A., Abbott AG,
Anandic Medical Systems, Pan Gas AG Healthcare, Bracco, Hamilton Medical
AG, Fresenius Kabi, Getinge Group Maquet AG, Dräger AG, Teleflex Medical
GmbH, Glaxo Smith Kline, Merck Sharp and Dohme AG, Eli Lilly and Company,
Baxter, Astellas, Astra Zeneca, CSL Behring, Novartis, Covidien, Philips Medical,
Phagenesis Ltd, Prolong Pharmaceuticals and Nycomed outside the submit-
ted work. The money went into departmental funds. No personal financial
gain applied.
Author details
1 Medical Faculty, Department of Cardiology, Pulmonology and Vascular Medi-
cine, Heinrich-Heine-University Duesseldorf, Moorenstraße 5, 40225 Dues-
seldorf, Germany. 2 Department of Internal Medicine, General Hospital
Oberndorf, Teaching Hospital of the Paracelsus Medical University Salzburg,
Paracelsusstraße 37, Oberndorf, 5110 Salzburg, Austria. 3 Center for Public
Health and Healthcare Research, Paracelsus Medical University Salzburg,
5020 Salzburg, Austria. 4 Department of Clinical Medicine, Department
of Anaestesia and Intensive Care, Haukeland University Hospital, University
of Bergen, Bergen, Norway. 5 Department of Intensive Care, Aarhus University
Hospital, Aarhus, Denmark. 6 Department of Intensive Care Medicine, CIBER
Enfermedades Respiratorias, Corporacion Sanitaria Universitaria Parc Tauli,
Autonomous University of Barcelona, Sabadell, Spain. 7 Department of Acute
Medicine, Geneva University Hospitals, Geneva, Switzerland. 8 Department
of Intensive Care Medicine, Inselspital, Universitätsspital, University of Bern,
Bern, Switzerland. 9 Department of Medical Intensive Care, Hadassah Medical
Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem,
Israel. 10 Department of Intensive Care and Perioperative Medicine, Jagiellonian
University Medical College, Krakow, Poland. 11 Faculty of Medicine, University
of Tripoli, Tripoli, Libya. 12 Division of Intensive Care and Emergency Medicine,
Department of Internal Medicine, Medical University Innsbruck, Innsbruck,
Austria. 13 Department of Intensive Care 1K12IC, Ghent University Hospital,
Ghent, Belgium. 14 Intensive Care Unit, University Hospital of Heraklion,
Heraklion, Greece. 15 Mater Misericordiae University Hospital, Dublin, Ireland.
16 Department of Anesthesiolgy, Perioperative and Pain Medicine, Brigham
and Women’s Hospital, Harvard Medical School, Boston, USA. 17 Depart-
ment of Anaesthesia and Intensive Care, Ålesund Hospital, Ålesund, Norway.
18 Department of Circulation and Medical Imaging, Norwegian University
of Science and Technology, Trondheim, Norway. 19 Hospital de São José, Centro
Hospitalar Universitário de Lisboa Central, Faculdade de Ciências Médicas de
Lisboa, Nova Médical School, Lisbon, Portugal. 20 Universidade da Beira Interior,
Covilhã, Portugal. 21 General Intensive Care, St George´S University Hospitals
NHS Foundation Trust, London, UK. 22 Sorbonne Universités, UPMC Univ Paris
06, INSERM, UMR_S 1136, Institut Pierre Louis d’Epidémiologie et de Santé
Publique, Equipe: épidémiologie hospitalière qualité et organisation des soins,
75012 Paris, France. 23 Assistance Publique - Hôpitaux de Paris, Hôpital Saint-
Antoine, Service de réanimation médicale, 75012 Paris, France. 24 Department
of Intensive Care Medicine, University Medical Center, University Utrecht,
Utrecht, the Netherlands.
Received: 2 November 2021 Accepted: 15 February 2022
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Importance: Among COVID-19 cases, especially the (frail) elderly show a high number of severe infections, hospital admissions, complications, and death. The highest mortality is found between 80 and 89 years old. Why do these patients have a higher risk of severe COVID-19? In this narrative review we address potential mechanisms regarding viral transmission, physical reserve and the immune system, increasing the severity of this infection in elderly patients. Observations: First, the spread of COVID-19 may be enhanced in elderly patients. Viral shedding may be increased, and early identification may be complicated due to atypical disease presentation and limited testing capacity. Applying hygiene and quarantine measures, especially in patients with cognitive disorders including dementia, can be challenging. Additionally, elderly patients have a decreased cardiorespiratory reserve and are more likely to have co-morbidity including atherosclerosis, rendering them more susceptible to complications. The aging innate and adaptive immune system is weakened, while there is a pro-inflammatory tendency. The effects of SARS-CoV-2 on the immune system on cytokine production and T-cells, further seem to aggravate this pro-inflammatory tendency, especially in patients with cardiovascular comorbidity, increasing disease severity. Conclusions and relevance: The combination of all factors mentioned above contribute to the disease severity of COVID-19 in the older patient. While larger studies of COVID-19 in elderly patients are needed, understanding the factors increasing disease severity may improve care and preventative measures to protect the elderly patient at risk for (severe) COVID-19 in the future.
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Purpose: Old (>64 years) and very old (>79 years) intensive care patients with sepsis have a high mortality. In the very old, the value of critical care has been questioned. We aimed to compare the mortality, rates of organ support, and the length of stay in old vs. very old patients with sepsis and septic shock in intensive care. Methods: This analysis included 9,385 patients, from the multi-center eICU Collaborative Research Database, with sepsis; 6184 were old (aged 65–79 years), and 3,201 were very old patients (aged 80 years and older). A multi-level logistic regression analysis was used to fit three sequential regression models for the binary primary outcome of ICU mortality. A sensitivity analysis in septic shock patients ( n = 1054) was also conducted. Results: In the very old patients, the median length of stay was shorter (50 ± 67 vs. 56 ± 72 h; p < 0.001), and the rate of a prolonged ICU stay was lower (>168 h; 9 vs. 12%; p < 0.001) than the old patients. The mortality from sepsis was higher in very old patients (13 vs. 11%; p = 0.005), and after multi-variable adjustment being very old was associated with higher odds for ICU mortality (aOR 1.32, 95% CI 1.09–1.59; p = 0.004). In patients with septic shock, mortality was also higher in the very old patients (38 vs. 36%; aOR 1.50, 95% CI 1.10–2.06; p = 0.01). Conclusion: Very old ICU-patients suffer from a slightly higher ICU mortality compared with old ICU-patients. However, despite the statistically significant differences in mortality, the clinical relevance of such minor differences seems to be negligible.
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Frailty is associated with perioperative adverse outcomes, especially for the elderly. This study aimed to assess whether frailty was an independent risk factor of one-year mortality in frail patients after elective orthopedic surgery. In this prospective study, three hundred and thirteen patients aged ≥ 65 years, undergoing elective orthopedic surgery were finally included. Frailty assessed by the Clinical Frailty Score (CFS) before the surgery was present in 29.7% (93/313). Among them, 7.7% of patients (24/313) died at one year after surgery. In multivariate logistic analysis, higher CFS (OR = 2.271, 95% CI= 1.472-3.504) was found to be an independent risk factor of one-year mortality after surgery in elderly orthopedic patients. The area under the receiver operating characteristic curve of the model was 0.897 (95% CI 0.834-0.959). In addition, we found higher Charlson comorbidity index (OR= 1.498, 95% CI = 1.082-2.073) was also a significant risk factor. In conclusion, frailty is associated with increased one-year mortality in elderly patients after elective orthopedic surgery, which should be considered as a routine assessment tool in preoperative practice.