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The impact of frailty on survival in elderly intensive care patients with COVID-19: the COVIP study

  • Ksar Al Ainy Cairo University Hospitals.

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Background: The COVID-19 pandemic has led highly developed healthcare systems to the brink of collapse due to the large numbers of patients being admitted into hospitals. One of the potential prognostic indicators in patients with COVID-19 is frailty. The degree of frailty could be used to assist both the triage into intensive care, and decisions regarding treatment limitations. Our study sought to determine the interaction of frailty and age in elderly COVID-19 ICU patients. Methods: A prospective multicentre study of COVID-19 patients ≥70 years admitted to intensive care in 138 ICUs from 28 countries was conducted. The primary endpoint was 30-day mortality. Frailty was assessed using the clinical frailty scale. Additionally, comorbidities, management strategies and treatment limitations were recorded. Results: The study included 1346 patients (28% female) with a median age of 75 years (IQR 72–78, range 70–96), 16.3% were older than 80 years, and 21% of the patients were frail. The overall survival at 30 days was 59% (95% CI 56–62), with 66% (63–69) in ft, 53% (47–61) in vulnerable and 41% (35–47) in frail patients (p<0.001). In frail patients, there was no diference in 30-day survival between diferent age categories. Frailty was linked to an increased use of treatment limitations and less use of mechanical ventilation. In a model controlling for age, disease severity, sex, treat- ment limitations and comorbidities, frailty was independently associated with lower survival. Conclusion: Frailty provides relevant prognostic information in elderly COVID-19 patients in addition to age and comorbidities. Trial registration NCT04321265, registered 19 March 2020.
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Jungetal. Crit Care (2021) 25:149
The impact offrailty onsurvival inelderly
intensive care patients withCOVID-19:
theCOVIP study
Christian Jung1* , Hans Flaatten2,3, Jesper Fjølner4, Raphael Romano Bruno1, Bernhard Wernly5,
Antonio Artigas6, Bernardo Bollen Pinto7, Joerg C. Schefold8, Georg Wolff1, Malte Kelm1, Michael Beil9,
Sigal Sviri9, Peter Vernon van Heerden10, Wojciech Szczeklik11, Miroslaw Czuczwar12, Muhammed Elhadi13,
Michael Joannidis14, Sandra Oeyen15, Tilemachos Zafeiridis16, Brian Marsh17, Finn H. Andersen18,19,
Rui Moreno20, Maurizio Cecconi21, Susannah Leaver22, Ariane Boumendil23,24, Dylan W. De Lange25 and
Bertrand Guidet23,24 on behalf of COVIP study group
Background: The COVID-19 pandemic has led highly developed healthcare systems to the brink of collapse due to
the large numbers of patients being admitted into hospitals. One of the potential prognostic indicators in patients
with COVID-19 is frailty. The degree of frailty could be used to assist both the triage into intensive care, and decisions
regarding treatment limitations. Our study sought to determine the interaction of frailty and age in elderly COVID-19
ICU patients.
Methods: A prospective multicentre study of COVID-19 patients 70 years admitted to intensive care in 138 ICUs
from 28 countries was conducted. The primary endpoint was 30-day mortality. Frailty was assessed using the clinical
frailty scale. Additionally, comorbidities, management strategies and treatment limitations were recorded.
Results: The study included 1346 patients (28% female) with a median age of 75 years (IQR 72–78, range 70–96),
16.3% were older than 80 years, and 21% of the patients were frail. The overall survival at 30 days was 59% (95% CI
56–62), with 66% (63–69) in fit, 53% (47–61) in vulnerable and 41% (35–47) in frail patients (p < 0.001). In frail patients,
there was no difference in 30-day survival between different age categories. Frailty was linked to an increased use of
treatment limitations and less use of mechanical ventilation. In a model controlling for age, disease severity, sex, treat-
ment limitations and comorbidities, frailty was independently associated with lower survival.
Conclusion: Frailty provides relevant prognostic information in elderly COVID-19 patients in addition to age and
Trial registration NCT04 321265, registered 19 March 2020.
Keywords: COVID-19, Frailty, Outcome, Elderly, Pandemia
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e first wave of the SARS-CoV-2 coronavirus dis-
ease (COVID-19) pandemic disproportionally affected
the elderly population, creating an unprecedented
influx of patients into hospital and intensive care [1].
Open Access
1 Department of Cardiology, Pulmonology and Vascular Medicine,
Medical Faculty, Heinrich-Heine-University Duesseldorf, Moorenstraße 5,
40225 Duesseldorf, Germany
Full list of author information is available at the end of the article
Page 2 of 9
Jungetal. Crit Care (2021) 25:149
Consequently, ICU capacity had to be increased several
fold [2]. Despite this, many countries struggled with lim-
ited resources and were forced into a stricter admission
policy to ICUs. For various reasons, this disproportion-
ately affected the very old subgroup of patients, with
reports of ad hoc criteria used for ICU admission, some
even based on age alone.
Over the last few years, the assessment of frailty at
admission to the ICU has become increasingly popu-
lar. e clinical frailty scale (CFS) proved to be a useful
tool in predicting the chances of ICU survival in very old
intensive care patients [35] and is one of a number of
tools commonly used to assess frailty. In previous studies,
the CFS demonstrated a high inter-rater reliability [6].
Indeed, the UK National Institute for Health and
Care Excellence (NICE) advocates the use of the CFS
in clinical decision making for patients with COVID-19
65 years [7]. Additionally, during the present COVID-
19 pandemic frailty has been found to be strongly asso-
ciated with all-cause mortality risk in hospitalised older
patients [8, 9]. Hewitt etal. showed in a study of 1564
adult (> 18 years) COVID-19 patients that frailty was
a better tool for risk stratification than age or concomi-
tant diseases [10]. In another, smaller study of 677 older
(> 65years) patients with COVID-19, frailty was associ-
ated with mortality after a mean follow-up of 34days [8].
A similar association between frailty and hospital mor-
tality was shown in a smaller cohort of 42 COVID-19
patients [9]. Bellelli etal. also demonstrated in a cohort of
105 patients that frailty could be used for risk evaluation
of COVID-19 and proposed the systematic collection of
frailty in all patients at hospital admission [11]. By con-
trast, recently published retrospective studies focusing
on non-ICU COVID-19 patients found that frailty had
no or low diagnostic or prognostic value [12, 13]. ere
is therefore need for more high-quality data on patient
outcomes to determine whether frailty, as assessed by
the CFS, and in particular its interaction with age can be
used for prognostication in critically ill elderly patients
with COVID-19 [14]. is is of paramount importance
in order to establish an ethical and medically appropriate
rationing of ICU care.
e main aim of the present study was to study the out-
come of elderly patients with COVID-19 admitted to an
intensive care unit and to study the influence of frailty on
Design andsetting
is is a prospective multicentre study of COVID-19
patients 70years old admitted to the ICU. Recruitment
took place from 19 March to 26 May 2020, in 138 inten-
sive care units in 28 countries (for a list of collaborators,
see Additional file 1, for a map of participating ICUs
and patients included see Additional file 2). e study
was planned and conducted by the very old intensive
care patients (VIP) project within the European Society
of Intensive Care Medicine (ESICM) (www. vipst udy. org)
who also endorsed the study. National coordinators were
responsible for the recruitment of ICUs, coordinated
national and local ethical permission and supervised
patient recruitment at the national level. Ethical approval
was mandatory for study participation in each coun-
try. Due to the diversity of ethical consent procedures,
some countries could recruit patients without informed
consent while the rest had to obtain it. e study delib-
erately allowed for co-enrolment of study patients to
additional observational COVID-19 studies. To limit
workload, screening failures for the study were not
recorded. e study was registered on
(ID: NCT04321265) and adhered to the European Union
General Data Privacy Regulation (GDPR) directive,
which is implemented in most participating countries.
It was agreed that the first period of the COVIP study
would stop on 26th of May 2020 corresponding with the
slowing down of the first wave of critically ill patients in
most of the participating countries. However, the study
continued to recruit in order to catch a possible second
wave. e present study included patients from the first
recruitment period. Each participating ICU included
consecutive patients up to and including those admitted
on 26th of May 2020. COVID-19 diagnosis was based on
a positive polymerase chain reaction (PCR) test. Patients
were followed up until death, 30days, and three months
after ICU admission. Similar to the previous VIP studies,
a website was set up to facilitate dissemination of infor-
mation about the study and to allow for data entry using
an electronic case report form (CRF).
Study population
Patients who were 70years or older with proven COVID-
19 and admitted to an ICU were eligible. Pre-ICU triage
was not a part of this study. To avoid duplication caused
by the transfer of a patient from one ICU to another,
each patient could only be entered once into the database
regardless of readmission, transfer or other reason. is
resulted in a single electronic CRF per patient. e refer-
ence date was day 1 of the first admission to an ICU. All
consecutive dates were numbered sequentially from the
admission date.
Data collection
Centres collected the data using a uniform online CRF.
e sequential organ-failure assessment (SOFA) score
on admission was calculated either manually or using
an online calculator in the electronic CRF as described
Page 3 of 9
Jungetal. Crit Care (2021) 25:149
previously [3, 4]. Additionally, the first arterial blood gas
analysis with pO2 [mmHg] and the FiO2 [%] to calculate
the PaO2/FiO2 index (pO2/FiO2 ratio) on admission was
recorded. Length of stay (LOS) was recorded in hours. As
described previously, [3] the electronic CRF and database
ran on a secure server set up by and stored at Aarhus
University, Aarhus, Denmark.
Frailty andcomorbidities
e frailty level prior to the acute illness and hospital
admission was assessed using the clinical frailty scale
(CFS). is is an intuitive pictographic description along
with information required to perform the assessment
[15]. e CFS defines nine classes from very fit to ter-
minally ill (Additional file 3). e required information
could be obtained either from the patient, the caregiver/
family or hospital records [4, 16]. We used the English
version of the CFS. Patients with a CFS of 1–3 were clas-
sified as fit, those with a CFS of 4 as vulnerable and a CFS
of 5 or higher as frail. CFS assessment was performed as
described previously with excellent inter-rater variation
[4]. e definitions of pre-existing comorbidities are pro-
vided in Additional file 4.
Outcome measurement
e primary endpoint was the survival status assessed
at 30days after ICU admission. e outcome at 90days
was also assessed. Data could be retrieved either directly,
from the hospital administration system or following tel-
ephone follow-up. Limitation of life-sustaining therapies
such as withholding or withdrawing organ support was
documented based on international recommendations
[17] although there is a large variation in Europe in the
use of end-of-life care [16]. e definitions of organ sup-
port are detailed in Additional file 5.
Statistical analysis
No formal sample size calculation prior to this purely
observational study was performed. e analysis plan
was finalised prior to any analysis. e primary exposure
was frailty (fit, vulnerable or frail at ICU admission), the
primary outcome was 30-day survival, and the secondary
outcomes were overall survival up to 90days after ICU
admission, organ support (vasoactive drugs, mechanical
ventilation, non-invasive ventilation and renal replace-
ment therapy) and treatment limitation. Group com-
parisons for continuous variables were performed using
the Kruskal–Wallis test if no-normally distributed, and
ANOVA if normally distributed; for categorical vari-
ables the Chi square test was used. Overall survival from
ICU admission was estimated using the Kaplan–Meier
method. If lost to follow-up at 90days, patients were cen-
sored at 30days or ICU discharge if status at 30days was
unknown. Survival between groups was compared using
the log-rank test. Incidence of organ support and treat-
ment limitation were estimated using cumulative inci-
dence analysis considering ICU death and ICU discharge
as competing risks. Comparisons between groups were
performed using Gray’s test.
Multivariate analysis of primary and secondary out-
comes: to account for the multilevel structure of the data
with individuals nested into the ICU, all multivariate
models were built including a random intercept by ICU,
assuming a Gaussian distribution for the random effect.
e random effect was tested by comparing log-likeli-
hood of two models including frailty with and without
random effect.
ree sequential random effects, multilevel Cox regres-
sion models, were used to evaluate the impact of frailty
on both 30-day and 90-day survival. First, we estimated
the impact of frailty on outcome without adjustment for
confounding using a baseline model including only frailty
(model 1). Second, to estimate the impact of frailty when
adjusting for patients’ baseline characteristics, the fol-
lowing covariates were added to model 1: age (as a con-
tinuous variable), sex, comorbidities, SOFA score, BMI,
PaO2/FiO2 (as continuous variables). ird, to evalu-
ate whether the effect of frailty was independent of ICU
management strategies, both organ support and treat-
ment limitation (model 3) were added to model 2 as time-
dependent covariates (variables start at 0 for all subjects
and are recoded to 1 only when organ support is received
or when limitation occurs). Two sequential random
effects, multilevel cause-specific Cox regression mod-
els, were used to evaluate the impact of frailty on organ
support and treatment limitation. First, we estimated the
impact of frailty on variable of interest without adjust-
ment for confounding using a baseline model including
only frailty (model 1). Second, to estimate the impact of
frailty when adjusting for patients’ baseline character-
istics the following covariates were added to model 1:
age (as a continuous variable), sex, comorbidities, SOFA
score, BMI, PaO2/FiO2 (as continuous variables).
A sensitivity analysis was conducted to investigate
whether results differ including only European patients.
All p values were two-sided, and p < 0.05 was consid-
ered statistically significant. Statistical analyses were per-
formed with R 3.2.3 software packages (R Development
Core Team, Vienna, Austria).
is study included 1346 patients from 138 ICUs across
28 countries. e median number of recruited patients
per ICU was 7 (IQR 3–12). e study flow chart is illus-
trated in Additional file 6. Survival at 30 and 90, respec-
tively, was available in 97% and 90% of the cohort.
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Jungetal. Crit Care (2021) 25:149
Frailty was assessed by an ICU physician in 55%, by
dedicated research staff in 21%, by an ICU nurse in 13%,
and by other personnel in 11% of the cases. Further
information for CFS assessment was provided by hospi-
tal records (51%), the family or caregivers (25%), by the
patient (22%) and by other information (2%). Patients
characteristics and outcome were similar whether CFS
was rated by an ICU physician or other personal and
whether the CFS was based on hospital records or other
e median age of patients was 75years (IQR 72–78,
range 70–96. Median CFS was 3 (IQR 2–4) and 20.7% of
the patients were frail (CFS 5). Only one patient had
CFS 9 so this group was not further split. Further base-
line characteristics of the study population are given in
e overall survival at 30days was 59% (95% CI 56–62).
Ninety-day survival was 52% and decreased with increas-
ing CFS as illustrated in Additional file 7. Numbers of
deaths are reported in Additional file 8. Figure1a shows
the prognostic relevance of frailty in a survival analysis.
Figure 1b illustrates the frailty category by age groups
below and above 75years. Of note, there is no difference
between age groups in frail patients.
Survival at 30days was 66% (63–69) in fit, 53% (47–61)
in vulnerable and 41% (35–47) in frail patients. e differ-
ence persisted at 90days with respective survival of 59%
(56–63), 47% (40–55) and 33% (27–39) p < 0.001). Length
of ICU stay for patients discharged alive was 15 days
(IQR7.0–29.0) in fit, 10 days (IQR 5–21) in vulnerable
and 6days (IQR 3.0–13) in frail patients (p < 0.001).
Table 2 shows models revealing the association
between frailty and outcome even after controlling for
comorbidities and disease severity and treatment strate-
gies. Frailty was associated with increased use of treat-
ment limitations and reduction in respiratory support
as shown in Fig.2 (cumulative incidences in Additional
file 9). e use of treatment limitations was significantly
higher in frail patients compared to fit patients and vul-
nerable patients (20-day cumulative incidence was 26%
(95% CI 23–29) for fit patients, 40% (33–47) for vulner-
able patients and 43% (95% CI 37–48) for frail patients
(p < 0.001). e association between frailty and treat-
ment limitation remained statistically significant after
Table 1 Patient characteristics of the study population
CFS clinical frailty scale, SOFA sequential organ failure assessment score, IQR inter-quartile range, BMI body mass index, PaO2 partial pressure of oxygen, FiO2 fraction of
inspired oxygen
All patients Fit (CFS 1–3) Vulnerable (CFS: 4) Frail (CFS: 5–9)
n = 1346 n = 874 n = 193 n = 279
Female sex
n (%) 381 (28) 209 (24) 64 (34) 108 (39)
Median (IQR) 75 (72–78) 74 (72–77) 76 (73–79) 78 (74–82)
Frailty score—CFS
Median (IQR) 3 (2–4) 2 (2–3) 4 (4–4) 6 (5–7)
SOFA score
Median (IQR) 6 (3–8) 5 (3–8) 6 (3–8) 6 (4–9)
Median (IQR) 28 (25–31) 27 (25–30) 28 (25–31) 28 (24–31)
PaO2/FiO2 (mmHg), n (%)
100 485 (37) 319 (37) 65 (35) 101 (37)
> 100–200 546 (41) 376 (43) 76 (40) 94 (34)
> 200–300 173 (13) 111 (13) 25 (13) 37 (13)
> 300 125 (9) 59 (7) 11 (12) 44 (16)
Comorbidities, n (%)
Diabetes mellitus 471 (35) 240 (27) 89 (47) 142 (51)
Ischemic heart disease 291 (22) 127 (14) 59 (31) 105 (39)
Chronic renal insufficiency 211 (16) 77 (9) 31 (17) 103 (38)
Arterial Hypertension 896 (67) 529 (61) 139 (73) 228 (82)
Pulmonary disease 314 (24) 169 (20) 60 (32) 85 (31)
Chronic heart failure 205 (15) 72 (6) 49 (26) 84 (32)
Page 5 of 9
Jungetal. Crit Care (2021) 25:149
adjustment for patient characteristics (aHR for frail vs fit
patients 2.79 (95% CI 1.96–3.91, p < 0.001)).
A sensitivity analysis showed that the findings were
similar when excluding patients from outside Europe
(N = 158).
Our study reveals three important findings in COVID-19
patients. First, frailty is a useful tool to stratify the risk
of death at one and three months after admission to the
ICU, and frailty offers an important additional prognos-
tic information to the age in patients aging 70 and older.
Second, outcome in terms of mortality in patients with a
frailty level 5 is similar in patients across all age groups
70years. ird, frailty was also associated with less use
of mechanical ventilation and a higher rate of treatment
Age is frequently associated with a higher rate of hos-
pitalisation, ICU admission and mortality in COVID-
19 patients [1820]. e high risk of mortality in older
patients together with constraints on ICU bed availability
may raise the question of rationing ICU admissions. Age
alone should not be used and may be considered ageist
[16]. For this reason, other factors should be investigated.
Frailty as assessed by the CFS is a good candidate as it
has previously been found to be strongly associated with
mortality and is easy to use in acutely ill patients.
Before the COVID-19 pandemic emerged, frailty was
already established as an important factor for outcome,
particularly in very old ICU patients. is was docu-
mented in large studies from Canada [21] and Europe,
and also in a systematic review. As a result, frailty was
suggested early on during the pandemic as a useful tool
to assist guiding therapy. In the UK, NICE issued guide-
lines advocating the use of the CFS in patients above
65 years to assist with decision making regarding ICU
admission. Here, scores of five and above were thought to
represent a worse prognosis in critically ill patients [22].
However, the evidence for using frailty was extrapolated
from pre-pandemic data [23], and as a result, this guide-
line was heavily criticised for being based on insufficient
During this pandemic, there have been many discus-
sions about the care of the old and the very old critical
ill patients. Frailty has been the focus in four studies. In
a retrospective single-centre study from Italy with 105
patients, the frailty index was found to be an independ-
ent predictor not only of in-hospital mortality but also
for ICU admission. In another single-centre study from
the UK of 215 hospitalised, non-ICU, patients both CFS
and age were associated with mortality [24]. By con-
trast, in a larger study of 1071 hospitalised patients with
COVID-19, CFS was not associated with mortality. To
date, the largest investigation of frailty in COVID-19
patients is a multicentre observational study from the
Fig. 1 a Kaplan–Meier curve illustrating survival dependent on clinical frailty scale (CFS) category: fit, vulnerable and frail. b Patients were divided
according to the age median (75 years) and survival was illustrated according to their frailty category
Page 6 of 9
Jungetal. Crit Care (2021) 25:149
UK and Italy involving 1564 patients from 11 hospitals.
e study included all hospitalised patients 18 years
admitted with COVID-19 during a defined period and
therefore differs from the present study which included
only patients 70years. ey found a large proportion
of patients were frail (CSF 5 in 51.4%) and that dis-
ease outcome was better predicted by frailty, measured
with the CFS, than either age or comorbidity alone.
e importance of chronological age in COVID-19
has been extensively documented. In a retrospective
case series, 1591 consecutive patients with a median
age of 63 years were admitted to 72 Italian hospitals
between February and March 2020 [18]. ey found
that patients with a median age of 63 years or more
had a higher mortality than younger patients; they also
required mechanical ventilation more frequently. is
result was supported by another retrospective study
from Germany in 10,021 hospitalised adult patients
from 920 different hospitals [25]. Overall, patients aged
80 years or older had the highest mortality of 72%.
ese two studies, however, focused on chronological
age alone with no frailty assessment used for outcome
e use of frailty in general, and CFS in particular, was
the focus of a recent editorial in ICM published prior to
the pandemic [26]. is suggested that risk stratification
should not be based on age alone but should include a
frailty assessment. ey also stressed the role of putting in
place a time limited trial of treatment on admission to ICU
as mortality after ICU treatment in frail elderly patients
remains high. Current and recent research has not proven
the long-term benefit of frailty assessment in these patients.
e role of frailty may therefore better inform best inter-
est decisions—i.e. whether burdens of ICU are more likely
to outweigh benefits (or vice versa) but caution should be
applied in excluding patients for ICU based on age/frailty
status alone as there may be some patients denied ICU that
still have the potential to benefit despite being frail.
e present research has several strengths [27]. It is a
multicentre trial that recruited patients from 28, mainly
European, countries. It included different types of hospitals
thus reflecting diverse health care systems underlining the
Table 2 Three sequential random effects and multilevel Cox regression models were used to evaluate the impact of frailty on both
30-days survival
First, we estimated the impact of frailty on outcome without adjustment on confounding using a baseline model including only frailty (model 1). Second, to estimate
the impact of frailty when adjusting on patients’ baseline characteristics the following covariates were added to model 1: age, sex, comorbidities, SOFA score, BMI,
PaO2/FiO2. Third, to evaluate whether the eect of frailty was independent of ICU management strategies, both organ support and treatment limitation (model 3)
were added to model 2 as time-dependent covariates. For all outcomes, signicance of the random centre eect was tested comparing the likelihood of two models
including frailty with and without random eect. Random eect was signicant for all outcomes. No violation of the proportional hazard assumption was detected in
the models
Model 1 unadjusted HR
(95% CI) p value Model 2 adjusted HR
(95% CI) p value Model 3 adjusted HR
(95% CI) p value
Survival at 30 days
Vulnerable versus fit 1.75 (1.35–2.25) < 0.001 1.55 (1.14–2.10) 0.011 1.14 (0.79–1.65) 0.4811
Frail versus fit 3.20 (2.56–4.13) < 0.001 2.41 (1.77–3.27) < 0.001 1.86 (1.36–2.52) < 0.001
Treatment limitation
Vulnerable versus fit 2.26 (1.73–2.96) < 0.001 1.7 (1.21–2.38) 0.0021
Frail versus fit 3.98 (3.08–5.21) < 0.001 2.79 (1.96–3.91) < 0.001
Mechanical ventilation
Vulnerable versus fit 0.83 (0.67–1.01) 0.055 0.92 (0.73–1.16) 0.5
Frail versus fit 0.75 (0.62–0.92) 0.005 0.69 (0.54–0.87) 0.0043
Non-invasive ventilation
Vulnerable versus fit 1.58 (1.11–2.25) 0.011 1.22 (0.79–1.88) 0.37
Frail versus fit 1.58 (1.12–2.24) 0.009 1.26 (0.8–1.95) 0.32
Non-invasive ventilation/mechanical ventilation
Vulnerable versus fit 0.94 (0.78–1.13) 0.51 1 (0.81–1.24) 0.99
Frail versus fit 0.84 (0.7–1.02) 0.069 0.74 (0.58–0.91) 0.0096
Vasoactive drugs
Vulnerable versus fit 0.99 (0.81–1.22) 0.93 1.09 (0.86–1.39) 0.47
Frail versus fit 1.01 (0.84–1.25) 0.88 0.9 (0.7–1.15) 0.44
Renal replacement therapy
Vulnerable versus fit 1.23 (0.81–1.86) 0.33 1.14 (0.7–1.87) 0.61
Frail versus fit 1.62 (1.1–2.45) 0.014 1.01 (0.59–1.65) 0.98
Page 7 of 9
Jungetal. Crit Care (2021) 25:149
validity of the results. High quality data were collected pro-
spectively despite the strain on health care systems during
the pandemic. In addition, this study focuses exclusively on
elderly patients who were admitted to an intensive care unit.
Our study, however, has a number of limitations: (1) No
data was collected about the pre-ICU triage process and
as a result we do not know how many very old critically ill
patients were denied ICU-admission. (2) it is an unblinded
study as it is difficult to conduct a blinded study for frailty.
(3) CFS is not a suitable tool to evaluate patients with
either temporary disability (For example as a result of
trauma or delirium) or stable long-term disabilities (for
example, cerebral palsy), learning disability or autism; in
the inclusion and exclusion criteria of our cohort these
considerations were not explicitly acknowledged, which
may have promoted selection bias. Furthermore, there
are other health conditions, unrelated to frailty, that can
limit activity that might lead to an artificially high CFS
that does not reflect true frailty. (4) Another limitation of
this study is the lack of functional outcome data. While
we were able to investigate associations with mortal-
ity, the extent of morbidity in survivors remains unclear.
(5) No younger patients were included in this study for
comparison. (6) e list of co-morbidities recorded was
incomplete as only the most prevalent were documented,
other comorbidities such as haematologic disorders or
those with immune deficiencies were not recorded. (7) It
was not possible to assess frailty in 7% of patients due to
insufficient information. (8) We did not record informa-
tion about the ethnic background, although it might be a
potential confounding factor [28].
Our study does throw up some ethical dilemmas. ere
is an inter-relationship between high frailty scores and
the unconscious bias of health care providers. For exam-
ple, a high CFS on admission could lead the ICU health
care provider to treat a patient less aggressively and to set
a limitation of therapy earlier in their illness. us, the
knowledge of frailty implicitly influences the outcome of
the patient. We raise this as a limitation of our study sim-
ilar to all studies describing outcomes in very old patients
[10, 11, 24, 29], but also as a "caveat" for future studies.
On the other hand, we know that frailty is the common,
multifactorial endpoint of life, and therefore the presence
of frailty per se (independent of its measurement) influ-
ences patient outcome and is thus a self-fulling prophecy.
Frailty provides relevant prognostic information in
elderly COVID-19 patients in addition to age and comor-
bidities. erefore, we recommend that a frailty assess-
ment should be routinely performed in these patients.
In times of limited resources on the ICU, a frailty assess-
ment of elderly patients could be included in a holistic
assessment of patients.
Fig. 2 Cumulative incidence of organ support and treatment limitations. a Combined mechanical ventilation (MV) and non-invasive ventilation. b
Mechanical ventilation (MV). c Vasoactive drugs. d Non-invasive ventilation (NIV). e Treatment limitations. f Renal replacement therapy (RRT)
Page 8 of 9
Jungetal. Crit Care (2021) 25:149
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s13054- 021- 03551-3.
Additional le1.: List of collaborators: COVIP-study; Description: List of
COVIP study collaborators with affiliations
Additional le2.: COVIP Country map; Distribution of study sites and
included patients per country. The first number is the number of ICUs per
country, the second the total number of included patients per country
Additional le3.: Clinical Frailty Scale; Description: Pictograms and
description of the Clinical Frailty Scale
Additional le4.: Definition of the comorbidities; Description: Detailed
definition of the comorbidities of patients included in the COVIP study
Additional le5.: Definition of organ support; Description: Definition of
organ support in recruited patients to be documented in the case report
Additional le6.: Consort flow chart; Description: Consort flow chart
illustrating screening and inclusion into the COVIP study
Additional le7.: Kaplan Meier curve illustrating survival dependent
on clinical frailty scale (CFS); Description: Kaplan Meier curve illustrating
survival dependent on clinical frailty scale (CFS) for each category
Additional le8.: Numbers of deaths; Description: Numbers of deaths
reported during the study/follow-up
Additional le9.: Survival estimates for the primary endpoint (30-day
mortality) and additional time points for fit, vulnerable and frail patients
as well as cumulative incidence of treatment limitations and treatment
modalities Description: Table of survival estimates for the primary end-
point (30-day mortality) and additional time points
The authors want to thank all investigators and study personal for their great
support of the study. The COVIP study group consists of the authors and the
following persons: Philipp Eller, Michael Joannidis, Dieter Mesotten, Pas-
cal Reper, Sandra Oeyen, Walter Swinnen, Helene Brix, Jens Brushoej, Maja
Villefrance, Helene Korvenius Nedergaard, Anders Thais Bjerregaard, Ida Riise
Balleby, Kasper Andersen, Maria Aagaard Hansen, Stine Uhrenholt, Helle
Bundgaard, Jesper Fjølner, Aliae AR Mohamed Hussein, Rehab Salah, Yasmin
Khairy NasrEldin Mohamed Ali, Kyrillos Wassim, Yumna A. Elgazzar, Samar
Tharwat, Ahmed Y. Azzam, Ayman abdelmawgoad Habib, Hazem Maarouf
Abosheaishaa, Mohammed A Azab, Susannah Leaver, Arnaud Galbois, Bertrand
Guidet, Cyril Charron, Emmanuel Guerot, Guillaume Besch, Jean-Philippe
Rigaud, Julien Maizel, Michel Djibré, Philippe Burtin, Pierre Garcon, Saad
Nseir, Xavier Valette, Nica Alexandru, Nathalie Marin, Marie Vaissiere, Gaëtan
Plantefeve, Thierry Vanderlinden, Igor Jurcisin, Buno Megarbane, Anais Cail-
lard, Arnaud Valent, Marc Garnier, Sebastien Besset, Johanna Oziel, Jean-herlé
Raphaelen, Stéphane Dauger, Guillaume Dumas, Bruno Goncalves, Gaël Piton,
Christian Jung, Raphael Romano Bruno, Malte Kelm, Georg Wolff, Eberhard
Barth, Ulrich Goebel, Eberhard Barth, Anselm Kunstein, Michael Schuster, Martin
Welte, Matthias Lutz, Patrick Meybohm, Stephan Steiner, Tudor Poerner, Hendrik
Haake, Stefan Schaller, Detlef Kindgen-Milles, Christian Meyer, Muhammed Kurt,
Karl Friedrich Kuhn, Winfried Randerath, Jakob Wollborn, Zouhir Dindane, Hans-
Joachim Kabitz, Ingo Voigt, Gonxhe Shala, Andreas Faltlhauser, Nikoletta Rovina,
Zoi Aidoni, Evangelia Chrisanthopoulou, Antonios Papadogoulas, Mohan
Gurjar, Ata Mahmoodpoor, Abdullah khudhur Ahmed, Brian Marsh, Ahmed
Elsaka, Sigal Sviri, Vittoria Comellini, Ahmed Rabha, Hazem Ahmed, Silvio a
Namendys-Silva, Abdelilah Ghannam, Martijn Groenendijk, Marieke Zegers,
Dylan W. De Lange, Alex Cornet, Mirjam Evers, Lenneke Haas, Tom Dormans,
Willem Dieperink, Luis Romundstad, Britt Sjøbø, Finn H. Andersen, Hans Frank
Strietzel, Theresa Olasveengen, Michael Hahn, Miroslaw Czuczwar, Ryszard
Gawda, Jakub Klimkiewicz, Maria de Lurdes Campos Santos, André Gordinho,
Henrique Santos, Rui Assis, Ana Isabel Pinho Oliveira, Mohamed Raafat Badawy,
David Perez-Torres, Gemma Gomà, Mercedes Ibarz Villamayor, Angela Prado
Mira, Patricia Jimeno Cubero, Susana Arias Rivera, Teresa Tomasa, David Iglesias,
Eric Mayor Vázquez, Cesar Aldecoa, Aida Fernández Ferreira, Begoña Zalba-
Etayo, Isabel Canas-Perez, Luis Tamayo-Lomas, Cristina Diaz-Rodriguez, Susana
Sancho, Jesús Priego, Enas M.Y. Abualqumboz, Momin Majed Yousuf Hilles,
Mahmoud Saleh, Nawfel Ben-HAmouda, Andrea Roberti, Alexander Dullenkopf,
Yvan Fleury, Bernardo Bollen Pinto, Joerg C. Schefold, Mohammed Al-Sadawi.
Hans Flaatten, Bernhard Wernly, Antonio Artigas, Michael Beil, Sigal Sviri, Peter
Vernon van Heerden, Wojciech Szczeklik, Muhammed Elhadi, Tilemachos
Zafeiridis, Rui Moreno, Maurizio Cecconi, Ariane Boumendil.
Authors’ contributions
All authors participated in the design and conductance of the trial and were
involved in patient recruitment and/or study organisation. The primary
statistical analysis was performed by AB; however, several authors had access
to the data. All authors participated in the interpretation of the results. CJ, HF
and BG wrote the initial draft of the manuscript. All other authors revised the
manuscript and had access to the final version.
Open Access funding enabled and organized by Projekt DEAL. The support of
the study in France by a grant from Fondation Assistance Publique-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 funded by a grant from the European Open Science Cloud (EOSC) by
the European Commission. has received funding from the
European Union’s Horizon Programme call H2020-INFRAEOSC-05-2018-2019,
grant agreement number 831644. No further specific funding was received.
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.
This prospective, large, international multicentre COVIP-study delineates the
value of using frailty in the prognostication of elderly patient with COVID-19
up to three months after ICU admission. CFS is independently associated with
CFS is a valuable tool to assist prognostication in elderly ICU patients during
the COVID-19 pandemic.
Ethics approval and consent to participate
Ethical approval was obtained for all sites. Informed consent was obtained if
not waived by the local ethical approval.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests. JCS reports grants
(full departmental disclosure) from Orion Pharma, Abbott Nutrition Interna-
tional, 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, Frese-
nius 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 submitted work. The
money went into departmental funds. No personal financial gain applied.
Author details
1 Department of Cardiology, Pulmonology and Vascular Medicine, Medical
Faculty, Heinrich-Heine-University Duesseldorf, Moorenstraße 5, 40225 Dues-
seldorf, Germany. 2 Department of Clinical Medicine, University of Bergen,
Bergen, Norway. 3 Department of Anaesthesia and Intensive Care, Haukeland
University Hospital, Bergen, Norway. 4 Department of Intensive Care, Aarhus
University Hospital, Aarhus, Denmark. 5 Department of Cardiology, Paracelsus
Medical University, Salzburg, Austria. 6 Department of Intensive Care Medicine,
Page 9 of 9
Jungetal. Crit Care (2021) 25:149
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 University
Medical Center, Jerusalem, Israel. 10 General Intensive Care Unit, Hadas-
sah University Medical Center, Jerusalem, Israel. 11 Center for Intensive Care
and Perioperative Medicine, Jagiellonian University Medical College, Krakow,
Poland. 12 2nd Department of Anesthesiology and Intensive Care, Medical
University of Lublin, Staszica 16, 20-081 Lublin, Poland. 13 Faculty of Medicine,
University of Tripoli, Tripoli, Libya. 14 Division of Intensive Care and Emergency
Medicine, Department of Internal Medicine, Medical University Innsbruck,
Innsbruck, Austria. 15 Department of Intensive Care 1K12IC, Ghent University
Hospital, Ghent, Belgium. 16 Intensive Care Unit General Hospital of Larissa,
Larissa, Greece. 17 Mater Misericordiae University Hospital, Dublin, Ireland.
18 Department of Anaesthesia and Intensive Care, Ålesund Hospital, Ålesund,
Norway. 19 Department of Circulation and Medical Imaging, Norwegian Univer-
sity of Science and Technology, Trondheim, Norway. 20 Unidade de Cuidados
Intensivos Neurocríticos e Trauma, Hospital de São José, Centro Hospitalar
Universitário de Lisboa Central, Faculdade de Ciências Médicas de Lisboa, Nova
Médical School, Lisbon, Portugal. 21 Department of Anaesthesia, IRCCS Instituto
Clínico Humanitas, Humanitas University, Milan, Italy. 22 General Intensive Care,
St George’s University Hospitals NHS Foundation Trust, London, UK. 23 Sor-
bonne 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. 24 Assistance Publique
- Hôpitaux de Paris, Hôpital Saint-Antoine, service de réanimation médicale,
75012 Paris, France. 25 Department of Intensive Care Medicine, University Medi-
cal Center, University Utrecht, Utrecht, The Netherlands.
Received: 17 February 2021 Accepted: 25 March 2021
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Full-text available
Purpose Frailty is a valuable predictor for outcome in elderly ICU patients, and has been suggested to be used in various decision-making processes prior to and during an ICU admission. There are many instruments developed to assess frailty, but few of them can be used in emergency situations. In this setting the clinical frailty scale (CFS) is frequently used. The present study is a sub-study within a larger outcome study of elderly ICU patients in Europe (the VIP-2 study) in order to document the reliability of the CFS. Materials and methods From the VIP-2 study, 129 ICUs in 20 countries participated in this sub-study. The patients were acute admissions ≥ 80 years of age and frailty was assessed at admission by two independent observers using the CFS. Information was obtained from the patient, if not feasible, from the family/caregivers or from hospital files. The profession of the rater and source of data were recorded along with the score. Interrater variability was calculated using linear weighted kappa analysis. Results 1923 pairs of assessors were included and background data of patients were similar to the whole cohort ( n = 3920). We found a very high inter-rater agreement (weighted kappa 0.86), also in subgroup analyses. The agreement when comparing information from family or hospital records was better than using only direct patient information, and pairs of raters from same profession performed better than from different professions. Conclusions Overall, we documented a high reliability using CFS in this setting. This frailty score could be used more frequently in elderly ICU patients in order to create a more holistic and realistic impression of the patient´s condition prior to ICU admission.
Full-text available
Objective To describe outcomes within different ethnic groups of a cohort of hospitalised patients with confirmed COVID-19 infection. To quantify and describe the impact of a number of prognostic factors, including frailty and inflammatory markers. Setting Five acute National Health Service Hospitals in east London. Design Prospectively defined observational study using registry data. Participants 1737 patients aged 16 years or over admitted to hospital with confirmed COVID-19 infection between 1 January and 13 May 2020. Main outcome measures The primary outcome was 30-day mortality from time of first hospital admission with COVID-19 diagnosis during or prior to admission. Secondary outcomes were 90-day mortality, intensive care unit (ICU) admission, ICU and hospital length of stay and type and duration of organ support. Multivariable survival analyses were adjusted for potential confounders. Results 1737 were included in our analysis of whom 511 had died by day 30 (29%). 538 (31%) were from Asian, 340 (20%) black and 707 (40%) white backgrounds. Compared with white patients, those from minority ethnic backgrounds were younger, with differing comorbidity profiles and less frailty. Asian and black patients were more likely to be admitted to ICU and to receive invasive ventilation (OR 1.54, (95% CI 1.06 to 2.23); p=0.023 and OR 1.80 (95% CI 1.20 to 2.71); p=0.005, respectively). After adjustment for age and sex, patients from Asian (HR 1.49 (95% CI 1.19 to 1.86); p<0.001) and black (HR 1.30 (95% CI 1.02 to 1.65); p=0.036) backgrounds were more likely to die. These findings persisted across a range of risk factor-adjusted analyses accounting for major comorbidities, obesity, smoking, frailty and ABO blood group. Conclusions Patients from Asian and black backgrounds had higher mortality from COVID-19 infection despite controlling for all previously identified confounders and frailty. Higher rates of invasive ventilation indicate greater acute disease severity. Our analyses suggest that patients of Asian and black backgrounds suffered disproportionate rates of premature death from COVID-19.
Full-text available
Purpose: To describe acute respiratory distress syndrome (ARDS) severity, ventilation management, and the outcomes of ICU patients with laboratory-confirmed COVID-19 and to determine risk factors of 90-day mortality post-ICU admission. Methods: COVID-ICU is a multi-center, prospective cohort study conducted in 138 hospitals in France, Belgium, and Switzerland. Demographic, clinical, respiratory support, adjunctive interventions, ICU length-of-stay, and survival data were collected. Results: From February 25 to May 4, 2020, 4643 patients (median [IQR] age 63 [54-71] years and SAPS II 37 [28-50]) were admitted in ICU, with day-90 post-ICU admission status available for 4244. On ICU admission, standard oxygen therapy, high-flow oxygen, and non-invasive ventilation were applied to 29%, 19%, and 6% patients, respectively. 2635 (63%) patients were intubated during the first 24 h whereas overall 3376 (80%) received invasive mechanical ventilation (MV) at one point during their ICU stay. Median (IQR) positive end-expiratory and plateau pressures were 12 (10-14) cmH2O, and 24 (21-27) cmH2O, respectively. The mechanical power transmitted by the MV to the lung was 26.5 (18.6-34.9) J/min. Paralyzing agents and prone position were applied to 88% and 70% of patients intubated at Day-1, respectively. Pulmonary embolism and ventilator-associated pneumonia were diagnosed in 207 (9%) and 1209 (58%) of these patients. On day 90, 1298/4244 (31%) patients had died. Among patients who received invasive or non-invasive ventilation on the day of ICU admission, day-90 mortality increased with the severity of ARDS at ICU admission (30%, 34%, and 50% for mild, moderate, and severe ARDS, respectively) and decreased from 42 to 25% over the study period. Early independent predictors of 90-day mortality were older age, immunosuppression, severe obesity, diabetes, higher renal and cardiovascular SOFA score components, lower PaO2/FiO2 ratio and a shorter time between first symptoms and ICU admission. Conclusion: Among more than 4000 critically ill patients with COVID-19 admitted to our ICUs, 90-day mortality was 31% and decreased from 42 to 25% over the study period. Mortality was higher in older, diabetic, obese and severe ARDS patients.
Full-text available
Abstract Background: There is a need for more observational studies across different clinical settings to better understand the epidemiology of the novel COVID-19 infection. Evidence on clinical characteristics of COVID-19 infection is scarce in secondary care settings in Western populations. Methods: We describe the clinical characteristics of all consecutive COVID-19 positive patients (n = 215) admitted to the acute medical unit at Fairfield General Hospital (secondary care setting) between 23 March 2020 and 30 April 2020 based on the outcome at discharge (group 1: alive or group 2: deceased). We investigated the risk factors that were associated with mortality using binary logistic regression analysis. Kaplan-Meir (KM) curves were generated by following the outcome in all patients until 12 May 2020. Results: The median age of our cohort was 74 years with a predominance of Caucasians (87.4%) and males (62%). Of the 215 patients, 86 (40%) died. A higher proportion of patients who died were frail (group 2: 63 vs group 1: 37%, p < 0.001), with a higher prevalence of cardiovascular disease (group 2: 58 vs group 1: 33%, p < 0.001) and respiratory diseases (group 2: 38 vs group 1: 25%, p = 0.03). In the multivariate logistic regression models, older age (odds ratio (OR) 1.03; p = 0.03), frailty (OR 5.1; p < 0.001) and lower estimated glomerular filtration rate (eGFR) on admission (OR 0.98; p = 0.01) were significant predictors of inpatient mortality. KM curves showed a significantly shorter survival time in the frail older patients. Conclusion: Older age and frailty are chief risk factors associated with mortality in COVID-19 patients hospitalised to an acute medical unit at secondary care level. A holistic approach by incorporating these factors is warranted in the management of patients with COVID-19 infection.
Background: COVID-19 has disproportionately affected older people. Objective: to investigate whether frailty is associated with all-cause mortality in older hospital inpatients, with COVID-19. Design: cohort study. Setting: secondary care acute hospital. Participants: six hundred and seventy-seven consecutive inpatients aged 65 years and over. Methods: Cox proportional hazards models were used to examine the association of frailty with mortality. Frailty was assessed at baseline, according to the Clinical Frailty Scale (CFS), where higher categories indicate worse frailty. Analyses were adjusted for age, sex, deprivation, ethnicity, previous admissions and acute illness severity. Results: six hundred and sixty-four patients were classified according to CFS. Two hundred and seventy-one died, during a mean follow-up of 34.3 days. Worse frailty at baseline was associated with increased mortality risk, even after full adjustment (p = 0.004). Patients with CFS 4 and CFS 5 had non-significant increased mortality risks, compared to those with CFS 1-3. Patients with CFS 6 had a 2.13-fold (95% CI 1.34-3.38) and those with CFS 7-9 had a 1.79-fold (95% CI 1.12-2.88) increased mortality risk, compared to those with CFS 1-3 (p = 0.001 and 0.016, respectively). Older age, male sex and acute illness severity were also associated with increased mortality risk. Conclusions: frailty is associated with all-cause mortality risk in older inpatients with COVID-19.
The coronavirus (COVID-19) pandemic is disproportionately affecting older people and those with underlying comorbidities. Guidelines are needed to help clinicians make decisions regarding appropriate use of limited NHS critical care resources. In response to the pandemic, the National Institute for Health and Care Excellence published guidance that employs the Clinical Frailty Scale (CFS) in a decision-making flowchart to assist clinicians in assessing older individuals’ suitability for critical care. This commentary raises some important limitations to this use of the CFS and cautions against the potential for unintended impacts. The COVID-19 pandemic has allowed the widespread implementation of the CFS with limited training or expert oversight. The CFS is primarily being used to assess older individuals’ risk of adverse outcome in critical care, and to ration access to care on this basis. While some form of resource allocation strategy is necessary for emergencies, the implementation of this guideline in the absence of significant pressure on resources may reduce the likelihood of older people with frailty, who wish to be considered for critical care, being appropriately considered, and has the potential to reinforce the socio-economic gradient in health. Our incomplete understanding of this novel disease means that there is a need for research investigating the short-term predictive abilities of the CFS on critical care outcomes in COVID-19. Additionally, a review of the impact of stratifying older people by CFS score, as a rationing strategy is necessary in order to assess its acceptability to older people as well its potential for disparate impacts.