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Increased 30-day mortality in very old ICU patients with COVID-19 compared to patients with respiratory failure without COVID-19

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Purpose: The number of patients ≥ 80 years admitted into critical care is increasing. Coronavirus disease 2019 (COVID-19) added another challenge for clinical decisions for both admission and limitation of life-sustaining treatments (LLST). We aimed to compare the characteristics and mortality of very old critically ill patients with or without COVID-19 with a focus on LLST. Methods: Patients 80 years or older with acute respiratory failure were recruited from the VIP2 and COVIP studies. Baseline patient characteristics, interventions in intensive care unit (ICU) and outcomes (30-day survival) were recorded. COVID patients were matched to non-COVID patients based on the following factors: age (± 2 years), Sequential Organ Failure Assessment (SOFA) score (± 2 points), clinical frailty scale (± 1 point), gender and region on a 1:2 ratio. Specific ICU procedures and LLST were compared between the cohorts by means of cumulative incidence curves taking into account the competing risk of discharge and death. Results: 693 COVID patients were compared to 1393 non-COVID patients. COVID patients were younger, less frail, less severely ill with lower SOFA score, but were treated more often with invasive mechanical ventilation (MV) and had a lower 30-day survival. 404 COVID patients could be matched to 666 non-COVID patients. For COVID patients, withholding and withdrawing of LST were more frequent than for non-COVID and the 30-day survival was almost half compared to non-COVID patients. Conclusion: Very old COVID patients have a different trajectory than non-COVID patients. Whether this finding is due to a decision policy with more active treatment limitation or to an inherent higher risk of death due to COVID-19 is unclear.
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Intensive Care Med
https://doi.org/10.1007/s00134-022-06642-z
ORIGINAL
Increased 30-day mortality invery old ICU
patients withCOVID-19 compared topatients
withrespiratory failure withoutCOVID-19
Bertrand Guidet1,2* , Christian Jung3, Hans Flaatten4,5, Jesper Fjølner6, Antonio Artigas7,
Bernardo Bollen Pinto8, Joerg C. Schefold9, Michael Beil10, Sviri Sigal10, Peter Vernon van Heerden11,
Wojciech Szczeklik12, Michael Joannidis13, Sandra Oeyen14, Eumorfia Kondili15, Brian Marsh16,
Finn H. Andersen17,18, Rui Moreno19, Maurizio Cecconi20, Susannah Leaver21, Dylan W. De Lange22
and Ariane Boumendil1,2 on behalf of the VIP2 and COVIP study groups
© 2022 Springer-Verlag GmbH Germany, part of Springer Nature
Abstract
Purpose: The number of patients 80 years admitted into critical care is increasing. Coronavirus disease 2019
(COVID-19) added another challenge for clinical decisions for both admission and limitation of life-sustaining treat-
ments (LLST). We aimed to compare the characteristics and mortality of very old critically ill patients with or without
COVID-19 with a focus on LLST.
Methods: Patients 80 years or older with acute respiratory failure were recruited from the VIP2 and COVIP stud-
ies. Baseline patient characteristics, interventions in intensive care unit (ICU) and outcomes (30-day survival) were
recorded. COVID patients were matched to non-COVID patients based on the following factors: age (± 2 years),
Sequential Organ Failure Assessment (SOFA) score (± 2 points), clinical frailty scale (± 1 point), gender and region on
a 1:2 ratio. Specific ICU procedures and LLST were compared between the cohorts by means of cumulative incidence
curves taking into account the competing risk of discharge and death.
Results: 693 COVID patients were compared to 1393 non-COVID patients. COVID patients were younger, less frail,
less severely ill with lower SOFA score, but were treated more often with invasive mechanical ventilation (MV) and
had a lower 30-day survival. 404 COVID patients could be matched to 666 non-COVID patients. For COVID patients,
withholding and withdrawing of LST were more frequent than for non-COVID and the 30-day survival was almost half
compared to non-COVID patients.
*Correspondence: bertrand.guidet@aphp.fr
1 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, Medical Intensive Care, Sorbonne
Universités, 184 rue du Faubourg Saint Antoine, 75012 Paris, France
Full author information is available at the end of the article
The members of the VIP2, COVIP study groups are listed in
acknowledgements.
Introduction
An ageing population leads to more old patients being
admitted to intensive care units (ICU) worldwide [1].
Patients 80years account for around 20% of all admis-
sions to ICUs and this is projected to increase sharply in
the next three decades [2]. is change in ICU patients’
characteristics will require substantial resources, while
many countries even today are facing a shortage of ICU
beds [35]. e general challenge coping with old patients
is even more acute during periods of surge, such as with
the ongoing coronavirus disease 2019 (COVID-19) pan-
demic. Previous studies have identified an increased mor-
tality among patients aged 80years or older [6] and recent
studies on COVID-19 patients have confirmed the poor
prognosis of critically ill patients in this age group [7]. Dur-
ing the COVID-19 pandemic, the old COVID-19 patients
may not only have been “selected” prior to admission, but
also earlier and more frequent decisions to limit life-sus-
taining treatment (LLST) during the ICU stay may have
been made[8]. Specific tools, such as the Clinical Frailty
Scale (CFS) [9, 10], together with severity assessment using
the Sequential Organ Failure Assessment (SOFA) score are
commonly used, to help the decision-making process [11].
We have combined data from two prospective interna-
tional studies including patients over 80years [12]. e
first, VIP2 included patients prior to the pandemic (non-
COVID patients) [12], while the COVIP study included
COVID-19 patients exclusively (COVID group) [13].
e aim of the study was to test the assumption that old
patients prior to and during the pandemic received equal
treatment and had similar outcomes.
Methods
Study participants anddata collection
Participants included in this analysis were enrolled to
one of two prospective observational studies of very old
intensive care patients (VIP2 and COVIP) [13, 14]. Par-
ticipating critical care units recruited consecutive admis-
sions of patients over 80years during a 6-month period
in 2018–19 (VIP 2) for non-COVID patients and patients
over 70years with proven SARS-CoV-2 infection from
March 2020 to January 2021 (COVIP). e studies were
registered at clinicaltrials.gov (VIP2: NCT0337069;
COVIP: NCT04321265). e European Society of Inten-
sive Care Medicine (ESICM) endorsed both studies.
e distribution of participating ICUs and the patients
included in each country are presented in supplemental
material 2. National coordinators were responsible for
the recruitment of ICUs, coordinated national and local
ethical permissions, and supervised patient recruitment
at the national level. Ethical approval was mandatory for
the study participation in each country. Due to the diver-
sity of ethical consent procedures, some countries could
recruit patients without informed consent, while others
had to collect it at admission.
Patients were followed up until death or up to 30days
after ICU admission. A website was set up to facilitate
dissemination of information about the two studies and
to allow for data entry using an electronic case report
form (eCRF).
Study population
Among patients in the VIP2, we selected patients admit-
ted to the ICU for respiratory failure with or without
circulatory failure. ese two predefined subgroups
accounted for 1393 out of the 3920 patients recruited
(35%). In the COVIP study, all patients had proven SARS-
CoV-2 infection and were 70years of age or older. In that
study only patients 80years and over were included in
the present analysis, representing 693 patients out of a
total of 3289 patients (21%).
Pre-ICU triage was not recorded. To avoid duplica-
tion 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 rea-
son. is resulted in a single eCRF 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 online using a eCRF. e
SOFA score on admission was calculated either manually
Conclusion: Very old COVID patients have a different trajectory than non-COVID patients. Whether this finding is due
to a decision policy with more active treatment limitation or to an inherent higher risk of death due to COVID-19 is
unclear.
Keywords: Old patients, COVID, Intensive care, Treatment limitation, Mortality
Take‑home message
Very old COVID-19 patients have different characteristics and
1-month survival than non-COVID patients. Increased limitation of
life-sustaining treatments might contribute to the reduced survival
in COVID-19 patients.
or using an online calculator in the eCRF as described
previously [15]. e electronic case record form and
database ran on a secure server composed and stored at
Aarhus University, Aarhus, Denmark.
e frailty level prior to the acute illness and hospi-
tal admission was assessed using the CFS version 1.0
[15, 16]. e CFS defines nine classes from very fit to
terminally ill (1–9). e required information could be
obtained from either the patient, the caregiver/family, or
hospital records. We used the English version or native
language of the CFS. Patients were classified as fit (CFS
of 1–3), vulnerable (CFS 4) and frail (CFS of 5 or higher).
We found a very high inter-rater agreement (weighted
kappa 0.86), in a study including 1923 pairs of assessors
from the VIP-2 study [17].
Type and duration of organ support were also docu-
mented. is included for respiratory support: invasive
mechanical ventilation (MV) and non-invasive ventila-
tion (NIV). High flow nasal oxygen was not considered as
NIV. For circulatory support, this included use of vasoac-
tive drugs and for renal support continuous or intermit-
tent renal replacement therapy (RRT).
Outcome measurement
e primary endpoint was the survival-status assessed
at 30days after ICU admission. Data could be retrieved
either directly, from the hospital administration system
or collected using active follow-up by telephone.
LLST such as withholding or withdrawing life-support-
ing treatments was documented based on international
recommendations and national guidelines [18]. We docu-
mented the delay between ICU admission and LLST and
the delay between LLST and death.
Role ofthe funding source
e COVIP study was supported by a grant from Fon-
dation Assistance Publique-Hôpitaux de Paris pour la
recherche in France. 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. No
further specific funding was received.
Statistical analysis
No formal sample size calculation prior to these two
purely observational studies was performed. e analysis
plan was finalised prior to any analysis.
Patients’ baseline characteristics were summarised
by frequencies and percentages for categorical vari-
ables and medians and interquartile ranges (IQRs) for
continuous variables. Comparisons of COVID and
non-COVID patients’ characteristics were performed
using the Wilcoxon test for continuous variables and
the χ2 or Fisher exact test for categorical variables as
appropriate.
e crude overall survival up to 30 days after ICU
admission was estimated by the Kaplan–Meier method
and compared between COVID and non-COVID
patients using a log-rank test.
e incidence of organ support and treatment limita-
tions were estimated using cumulative incidence analysis
considering ICU death and ICU discharge as competing
risks. Comparisons between COVID and non-COVID
patients were performed using Gray’s test.
Patients’ characteristics were different in COVID and
non-COVID patients. To compare survival and incidence
of organ support adjusting for patients’ characteristics,
COVID patients werematchedto non-COVID-selected
patients based on the following factors: age (± 2 years),
sofa (± 2 points), clinical frailty scale (± 1 point), gender
and region on a 1:2 ratio.
Pairs were identified as correlated groups of data with
pair identifier marked as cluster in the analysis. Robust
sandwich estimators were used to estimate the variance–
covariance matrix of the regression coefficient estimates
accounting for clustering of patients with pair.
To confirm the results, we assessed the impact of
COVID-19 on outcome using propensity score analysis.
e same variables used for the matching procedure were
used to build the score (namely, age, SOFA, CFS, gender
and region). Generalised boosted regression were used
to estimate the propensity score and cases were then
weighted to estimate the average “COVID” effect. e
analysis used the twang package in R.
A multivariate Cox model regression model also quan-
tified independent effect of COVID-19 and other covari-
ates on 30-day survival. is analysis was first performed
on the whole sample including COVID and non-COVID
patients and repeated separately in the two cohorts.
Two subgroup analyses were performed to compare
survival of COVID and non-COVID patients, respec-
tively, in patients receiving respiratory support (either
NIV or invasive MV) and in patients without treatment
limitation. In these two subgroups, COVID patients were
matched to non-COVID patients based on the same
matching criteria and using the same ratio.
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.
Results
A total of 2086 ICU patients 80 years were included,
1393 from the VIP2 study and 693 from the COVIP
study. Most patients were included from countries
located in northern Europe, Israel and USA (Supplement
2). e number of patients included in northern Europe,
Israel and USA was 78.8% in the non-COVID cohort and
64.9% in the COVID cohort (p < 0.001). In the matched
paired analysis, 666 non-COVID patients from the VIP2
study were matched to 404 COVID patients from the
COVIP study.
Table 1 shows the characteristics of the participants
enrolled in both cohorts.
COVID compared to non-COVID patients were more
frequently male, younger, less frail, and less acutely ill
according to SOFA score. ey also were more likely
to be treated with invasive mechanical ventilation, but
received less NIV. ere was no difference in the use of
vasoactive drugs or renal replacement therapy (RRT).
Cumulative incidences of organ support are reported
in supplemental material 3. e ICU length of stay was
longer in COVID compared to non-COVID patients in
survivors as well as in non-survivors.
Overall survival was lower among COVID patients
(Table 2) (Fig. 1a). At 1month, survival was only 38%
in COVID patients compared with 57% in non-COVID
patients. Decisions to limit LST were more frequent
among COVID patients (Table 2) (Fig. 2a). e time
between ICU admission and withholding of LST was
similar in both cohorts, whereas the time between admis-
sion and withdrawing was significantly longer in the
COVID group.
ere were regional differences. In ICUs located in
south Europe there was less limitation of Life sustain-
ing treatment, more invasive mechanical ventilation and
less non-invasive mechanical ventilation compared with
ICUs located in north Europe (SEM5).
404 COVID patients were matched to 666 non-COVID
patients. 289 COVID patients could not be matched and
were thus excluded from the analysis. Excluded patients
were older but with similar 30-day survival (See SEM4).
e matched paired analysis reduced most of the base-
line differences between the two groups (Table3).
Matched COVID and non-COVID patients have simi-
lar age, gender, SOFA, CFS, and activity of daily living
(ADL). Invasive mechanical ventilation was more fre-
quent among COVID patients together with vasoactive
Table 1 Patients andICU stay characteristics inboth cohorts
ADL Activity of daily living, CFS clinical frailty scale, NIV non invasive mechanical ventilation, LOS length of stay, LST life sustaining therapy
COVID patients Non-COVID patients p-value
(n=693) (n=1393)
Age
Med (range) (IQR) 82 (80–96) (81–85) 83 (80–99) (81–87) < 0.0001
Gender
Male 456 (65.8%) 742 (53.3%) < 0.0001
Female 237 (34.2%) 651 (46.7%)
Frailty
Fit (CFS 1–3) 285 (47.2%) 438 (31.6%) < 0.0001
Vulnerable (CFS 4) 104 (17.2%) 314 (22.6%)
Frail (CFS 5–8) 215 (35.6%) 636 (45.8%)
Sofa
Med (range) (IQR) 5 (0–17) (3–8) 6 (0–18) (4–9) < 0.0001
ADL (Katz)
Med (range) (IQR) 6 (0–6) (4–6) 6 (0–6) (4–6) 0.062
Mechanical ventilation
Yes 404 (58.7%) 724 (52.1%) 0.005
NIV
Yes 215 (31.5%) 616 (44.4%) < 0.0001
Vasoactive drugs
Yes 392 (57.6%) 751 (54%) 0.13
Renal replacement therapy
Yes 70 (10.2%) 148 (10.7%) 0.81
ICU LOS in alive patients
Med (range) (IQR) 7 (0.08–85) (3.79–14) 4.65 (0.04–120) (2.11–9.01) < 0.0001
ICU LOS in dead patients
Med (range) (IQR) 7 (0.04–53) (3.04–13.75) 5 (0.04–85.5) (2–10.06) < 0.0002
drugs, while NIV was more frequent among non-COVID
patients. e ICU LOS was longer for COVID patients
in both survivors and non-survivors. Overall survival
was lower among COVID patients (Table4) (Fig.1b). At
1month, survival was 39% among COVID patients, while
it was 66% among non-COVID patients.
LLST was applied very differently: withholding was
applied in 51.1% vs 25.9% and withdrawing 25.9% vs
14.1% in COVID and non-COVID patients, respectively
(Table4). e cumulative incidence of limitation (with-
holding or withdrawing) 7days after ICU admission was
43% in COVID patients and 24% in non-COVID patients
(Fig.2b).
e timing of LST was similar in both cohorts but the
delay between withholding treatment and death was
longer among COVID patients. In the subgroup analy-
sis of patients without treatment limitation among 392
COVID patients, 152 could be matched to 230 non-
COVID patients. Survival was also lower in the COVID
patients (Fig.3) 62% (95%CI 55–71) at day 30 compared
to 79% (95%CI 74–85) in non-COVID patients. In the
subgroup analysis of patients receiving respiratory sup-
port among 374 COVID patients, 195 could be matched
to 291 non-COVID patients. Survival was still much
lower in the COVID patients (SEM 5).
e weighted sample built with the propensity score
method had similar characteristics for COVID and
non-COVID patients (SEM 6) and the weighted haz-
ard ratio (HR) for 1-month survival in non-COVID vs
COVID patients was 0.53 (0.46–0.61); p < 0.0001. In the
matched sample the estimated for 1-month survival in
non-COVID vs COVID patients was 0.46 (0.34–0.62);
p < 0.0001. In the multivariate Cox-regression analysis,
non-COVID patients also had a better 1-month survival
with an HR of 0.50; 95%CI 0.40–0.62; p > 0.00001 (SEM
7).
After adjustment for baseline characteristics, whatever
the statistical method used, we found consistent results
with lower 30-day survival among COVID patients.
Discussion
In this comparative study, old COVID-19 patients were
found to have a substantially higher mortality and had
treatment limitations instituted more frequently com-
pared to similar old non-COVID patients. is differ-
ence was maintained in a matched pair analysis. e
COVID patients demonstrate different baseline charac-
teristics compared with non-COVID patients suggest-
ing a selection bias on admission. Our COVIP cohort
includes patients from the first two surges from March
2020 to January 2021. During this period, the shortage
of ICU beds prompted drastic actions in many coun-
tries: planned surgical activity was almost stopped
and ICU capacity was expanded with potential impact
on quality of care [19, 20]. An unusually high pressure
on ICU bed availability led several countries to issue
revised recommendation for ICU admission. Many
included age as one of the criteria to be considered
Table 2 Survival andlimitation oflife sustaining treatments inboth cohorts
ADL Activity of daily living, CFS clinical frailty scale, NIV non invasive mechanical ventilation, LOS length of stay, LST life sustaining therapy
COVID patients Non-COVID patients p-value
(n=693) (n=1393)
Overall survival (OS)
At 1 days (range) 97% (96–98) 97% (96–98) < 0.001
At 3 days (range) 87% (85–90) 89% (87–90)
At 7 days (range) 72% (69–75) 77% (74–79)
At 30 days (range) 38% (35–42) 57% (55–60)
Withholding LST
Yes 267 (39.1%) 456 (33.1%) 0.009
Withdrawing LST
Yes 136 (19.9%) 212 (15.4%) 0.012
Time admission—withholding
Med (range) (IQR) 2 ( 6 to 50) (1–6) 1 ( 2 to 80) (1–4) 0.094
Time withholding—death
Med (range) (IQR) 4 (0–71) (1.5–7) 3 (0–184) (1–7) 0.27
Time admission—withdrawing
Med (range) (IQR) 7 (1–46) (4–13) 4 (1–54) (2–7) < 0.0001
Time withdrawing—death
Med (range) (IQR) 0 (0–18) (0–1) 0 (0–165) (0–1) 0.49
[2123]. However, in most countries age was not a
stand-alone factor and chronological age was not con-
sidered a legitimate criterion for triage and was only
used as part of a combination of risk factors [24]. e
rationale for this position was sociocultural respect for
the elderly. International guidelines stated that: “even
under triage, we must uphold our obligation to care for
all patients as best as possible under difficult circum-
stances” [25].
Given the pressure on the allocation of scarce ICU
resources, it is important to analyse the case mix of old
patients admitted prior to and during the pandemic. We
Fig. 1 Survival curves. a Unpaired analysis. b Matched paired analysis
speculate that selection (pre-ICU triage) of old COVID
patients led to patients admitted being less frail, with a
higher functional status and a lower disease severity.
Hence, patients with a better chance of recovery were
admitted with the aim of maximising the number of lives
saved. e participating countries were hit differently by
the initial phases of the pandemic. Some countries, such
as France, were affected much more than others, such as
Germany or Norway. A high demand for ICU beds might
have contributed to more stringent admission criteria.
Fig. 2 Limitation of life sustaining treatments. a Unpaired analysis. b Matched paired analysis
Table 3 Patients andICU stay characteristics inthe matched analysis
COVID patients Non-COVID patients p-value
(n=404) (n=666)
Age
Median (range) (IQR) 82 (80–95) (81–84) 82 (80–94) (81–84) 0.33
Gender
Male 286 (70.8%) 484 (72.7%) 0.55
Female 118 (29.2%) 182 (27.3%)
Frailty
Fit (CFS 1–3) 209 (51.7%) 360 (54.1%) 0.51
Vulnerable (CFS 4) 87 (21.5%) 149 (22.4%)
Frail (CFS 5–8) 108 (26.7%) 157 (23.6%)
Sofa
Med (range) (IQR) 5 (0–15) (3–8) 5 (0–15) (3–7) 0.66
Katz
Med (range) (IQR) 6 (0–6) (5–6) 6 (0–6) (5–6) 0.93
Mechanical ventilation
Yes 225 (56%) 293 (44%) < 0.0002
NIV
Yes 128 (32.1%) 335 (50.3%) < 0.0001
Vasoactive drugs
Yes 225 (56.1%) 323 (48.5%) 0.019
Renal replacement therapy
Yes 36 (9%) 50 (7.5%) 0.46
ICU LOS in alive patients
Med (range) (IQR) 7 (0.08–54) (3.4–14.0) 4.98 (0.08–100) (2.42–9.7) < 0.0004
ICU LOS in dead patients
Med (range) (IQR) 7.96 (0.08–53) (3.38–14) 5.92 (0.08–60) (2.08–9.27) < 0.0009
Table 4 Survival andlimitation oflife sustaining treatments inthe matched analysis
COVID patients Non-COVID patients p-value
(n=404) (n=666)
Overall survival (OS)
At 1 days (range) 98% (97–100) 98% (97–99) < 0.001
At 3 days (range) 88% (85–91) 93% (91–95)
At 7 days (range) 75% (71–80) 84% (81–87)
At 30 days (range) 39% (34–44) 66% (62–70)
Withholding LST
Yes 204 (51.1%) 171 (25.9%) < 0.0001
Withdrawing LST
Yes 103 (25.9%) 93 (14.1%) < 0.0001
Time admission—withholding
Med (range) (IQR) 1 ( 6 to 50) (1–6) 1 ( 2 to 45) (1–4) 0.36
Time withholding—death
Med (range) (IQR) 4 (0–71) (2–8) 4 (0–103) (1–7) 0.35
Time admission—withdrawing
Med (range) (IQR) 7 (1–46) (4–13) 5 (1–54) (2–7) < 0.0001
Time withdrawing—death
Med (range) (IQR) 0 (0–18) (0–1) 0 (0–39) (0–1) 0.72
is hypothesis is supported by data showing a lower
percentage of frail patients among French COVID
patients than in other European patients with COVID-
19. e percentage of frail patients in the European
COVIP study was 20% [14], while it was only 9.1% in
the French COVID ICU study [26].
Decisions to limit life-sustaining treatment in ICU
seem to increase in parallel with the pressure to admit
patients to ICU during the pandemic [27].
In addition to a possible stricter selection, COVID
patients were more likely to have decisions to limit
LST put in place. is result is counter intuitive con-
sidering that the COVID patients admitted had a bet-
ter baseline condition. Withholding of treatment in
COVID patients occurred in 39.1% compared with
33.1% in non-COVID patients and withdrawal (includ-
ing previous WH decisions) in 19.3% compared with
15.4% in non-COVID patients. e difference was even
more pronounced in the matched pair analysis with
almost twice as many decisions to withhold treatment
in COVID compared with non-COVID patients. e
results in the non-COVID patients agree with our pre-
vious study [28] and with other recent international
studies [29, 30] and are in line with a documented rela-
tion between treatment limitations and pressure on
intensive care units in elderly patients [27]. We provide
additional information about the timing of LLST. e
delay between ICU admission and LLST was similar in
the two groups. e delay between LLST and death was
longer among COVID patients compared with non-
COVID patients. is suggests that such difficult deci-
sions were protracted in COVID patients translating
into longer ICU length of stay.
Changes in end-of-life decision making in ICU over
time, have been elegantly shown in a study comparing
two time periods from 22 countries [30]. Significantly
more treatment limitations occurred in the 2015–2016
cohort compared with the 1999–2000 cohort. Our results
together with the results above suggest that we are now
more likely to limit LST even if regional variability exits
with less LLST in Eastern and Southern countries [31].
Although an increased use of LLST can explain some
of the differences in mortality, this is probably not the
whole explanation. Since COVID patients in general were
less critically ill at admission and had better scores on
pre-ICU frailty and ADL, it is also tempting to blame the
specific pathophysiology of COVID-19, in particular the
rapidly progressive pulmonary failure. is may explain
the increased use of MV in this group. In addition, the
increased ICU LOS indicates different patient trajecto-
ries in old COVID patients [7].
We have not been able to find other published matched
pair analyses of elderly COVID-19 vs non-COVID
patients, but there are reports comparing patient out-
comes from influenza-virus to SARS-CoV-2 virus. In
a study from Germany [32] that compared outcomes in
Fig. 3 Survival curves in matched subgroup of patients without treatment limitation
2343 hospitalised COVID-19 patients with 6762 patients
admitted with influenza, the overall in-hospital mortal-
ity was more than twofold higher in COVID-19 than in
influenza patients and the need for ICU admission and
MV was also substantially higher. In a study from Mexico,
outcomes in acute respiratory distress syndrome (ARDS)
patients with and without COVID-19 were compared
[33] and ICU mortality was 3.7 times higher for patients
with COVID-19-induced ARDS compared to similar
patients with Influenza A-H1N1. Likewise, the need for
ICU admission, length of stay in the ICU, and mortality
were also higher among COVID patients compared with
Influenza patients in a Finish study [34]. Observations
like these confirm the high severity of illness of critically
ill COVID-19 patients and are probably also relevant to
our findings, although admission status was better.
Decisions to institute LST might have also contrib-
uted to the high mortality of COVID patients [35, 36],
considering that intensivists would have to work under
considerable pressure to increase bed availability [27,
37]. However, in the matched analysis including patients
without any limitation of LST, the survival was still lower
among COVID patients suggesting that COVID per
se carried a higher risk of mortality compared to other
causes of acute respiratory failure (Fig.3).
Our study has several strengths. It included more than
2000 patients from two large prospective international
cohorts focusing on patients over the age of 80 with an
admission diagnosis of acute respiratory failure. ere
was no overlap with clear separation between the two
time periods, which both occurred in the last 4 years.
We documented the organ support provided, LLST and
the time when this occurred. We performed a matched
analysis including sub studies for mechanically ventilated
patients and patients without any LLST. e contribu-
tion from different countries enables us to generalise the
results to most ICU populations.
is study has several limitations. Recruitment of
patients was mainly in European countries. We have no
detailed information on the type of treatment that was
withheld or withdrawn. For example, a do-not-resuscitate
(DNR) order does not necessarily result in certain death,
whereas a patient denied intubation who requires MV
is likely to die. We have no long-term follow-up and no
qualitative outcomes, such as health-related quality of
life.
It is likely that only the healthiest octogenarians were
admitted. However, the duration of treatment was lim-
ited by the implementation of limitations in LST. We
did not study the mortality of patients aged 80years and
older admitted to the ICU for non-COVID causes during
the COVIP inclusion period. As documented in Brazil,
we cannot exclude there also being a higher mortality for
non-COVID patients during the COVID-19 period [38]
e respective contribution of LLST and intrinsic
severity of COVID-19 is hard to disentangle.
Conclusions
We found old COVID patients to be less severely ill and
less frail than old non-COVID patients at ICU admission,
suggesting an underlying triage process. In the matched
paired analysis, decisions to limit LST were almost twice
as likely in COVID than in non-COVID patients. e
1-month survival in the COVID patients was almost half
that of non-COVID patients. Our results suggest that
COVID-19 patients have a more aggressive disease tra-
jectory leading to a reduced 1-month survival.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s00134- 022- 06642-z.
Author details
1 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, Medical Intensive Care, Sorbonne Univer-
sités, 184 rue du Faubourg Saint Antoine, 75012 Paris, France. 2 Assistance
Publique-Hôpitaux de Paris, Hôpital Saint-Antoine, service de réanimation
médicale, 75012 Paris, France. 3 Department of Cardiology, Pulmonology
and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Dues-
seldorf, Duesseldorf, Germany. 4 Department of Clinical Medicine, University
of Bergen, Bergen, Norway. 5 Department of Anaestesia and Intensive Care,
Haukeland University Hospital, Bergen, Norway. 6 Department of Intensive
Care, Aarhus University Hospital, Aarhus, Denmark. 7 Department of Intensive
Care Medicine, CIBER Enfermedades Respiratorias, Corporacion Sanitaria
Universitaria Parc Tauli, Autonomous University of Barcelona, Sabadell, Spain.
8 Department of Acute Medicine, Geneva University Hospitals, Geneva, Swit-
zerland. 9 Department of Intensive Care Medicine, Inselspital, Universitätsspital,
University of Bern, Bern, Switzerland. 10 Medical Intensive Care, Hadassah Med-
ical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem,
Israel. 11 General Intensive Care, Hadassah Medical Center, Faculty of Medicine,
Hebrew University of Jerusalem, Jerusalem, Israel. 12 Center for Intensive Care
and Perioperative Medicine, Jagiellonian University Medical College, Krakow,
Poland. 13 Division of Intensive Care and Emergency Medicine, Depart-
ment of Internal Medicine, Medical University Innsbruck, Innsbruck, Austria.
14 Department of Intensive Care 1K12IC, Ghent University Hospital, Ghent,
Belgium. 15 Intensive Care Unit, University Hospital of Heraklion, Medical
School University of Crete, Giofirakia, Greece. 16 Mater Misericordiae University
Hospital, Dublin, Ireland. 17 Department of Anaesthesia and Intensive Care,
Ålesund Hospital, Alesund, Norway. 18 Department of Circulation and Medical
Imaging, Norwegian University of Science and Technology, Trondheim, Nor-
way. 19 Centro Hospitalar Universitário de Lisboa Central, Faculdade de Ciên-
cias Médicas de Lisboa, Nova Médical School, Unidade de Cuidados Intensivos
Neurocríticos e Trauma. Hospital de São José, Lisbon, Portugal. 20 Department
of Anaesthesia IRCCS, Instituto Clínico Humanitas, Humanitas University, Milan,
Italy. 21 General Intensive Care, St George’s University Hospitals NHS Founda-
tion Trust, London, UK. 22 Department of Intensive Care Medicine, University
Medical Center, University Utrecht, Utrecht, the Netherlands.
Acknowledgements
List of collaborators COVIP: Philipp Eller, Allgemeine Medizin Intensivstation,
Medical University Graz, Graz, Austria. Michael Joannidis, Division of Intensive
Care and Emergency Medicine, Department of Internal Medicine, Medical
University Innsbruck, Innsbruck, Austria. Dieter Mesotten, Department of
Intensive Care, Ziekenhuis Oost-Limburg, Genk, Belgium. Pascal Reper,
Department of Intensive Care, CHR Haute Senne, Soignies, Belgium. Sandra
Oeyen, Department of Intensive Care, Ghent University Hospital, Ghent,
Belgium. Walter Swinnen, Department of Intensive Care, AZ Sint-Blasius,
Dendermonde, Belgium. Helene Brix, Intensiv Behandling, Herlev og Gentofte
Hospital, Herlev, Denmark. Jens Brushoej, Intensiv, Slagelse, Slagelse, Denmark.
Maja Villefrance, Intensiv, Regionshospitalet Horsens, Horsens, Denmark.
Helene Korvenius Nedergaard, Intensive Care Unit, Odense University Hospital,
Odense, Denmark. Anders Thais Bjerregaard, Intensive, Sygehus Lillebælt,
Kolding, Denmark. Ida Riise Balleby, Intensiv, Regionshospitalet Viborg, Viborg,
Denmark. Kasper Andersen, Department of Anaesthesia and Intensive Care,
Sygehus Sønderjylland, Aabenraa, Denmark. Maria Aagaard Hansen, Intensiv
Afdeling, Regionshospitalet Herning, Herning, Denmark. Stine Uhrenholt,
Department of Anaesthesia and Intensive Care, Nordsjællands Hospital,
Hillerød, Denmark. Helle Bundgaard, Intensiv, Regionshospitalet Randers,
Randers, Denmark. Jesper Fjølner, Department of Intensive Care, Aarhus
University Hospital, Aarhus, Denmark. Aliae AR Mohamed Hussein, Medical
ICU and Isolation Centers, Assiut University Hospital, Assiut, Egypt. Rehab
Salah, Cardiology ICU, One day surgery hospital, Nasr city, Egypt. Yasmin Khairy
NasrEldin Mohamed Ali, MICU, Minia University Hospitals, Minia, Egypt. Kyrillos
Wassim, MICU, Quweisna central hospital, Quweisna, Egypt. Yumna A. Elgazzar,
MICU, Mayo Isolation Hospital, Cairo Governorate, Egypt. Samar Tharwat,
Mansoura university Hospital, Temi El amdid, Mansoura, Egypt. Ahmed Y.
Azzam, Alazhar University Hospitals, Cairo, Egypt. Ayman abdelmawgoad
habib, intermediate ccu, one day surgery, nasr city, Egypt. Hazem Maarouf
Abosheaishaa, MICU, Mostafa Mahmoud Specialized Hospital, Giza, Egypt.
Mohammed A Azab, Sherif Mokhtar Cairo University ICU, Kar Al-Ainy Cairo
University Hospital, Cairo, Egypt. Susannah Leaver, General Intensive care, St
George’s University Hospitals NHS Foundation trust, London, England. Arnaud
Galbois, Medico-surgical ICU, Hôpital Privé Claude Galien, Quincy sous Sénart,
France. Tomas Urbina, Medical intensive care unit, Saint Antoine, Paris, France.
Cyril Charron, Medical intensive care unit, Hôpital Ambroise Paré, Boulogne
Billancourt, France. Emmanuel Guerot, Medical intensive care unit, Hopital
Européen Georges Pompidou, Paris, France. Guillaume Besch, Medico-surgical
ICU, CHU de Besançon, Besançon, France. Jean-Philippe Rigaud, Medical
intensive care unit, Dieppe General Hospital, Dieppe, France. Julien Maizel,
Medical intensive care unit, CHU Amiens, Amiens, France. Michel Djibré,
Medico-surgical ICU, Tenon, Paris, France. Philippe Burtin, Surgical ICU, Clinique
Du Millenaire, Montpellier, France. Pierre Garcon, Medico-surgical ICU, Marne
La Vallee, Jossigny, France. Saad Nseir, Medical intensive care unit, CHU Lille,
Lille, France. Xavier Valette, Medical intensive care unit, CHU de Caen, Caen,
France. Nica Alexandru, Medico-surgical ICU, Compiegne Noyon Hospital,
Compiegne, France. Nathalie Marin, Medical intensive care unit, Cochin, Paris,
France. Marie Vaissiere, Medico-surgical ICU, CH Pau, Pau, France. Gaëtan
Plantefeve, Medico-surgical ICU, Victor Dupouy, Argenteuil, France. Thierry
Vanderlinden, Medical intensive care unit, CH Saint Philibert, Lomme lez Lille,
France. Igor Jurcisin, Medico-surgical ICU, Beaujon, Clichy, France. Buno
Megarbane, Medical intensive care unit, Lariboisière, Paris, France. Anais
Caillard, Surgical ICU, Lariboisière, Paris, France. Arnaud Valent, Surgical ICU,
Saint-Louis, Paris, France. Marc Garnier, Surgical ICU, Saint Antoine, Paris,
France. Sebastien Besset, Medico-surgical ICU, Louis Mourier, Colombes,
France. Johanna Oziel, Medico-surgical ICU, Avicenne, Bobigny, France.
Jean-herlé RAPHALEN, Medico-surgical ICU, Centre hospitalier de Versailles, Le
Chesnay, France. Stéphane Dauger, Pediatric Intensive and Intermediate Care
Unit, Robert Debré, Paris, France. Guillaume Dumas, Medical intensive care
unit, Saint-Louis, Paris, France. Bruno Goncalves, Medico-surgical ICU,
Sainte-Anne, Paris, France. Gaël Piton, Medical ICU, CHU de Besancon,
Besançon, France. Eberhard Barth, Anesthesiologic Intensive Care Department,
University Hospital Ulm, Ulm, Germany. Ulrich Goebel, Klinik für Anästhesie
und operative Intensivmedizin, St. Franziskus-Hospital Münster, Münster,
Germany. Eberhard Barth, IOI-Interdisziplinäre Operative Intensivmedizin,
University Hospital Ulm, Ulm, Germany. Anselm Kunstein, MX01, Uniklinik
Düsseldorf, Düsseldorf, Germany. Michael Schuster, Anästhesie-Intensivstation,
Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz,
Germany. Martin Welte, Interdiszipinaere Operative Intensivstation, Klinik fuer
Anaesthesiologie und operative Intensivmedizin, Klinikum Darmstadt GmbH,
Darmstadt, Germany. Matthias Lutz, Internistische Intensivstation, Uniklinik
Schleswig Holstein Campus Kiel, Kiel, Germany. Patrick Meybohm, Klinik für
Anästhesie und operative Intensivmedizin, University Hospital Würzburg,
Würzburg, Germany. Stephan Steiner, ICU, St Vincenz, Limburg, Germany.
Tudor Poerner, ITS, Marienhospital Aachen, Aachen, Germany. Hendrik Haake,
Internistische Intensivstation I und II, Kliniken Maria Hilf, Mönchengladbach,
Germany. Stefan Schaller, 43i, Charité—Universitätsmedizin Berlin, Berlin,
Germany. Stefan Schaller, 44i, Charité—Universitätsmedizin Berlin, Berlin,
Germany. Stefan Schaller, 8i, Charité—Universitätsmedizin Berlin, Berlin,
Germany. Detlef Kindgen-Milles, CIA1, University Hospital Duesseldorf,
Duesseldorf, Germany. Christian Meyer, Intensivstation, Evangelisches
Krankenhaus Düsseldorf, Düsseldorf, Germany. Muhammed Kurt, 32,
Florence-Nightingale Krankenhaus, Duesseldorf, Germany. Karl Friedrich Kuhn,
144i, Charité—Universitätsmedizin Berlin, Berlin, Germany. Winfried Randerath,
Intensivpflege Bethanien, Krankenhaus Bethanien GmbH, Solingen, Solingen,
Germany. Jakob Wollborn, Anaesthesiologiesche Intensivtherapiestation,
Medical Center—University of Freiburg, Freiburg, Germany. Zouhir Dindane,
Interdisziplinäre Intensivstation, Städtische Kliniken Mönchengladbach,
Mönchengladbach, Germany. Hans-Joachim Kabitz, I01, Klinikum Konstanz,
Konstanz, Germany. Ingo Voigt, Kardiologisch-internistische Intensivstation,
Elisabeth-Krankenhaus Essen, Essen, Germany. Gonxhe Shala, Station 2,
Johanna Etienne Krankenhaus, Neuss, Germany. Andreas Faltlhauser,
Interdisziplinäre Intensivmedizin, Kliniken Nordoberpfalz AG, Klinikum Weiden,
Weiden, Germany. Nikoletta Rovina, ICU 1st Department of Pulmonary
Medicine Athens Medical School, National and Kapodistrian University of
Athens, Sotiria Hospital, Athens, Greece. Zoi Aidoni, ICU, University General
Hospital Ahepa, Thessaloniki, Greece. Evangelia Chrisanthopoulou, 2nd
Department of Critical Care, UNIVERSITY HOSPITAL (ATTIKON), Haidari, Greece.
Antonios Papadogoulas, ICU, GENERAL HOSPITAL OF LARISSA, Larissa, Greece.
Mohan Gurjar, Critical Care Medicine, Sanjay Gandhi Postgraduate Institute of
Medical Sciences (SGPGIMS), Lucknow, India. Ata Mahmoodpoor, General,
Imam Reza, Tabriz, Iran. Abdullah khudhur Ahmed, Baghdad teaching hospital,
Baghdad, Iraq. Brian Marsh, Department of Critical Care Medicine, Mater
Misericordiae University Hospital, Dublin, Ireland. Ahmed Elsaka, Covid ICU,
Cork University Hospital, Cork, Ireland. Sigal Sviri, Corona ICU, Hadassah
University Medical Center, Jerusalem, Israel. Vittoria Comellini, Terapia Intensiva
Respiratoria, Policlinico S. Orsola-Malpighi, Bologna, Italy. Ahmed Rabha, MICU,
Askar, Suq Elkamis, Libya. Hazem Ahmed, MICU, Tripoli University Hospital,
Tripoli, Libya. Silvio A. Namendys-Silva, Department of Critical Care Medicine,
Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Mexico
City, Mexico. Abdelilah Ghannam, Service de Réanimation—Institut National
d´Oncologie, CHU Ibn Sina de Rabat, Rabat, Morocco. Martijn Groenendijk, ICU
Department, Alrijne Zorggroep, Leiderdorp, Netherland. Marieke Zegers,
Intensive Care department Radboudumc, Radboudumc, Nijmegen,
Netherland. Dylan de Lange, ICU departement, UMC Utrecht, Utrecht,
Netherland. Alex Cornet, Intensive Care Center, Medisch Spectrum Twente,
Enschede, Netherland. Mirjam Evers, ICU Department, Canisius Wilhelmlina
Ziekenhuis, Nijmegen, Netherland. Lenneke Haas, Intensive care, Diakonessen-
huis Utrecht, Utrecht, Netherland. Tom Dormans, Zuyderland Heerlen,
Zuyderland Medical Center, Heerlen, Netherland. Willem Dieperink,
Department of Critical Care, University Medical Center Groningen, Groningen,
Netherland. Luis Romundstad, Department of Critical Care and Emergencies,
Oslo University Hospital, Rikshospitalet Medical, Oslo, Norway. Britt Sjøbø,
General ICU, Haukeland University Hospital, Bergen, Norway. Finn H. Andersen,
Dept. Anesthesia and Intensive Care, Surgical ICU, Ålesund Hospital, Ålesund,
Norway. Hans Frank Strietzel, ICU, Kristiansund Hospital Helse Møre og
Romsdal HF, Kristiansund N, Norway. Theresa Olasveengen, Surgical ICU, Oslo
University Hospital, Oslo, Norway. Michael Hahn, ICU, Haugesund Hospital,
Haugesund, Norway. Miroslaw Czuczwar, II Department of Anesthesiology and
Intensive Care, First Independent Teaching Hospital No. 1, Lublin, Poland.
Ryszard Gawda, Department of Anesthesiology and Intensive Care, Opole
University Hospital, Opole, Poland. Jakub Klimkiewicz, COVID-19 ICU, Military
Institute of Medicine, Warsaw, Poland. Maria de Lurdes Campos Santos,
Infectious Diseases ICU, Centro Hospitalar e Universitário São João, Porto,
Portugal.André Gordinho, Serviço de Medicina Intensiva, Hospital de Beatriz
Ângelo, Loures, Portugal. Henrique Santos, D, Centro Hospitalar Tráz os Montes
e Alto Dour, Vila Real, Portugal. Rui Assis, Serviço
Author contributions
BG, AB, HF and CJ analysed the data and wrote the first draft of the manu-
script. All other authors gave guidance and improved the paper. All authors
read and approved the final manuscript.
Funding
This study was endorsed by the ESICM. Free support for running the electronic
database and was granted from Aarhus University, Denmark. 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 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 Forschungskommission of the Medi-
cal 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 VIP2 and COVIP steering committee. The anonymised data can be
requested from the authors if required.
Declarations
Conflicts of interest
The authors declare that they have no competing interests.
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.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in pub-
lished maps and institutional affiliations.
Received: 22 November 2021 Accepted: 5 February 2022
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... The COVID-19 pandemic exposed all HCPs to the new situations. Physicians had to perform rationing and triage patients without enough information about this new diagnosis with a poor prognosis [42][43][44]. The hospitals were overloaded, and the nursing staff had less time for patient care, which could lead to poor quality of care. ...
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Background: Providing palliative care at the end of life (EOL) in intensive care units (ICUs) seems to be modified during the COVID-19 pandemic with potential burden of moral distress to health care providers (HCPs). We seek to assess the practice of EOL care during the COVID-19 pandemic in ICUs in the Czech Republic focusing on the level of moral distress and its possible modifiable factors. Methods: Between 16 June 2021 and 16 September 2021, a national, cross-sectional study in intensive care units (ICUs) in Czech Republic was performed. All physicians and nurses working in ICUs during the COVID-19 pandemic were included in the study. For questionnaire development ACADEMY and CHERRIES guide and checklist were used. A multivariate logistic regression model was used to analyse possible modifiable factors of moral distress. Results: In total, 313 HCPs (14.5% out of all HCPs who opened the questionnaire) fully completed the survey. Results showed that 51.8% (n = 162) of respondents were exposed to moral distress during the COVID-19 pandemic. 63.1% (n = 113) of nurses and 71.6% of (n = 96) physicians had experience with the perception of inappropriate care. If inappropriate care was perceived, a higher chance for the occurrence of moral distress for HCPs (OR, 1.854; CI, 1.057-3.252; p = 0.0312) was found. When patients died with dignity, the chance for moral distress was lower (OR, 0.235; CI, 0.128-0.430; p < 0.001). The three most often reported differences in palliative care practice during pandemic were health system congestion, personnel factors, and characteristics of COVID-19 infection. Conclusions: HCPs working at ICUs experienced significant moral distress during the COVID-19 pandemic in the Czech Republic. The major sources were perceiving inappropriate care and dying of patients without dignity. Improvement of the decision-making process and communication at the end of life could lead to a better ethical and safety climate. Trial registration: NCT04910243 .
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In Europe there are increasing numbers of old (more than 65 years old) and very old (more than 80 years old) patients (very old intensive care patients - VIPs) (Figure 1). In addition to combinations of chronic conditions (multi-morbidity), there are geriatric disabilities and functional limitations, with a profound impact on management in the ICU and afterwards [1].
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Background: The Clinical Frailty Scale (CFS) is frequently used to measure frailty in critically ill adults. There is wide variation in the approach to analysing the relationship between the CFS score and mortality after admission to the ICU. This study aimed to evaluate the influence of modelling approach on the association between the CFS score and short-term mortality and quantify the prognostic value of frailty in this context. Methods: We analysed data from two multicentre prospective cohort studies which enrolled intensive care unit patients ≥ 80 years old in 26 countries. The primary outcome was mortality within 30-days from admission to the ICU. Logistic regression models for both ICU and 30-day mortality included the CFS score as either a categorical, continuous or dichotomous variable and were adjusted for patient's age, sex, reason for admission to the ICU, and admission Sequential Organ Failure Assessment score. Results: The median age in the sample of 7487 consecutive patients was 84 years (IQR 81-87). The highest fraction of new prognostic information from frailty in the context of 30-day mortality was observed when the CFS score was treated as either a categorical variable using all original levels of frailty or a nonlinear continuous variable and was equal to 9% using these modelling approaches (p < 0.001). The relationship between the CFS score and mortality was nonlinear (p < 0.01). Conclusion: Knowledge about a patient's frailty status adds a substantial amount of new prognostic information at the moment of admission to the ICU. Arbitrary simplification of the CFS score into fewer groups than originally intended leads to a loss of information and should be avoided. Trial registration NCT03134807 (VIP1), NCT03370692 (VIP2).
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Background The COVID-19 pandemic is a heavy burden in terms of health care resources. Future decision-making policies require consistent data on the management and prognosis of the older patients (> 70 years old) with COVID-19 admitted in the intensive care unit (ICU). Methods Characteristics, management, and prognosis of critically ill old patients (> 70 years) were extracted from the international prospective COVID-ICU database. A propensity score weighted-comparison evaluated the impact of intubation upon admission on Day-90 mortality. Results The analysis included 1199 (28% of the COVID-ICU cohort) patients (median [interquartile] age 74 [72–78] years). Fifty-three percent, 31%, and 16% were 70–74, 75–79, and over 80 years old, respectively. The most frequent comorbidities were chronic hypertension (62%), diabetes (30%), and chronic respiratory disease (25%). Median Clinical Frailty Scale was 3 (2–3). Upon admission, the PaO 2 /FiO 2 ratio was 154 (105–222). 740 (62%) patients were intubated on Day-1 and eventually 938 (78%) during their ICU stay. Overall Day-90 mortality was 46% and reached 67% among the 193 patients over 80 years old. Mortality was higher in older patients, diabetics, and those with a lower PaO 2 /FiO 2 ratio upon admission, cardiovascular dysfunction, and a shorter time between first symptoms and ICU admission. In propensity analysis, early intubation at ICU admission was associated with a significantly higher Day-90 mortality (42% vs 28%; hazard ratio 1.68; 95% CI 1.24–2.27; p < 0·001). Conclusion Patients over 70 years old represented more than a quarter of the COVID-19 population admitted in the participating ICUs during the first wave. Day-90 mortality was 46%, with dismal outcomes reported for patients older than 80 years or those intubated upon ICU admission.
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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 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, treatment limitations and comorbidities, frailty was independently associated with lower survival.
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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 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, treatment 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 Clinicaltrials.gov: NCT04321265 , registered 19 March 2020.
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PurposeTo assess whether intensive care unit (ICU) outcomes for patients not affected by coronavirus disease 2019 (COVID-19) worsened during the COVID-19 pandemic.Methods Retrospective cohort study including prospectively collected information of patients admitted to 165 ICUs in a hospital network in Brazil between 2011 and 2020. Association between admission in 2020 and worse hospital outcomes was performed using different techniques, including assessment of changes in illness severity of admitted patients, a variable life-adjusted display of mortality during 2020, a multivariate mixed regression model with admission year as both fixed effect and random slope adjusted for SAPS 3 score, an analysis of trends in performance using standardized mortality ratio (SMR) and standardized resource use (SRU), and perturbation analysis.ResultsA total of 644,644 admissions were considered. After excluding readmissions and patients with COVID-19, 514,219 patients were available for analysis. Non-COVID-19 patients admitted in 2020 had slightly lower age and SAPS 3 score but a higher mortality (6.4%) when compared with previous years (2019: 5.6%; 2018: 6.1%). Variable-adjusted life display (VLAD) in 2020 increased but started to decrease as the number of COVID-19 cases increased; this trend reversed as number of COVID cases reduced but recurred on the second wave. After logistic regression, being admitted in 2020 was associated with higher mortality when compared to previous years from 2016 and 2019. Individual ICUs standardized mortality ratio also increased during 2020 (higher SMR) while resource use remained constant, suggesting worsening performance. A perturbation analysis further confirmed changes in ICU outcomes for non-COVID-19 patients.Conclusion Hospital outcomes of non-COVID-19 critically ill patients worsened during the pandemic in 2020, possibly resulting in an increased number of deaths in critically ill non-COVID patients.
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Background End-of-life practices vary among intensive care units (ICUs) worldwide. Differences can result in variable use of disproportionate or non-beneficial life-sustaining interventions across diverse world regions. This study investigated global disparities in end-of-life practices. Methods In this prospective, multinational, observational study, consecutive adult ICU patients who died or had a limitation of life-sustaining treatment (withholding or withdrawing life-sustaining therapy and active shortening of the dying process) during a 6-month period between Sept 1, 2015, and Sept 30, 2016, were recruited from 199 ICUs in 36 countries. The primary outcome was the end-of-life practice as defined by the end-of-life categories: withholding or withdrawing life-sustaining therapy, active shortening of the dying process, or failed cardiopulmonary resuscitation (CPR). Patients with brain death were included in a separate predefined end-of-life category. Data collection included patient characteristics, diagnoses, end-of-life decisions and their timing related to admission and discharge, or death, with comparisons across different regions. Patients were studied until death or 2 months from the first limitation decision. Findings Of 87 951 patients admitted to ICU, 12 850 (14·6%) were included in the study population. The number of patients categorised into each of the different end-of-life categories were significantly different for each region (p<0·001). Limitation of life-sustaining treatment occurred in 10 401 patients (11·8% of 87 951 ICU admissions and 80·9% of 12 850 in the study population). The most common limitation was withholding life-sustaining treatment (5661 [44·1%]), followed by withdrawing life-sustaining treatment (4680 [36·4%]). More treatment withdrawing was observed in Northern Europe (1217 [52·8%] of 2305) and Australia/New Zealand (247 [45·7%] of 541) than in Latin America (33 [5·8%] of 571) and Africa (21 [13·0%] of 162). Shortening of the dying process was uncommon across all regions (60 [0·5%]). One in five patients with treatment limitations survived hospitalisation. Death due to failed CPR occurred in 1799 (14%) of the study population, and brain death occurred in 650 (5·1%). Failure of CPR occurred less frequently in Northern Europe (85 [3·7%] of 2305), Australia/New Zealand (23 [4·3%] of 541), and North America (78 [8·5%] of 918) than in Africa (106 [65·4%] of 162), Latin America (160 [28·0%] of 571), and Southern Europe (590 [22·5%] of 2622). Factors associated with treatment limitations were region, age, and diagnoses (acute and chronic), and country end-of-life legislation. Interpretation Limitation of life-sustaining therapies is common worldwide with regional variability. Withholding treatment is more common than withdrawing treatment. Variations in type, frequency, and timing of end-of-life decisions were observed. Recognising regional differences and the reasons behind these differences might help improve end-of-life care worldwide. Funding None.
Article
Purpose To evaluate the association between provider religion and religiosity and consensus about end-of-life care and explore if geographical and institutional factors contribute to variability in practice. Materials and Methods Using a modified Delphi method 22 end-of-life issues consisting of 35 definitions and 46 statements were evaluated in 32 countries in North America, South America, Eastern Europe, Western Europe, Asia, Australia and South Africa. A multidisciplinary, expert group from specialties treating patients at the end-of-life within each participating institution assessed the association between 7 key statements and geography, religion, religiosity and institutional factors likely influencing the development of consensus. Results Of 3049 participants, 1366 (45%) responded. Mean age of respondents was 45 ± 9 years and 55% were females. Following 2 Delphi rounds, consensus was obtained for 77 (95%) of 81 definitions and statements. There was a significant difference in responses across geographical regions. South African and North American respondents were more likely to encourage patients to write advance directives. Fewer Eastern European and Asian respondents agreed with withdrawing life-sustaining treatments without consent of patients or surrogates. While respondent’s religion, years in practice or institution did not affect their agreement, religiosity, physician specialty and responsibility for end-of-life decisions did. Conclusions Variability in agreement with key consensus statements about end-of-life care is related primarily to differences among providers, with provider-level variations related to differences in religiosity and specialty. Geography also plays a role in influencing some end-of-life practices. This information may help understanding ethical dilemmas and developing culturally sensitive end-of-life care strategies.