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Variations in end‐of‐life care practices in older critically ill patients with COVID‐19 in Europe

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Background: Previous studies reported regional differences in end-of-life care (EoLC) for critically ill patients in Europe. Objectives: The purpose of this post-hoc analysis of the prospective multi-centre COVIP study was to investigate variations in EoLC practices among older patients in intensive care units during the coronavirus disease 2019 pandemic. Methods: A total of 3105 critically ill patients aged 70 years and older were enrolled in this study (Central Europe: n = 1573; Northern Europe: n = 821; Southern Europe: n = 711). Generalised estimation equations were used to calculate adjusted odds ratios (aOR) to population averages. Data were adjusted for patient-specific variables (demographic, disease-specific) and health economic data (GDP, health expenditure per capita). The primary outcome was any treatment limitation, and 90-day-mortality was a secondary outcome. Results: The frequency of the primary endpoint (treatment limitation) was highest in Northern Europe (48%), intermediate in Central Europe (39%), and lowest in Southern Europe (24%). The likelihood for treatment limitations was lower in Southern than in Central Europe (aOR 0.39; 95%CI 0.21-0.73; p = 0.004), even after multivariable adjustment, whereas no statistically significant differences were observed between Northern and Central Europe (aOR 0.57; 95%CI 0.27-1.22; p = 0.15). After multivariable adjustment, no statistically relevant mortality differences were found between Northern and Central Europe (aOR 1.29; 95%CI 0.80-2.09; p = 0.30) or between Southern and Central Europe (aOR 1.07; 95%CI 0.66-1.73; p = 0.78). Conclusion: This study shows a north-to-south gradient in rates of treatment limitation in Europe, highlighting the heterogeneity of EoLC practices across countries. However, mortality rates were not affected by these results. This article is protected by copyright. All rights reserved.
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Variations in end-of-life care practices in older
critically ill patients with COVID-19 in Europe
Bernhard Wernly (1,2)*, Richard Rezar (3)*, Hans Flaatten (4), Michael Beil (5), Jesper
Fjølner (6), Raphael Romano Bruno (7), Antonio Artigas (8), Bernardo Bollen Pinto (9),
Joerg C. Schefold (10), Malte Kelm (7), Sviri Sigal (5), Peter Vernon van Heerden (11),
Wojciech Szczeklik (12), Muhammed Elhadi (13), Michael Joannidis (14), Sandra Oeyen
(15), Georg Wolff (7), Brian Marsh (16), Finn H. Andersen (17), Rui Moreno (18), Susannah
Leaver (19), Sarah Wernly (1,2), Ariane Boumendil (20), Dylan W. De Lange (21), Bertrand
Guidet (20), Christian Jung (7) on behalf of the COVIP study group
* both authors share first authorship
Affiliations
1. Department of Internal Medicine, General Hospital Oberndorf, Teaching Hospital of the Paracelsus
Medical University Salzburg, Salzburg, Austria (bernhard@wernly.net, sarah@wernly.net).
2. Center for Public Health and Healthcare Research, Paracelsus Medical University of Salzburg, Austria.
3. Clinic of Internal Medicine II, Department of Cardiology and Intensive Care Medicine, Paracelsus
Medical University, Salzburg, Austria (r.rezar@salk.at).
4. Department of Clinical Medicine, University of Bergen, Department of Anaestesia and Intensive Care,
Haukeland University Hospital, Bergen, Norway (hans.flaatten@uib.no).
5. Department of Medical Intensive Care, Hadassah Medical Center and Faculty of Medicine, Hebrew
University of Jerusalem, Jerusalem, Israel (beil@doctors.org.uk; sigals@hadassah.org.il).
6. Department of Anesthesia and Intensive Care, Viborg Regional Hospital, Viborg, Denmark
(jesperfjoelner@clin.au.dk).
7. Heinrich-Heine-University Düsseldorf, Medical Faculty, Department of Cardiology, Pulmonology and
Vascular Medicine, Düsseldorf, Germany (raphael.bruno@med.uni-duesseldorf.de;
malte.kelm@med.uni-duesseldorf.de; georg.wolff@med.uni-duesseldorf.de; christian.jung@med.uni-
duesseldorf.de).
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8. Department of Intensive Care Medicine, CIBER Enfermedades Respiratorias, Corporacion Sanitaria
Universitaria Parc Tauli, Autonomous University of Barcelona, Sabadell, Spain (aartigas@tauli.cat).
9. Department of Acute Medicine, Geneva University Hospitals, Geneva, Switzerland
(bernardo.bollenpinto@hcuge.ch).
10. Department of Intensive Care Medicine, Inselspital, Universitätsspital, University of Bern, Bern,
Switzerland (joerg.schefold@insel.ch).
11. Department of Anesthesia, Intensive Care and Pain Medicine, Hadassah Medical Center and Faculty of
Medicine, Hebrew University of Jerusalem, Jerusalem, Israel (vernon@hadassah.org.il).
12. Jagiellonian University Medical College, Center for Intensive Care and Perioperative Medicine, Krakow,
Poland (wojciech.szczeklik@uj.edu.pl).
13. Faculty of Medicine, University of Tripoli, Tripoli, Libya (muhammed.elhadi.uot@gmail.com).
14. Division of Intensive Care and Emergency Medicine, Department of Internal Medicine, Medical
University Innsbruck, Innsbruck, Austria (michael.joannidis@tirol-kliniken.at).
15. Department of Intensive Care 1K12IC Ghent University Hospital, Ghent, Belgium
(sandra.oeyen@ugent.be).
16. Mater Misericordiae University Hospital, Dublin, Ireland (bmarsh@mater.ie).
17. Department Of Anaesthesia and Intensive Care, Ålesund Hospital, Ålesund, Norway & Department of
Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
(finn.andersen@ntnu.no).
18. Hospital de São José, Centro Hospitalar Universitário de Lisboa Central, Faculdade de Ciências
Médicas de Lisboa, Nova Médical School, Lisbon, FCSaude - Universidade da Beira Interior, Portugal
(r.moreno@mail.telepac.pt).
19. General Intensive Care, St. George' 's University Hospital NHS Foundation Trust, London, United
Kingdom (susannahleaver@nhs.net).
20. Inserm, Service de réanimation, Sorbonne Université, Hôpital Saint-Antoine, Institut Pierre-Louis
d'épidémiologie et de santé publique, AP-HP, 184, Rue du Faubourg-Saint-Antoine, Paris, France
(ariane.boumendil@upmc.fr; bertrand.guidet@aphp.fr).
21. Department of Intensive Care Medicine, University Medical Center, University Utrecht, Utrecht, The
Netherlands (D.W.deLange-3@umcutrecht.nl).
Running Headline: End-of-life care in COVID-19
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Abstract
Background
Previous studies reported regional differences in end-of-life care (EoLC) for critically ill
patients in Europe.
Objectives
post-hoc analysis of the prospective The purpose of this multi-centre COVIP study was to
investigate variations in EoLC practices among older patients in intensive care units during
the coronavirus disease 2019 pandemic.
Methods
A total of 3105 critically ill patients aged 70 years and older were enrolled in this study
(Central Europe: n=1573; Northern Europe: n=821; Southern Europe: n=711). Generalised
estimation equations were used to calculate adjusted odds ratios (aOR) to population
averages. Data were adjusted for patient-specific variables (demographic, disease-specific)
and health economic data (GDP, health expenditure per capita). The primary outcome was
any treatment limitation, and 90-day-mortality was a secondary outcome.
Results
The frequency of the primary endpoint (treatment limitation) was highest in Northern Europe
(48%), intermediate in Central Europe (39%), and lowest in Southern Europe (24%). The
likelihood for treatment limitations was lower in Southern than in Central Europe (aOR 0.39;
95%CI 0.21-0.73; p=0.004), even after multivariable adjustment, whereas no statistically
significant differences were observed between Northern and Central Europe (aOR 0.57;
95%CI 0.27-1.22; p=0.15). After multivariable adjustment, no statistically relevant mortality
differences were found between Northern and Central Europe (aOR 1.29; 95%CI 0.80-2.09;
p=0.30) or between Southern and Central Europe (aOR 1.07; 95%CI 0.66-1.73; p=0.78).
Conclusion
This study shows a north-to-south gradient in rates of treatment limitation in Europe,
highlighting the heterogeneity of EoLC practices across countries. However, mortality rates
were not affected by these results.
Keywords: critical care, COVID-19, frail elderly, resuscitation orders, public health systems
research
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Introduction
Many individuals receive prolonged and invasive intensive care treatments at the end of their
lives. The definition of treatment goals represents an important component in the treatment
of critically ill patients, and therapy limitations need to be discussed when specific therapies
become futile and/or death inevitable. These decisions can be difficult, and practices vary
by physician, hospital, and country. A general principle of medicine is to do no harm.
However, there is a fine line between where modern intensive care encounters biological
limits, especially in very old and severely ill patients. Finally and most importantly, the
preservation of an individual's dignity and autonomy should always come first, and
protracted suffering should be avoided. Avidan et al. recently confirmed in the Ethicus-2
study that end-of-life practices are subject to regional variations [1]. These differences are
observed worldwide, which is presumably due to social, religious, and/or legal reasons [1,2].
However, a deeper understanding of these regional differences could contribute to a better
mutual understanding and help decision makers adjust guidelines and organisational
frameworks accordingly.
However, the data from Avidan et al. predate the COVID-19 era [1]. The COVID-19
pandemic put immense pressure on Europe's heterogenous but highly developed health-
care systems, and in particular on intensive care units [3]. Even before COVID-19, older
critically ill patients posed not only a medical but also an ethical challenge, as the added
value of intensive care for older patients and especially octogenarians is the subject of
ongoing debate [47]. In addition to a clinical assessment and a structured evaluation of the
severity of an acute illness, the evaluation of frailtywhich reflects the functional capacity of
patients prior to the acute illnesshas also been proven to be helpful in increasingly ageing
societies [8]. In particular, the Clinical Frailty Scale (CFS), among others, has been
confirmed by our group as an independent predictor of mortality in critically ill older patients
[2,9,10].
The aim of this study was to investigate regional variations within Europe with regards to the
use of treatment limitations in older (≥70 years) critically ill patients with COVID-19.
Furthermore, baseline characteristics and mortality between three distinct European regions
were compared.
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Materials and Methods
COVIP
COVIP (COVID-19 in very old intensive care patients) is a multi-centre investigation which is
part of the Very Old Intensive Care Patients (VIP) project (www.vipstudy.org). The study has
been endorsed by the European Society of Intensive Care Medicine (ESICM). COVIP was
registered on ClinicalTrials.gov (NCT04321265). The COVIP study adhered to the
European Union General Data Privacy Regulation (GDPR) directive. As in the previous VIP
studies, the national coordinators recruited the intensive care units (ICU), coordinated
national and local ethical permissions, and supervised patient recruitment at the national
level [2,10]. The COVIP study protocol is available at https://vipstudy.org/covip-study/.
Study participants and general information
In this post-hoc analysis of the prospective COVIP study, all patients aged 70 years and
older admitted to the ICU with confirmed COVID-19 by means of (polymerase chain
reaction/PCR) with complete data on the primary endpoint (any treatment limitation) were
included. All patients admitted between 19 March 2020 and 4 February 2021 were included.
Data collection started on ICU admission. The admission day was defined as day one, and
all consecutive days were numbered sequentially from the admission date. For each patient,
baseline characteristics (including age, sex, main reason for admission, and frailty) and
management strategies (including use of renal replacement therapy [RRT], mechanical
ventilation [MV], non-invasive ventilation [NIV], and use of vasoactive drugs) were
documented. Also, any treatment limitations (treatment withheld or withdrawn) were
documented. Patients were defined as belonging to Central Europe (Austria, Belgium,
France, Germany, Poland, Switzerland), Northern Europe (Denmark, Ireland, Netherlands,
Norway, United Kingdom) or Southern Europe (Greece, Israel, Italy, Portugal, Romania,
Spain). In this respect, we have used a country classification similar to the Ethicus-2 study
[11]. 90-day follow-up was obtained by means of telephone interviews. The primary
endpoint of this study was any treatment limitation, and the secondary endpoints were ICU-,
30- and 90-day-mortality and rates withholding and withdrawing treatment. Frailty was
assessed by the Clinical Frailty Score (CFS), and the respective visual and simple
descriptions were used with permission [8,12]. The gross domestic product (GDP) per
capita for 2019 in US-$ was retrieved from the International Money Fund (IMF) [13], the
human development index (HDI) from the United Nations Development Program (UNDP)
[14], and the total (compulsory, out-of-pocket, voluntary) amount of health spending per
capita in US-$ in 2019 from the Organisation for Economic Co-operation and Development
(OECD) [15]. A general review of the literature prior to conducting the study was conducted,
and the corresponding search terms can be found in Supplement 1.
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Each study site obtained institutional research ethics board (IRB) approval. Many countries
could recruit patients without informed consent while others had to collect informed consent
as ethical consent practices vary across Europe.
Statistical analysis
The primary exposure was belonging to one of the three European regions. Central Europe
was chosen as the reference category and compared to either Northern or Southern Europe.
Missing data (including loss to follow-up) were addressed by listwise deletion. The data are
likely to be clustered at an ICU level. To compensate for possible confounders, a multilevel
regression analysis was used. As health economic data do not vary within a given cluster
(as patients in one ICU belong to one country), generalised estimation equations (GEE) with
robust standard errors were used to produce population average odds ratios for the binary
endpoints treatment limitation and mortality. Adjusted odds ratios (aORs) and respective
95% confidence intervals (95%CI) were obtained. We fitted a multilevel linear regression
model to evaluate the association of the primary exposure with the length of ICU stay as
dependent variable and obtained regression coefficients and respective 95%CI. The
regression analyses were conducted using only robust estimators of the standard errors and
not in the sense of robustness against violations of normality assumptions as for the
methods (e.g., Mann-Whitney tests) used for the univariate analyses. Model-1 includes only
the ICU as a panel. Model-2 includes patient-specific factors (sex, age per year, sequential
organ failure assessment (SOFA) score per point, frailty score per CFS point). Model-3
adds the number of ICU beds per 100,000 inhabitants. Model-4 includes the health
economic data (HDI). Model-5 adds the treatment limitations (treatment withdrawal or
withholding) and calculates mortality and the ICU length of stay. Sensitivity analyses
stratifying treatment limitation were done. Continous data are given as median ±
interquartile range (IQR) and compared using the Mann-Whitney U test or given as mean ±
standard deviation and compared using the Student's t-test. Categorical data are given as
numbers (percentages) and compared using the chi-square test. All tests were two-sided,
and a p-value of <0.05 was considered statistically significant. Stata/IC 17 (Stata Statistical
Software: Release 17. StataCorp LLC, College Station, Texas, United States of America)
was used for all statistical analyses.
Results
Baseline demographics in Central versus Northern and Southern Europe
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In total, 3105 older (≥70 years) critically ill patients were included in this study1573 in
Central Europe, 821 in Northern and 711 in Southern. The baseline characteristics are
given in Table 1. No differences in sex distribution within regions were found. Patients in
Central Europe (23%) were more frequently older than 80 years old compared to Northern
(14%) and Southern Europe (16%) (p<0.001). Likewise, patients in Central Europe (19%)
were more likely to be frail and suffer from cardiovascular comorbidities than those from
Northern and Southern Europe (see Table 1). The ICUs included in Central Europe had a
higher median number of ICU beds (12) compared to Northern (7) and Southern Europe (10)
The results of the univariate analysis on baseline characteristics are shown in
(p<0.001).
Table 1.
Organ support and management
In the univariate analysis, ICU length of stay was significantly longer in Southern Europe
(median 21 days) than in Central and Northern Europe (median 15 days each; p<0.001).
This finding remained after performing multilevel linear regression analysis across all five
models (see table in Supplement 2). The figure in Supplement 3 shows the median length of
stay in the groups with and without treatment limitations according to the three regions.
Intubation and mechanical ventilation rates were highest in Southern Europe (85%), followed
by Central (69%) and Northern (67%) (p<0.001). Likewise, tracheostomy rates were highest
in Southern Europe (31%) and lowest in Central (16%) (p<0.001). By contrast, the use of
renal replacement therapy (RRT) was most common in Central Europe (18%), and least
common in Northern (12%) (p<0.001). The results on management strategies of the
univariate analysis are shown in Table 2.
Treatment limitation analysis
The frequency of the primary endpoint (any treatment limitation) was highest in Northern
Europe (48%), lowest in Southern (24%) and intermediate in Central (39%; p<0.001). A
decreasing incidence of withholding and withdrawing treatment was observed from north to
south (see Table 2). Table 3 shows the multivariable regression analyses. The odds for any
treatment limitation were lower in Southern Europe compared to Central (aOR 0.39 95%CI
0.21-0.73; p=0.004) even after adjustment for patient-specific characteristics, ICU beds per
100,000 population, and the HDI. No statistically significant differences were observed
between Northern and Central Europe (reference category) (aOR 0.57 95%CI 0.27-1.22;
p=0.15). The rates of withholding and withdrawing treatment showed a similar pattern, with
the highest rates in Northern Europe and the lowest rates in Southern Europe (see Table 2).
Mortality analysis
ICU mortality was highest in Southern Europe (51%), and lower in Northern (43%) and
Central Eruope (44%) (p=0.003). The 30-day mortality rate was similar in the three regions
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(see Table 2). In univariate analysis, 90-day mortality was highest in Southern Europe
(62%) and similar in Central (56%) and Northern Europe (56%) (p=0.019). After adjustment
for patient-specific characteristics, ICU beds per 100,000 population and HDI, no mortality
differences were found between Northern Europe compared to Central Europe (aOR 1.29
95%CI 0.80-2.09; p=0.30), and Southern Europe compared to Central Europe (aOR 1.07;
95%CI 0.66-1.73; p=0.78). Figure 1 shows a comparison of the survival probability of
patients from the three regions.
Sensitivity analyses
In sensitivity analyses comparing treatment limitations between European regions, there was
a consistent trend towards lower odds for any treatment limitation in Southern Europe versus
Central Europe (see Forest plot in Figure 2) and higher odds for any treatment limitation in
Northern Europe versus Central Europe (see Forest plot in Figure 3).
Discussion
In this study, differences in the incidence of treatment limitations among critically ill older
(≥70 years) patients in three European regions were investigated. A north-south divide was
observed: Treatment limitations were more common in Northern Europe than in Southern
Europe. However, the use of treatment limitations does not appear to translate into a
mortality difference after correcting for various confounders.
In general, treatment goals should always be set for critically ill patients. If feasible, the
benefits versus the risks of treatment should be considered for every patient. Ideally, a
decision to withhold invasive intensive care measures should be made prior to ICU
admission, yet in reality these expectations are not always met In addition to the [16].
declared will of an individual (if available and realistic) and the current state of the acute
illness, the patient's past medical history (including previous illnesses, frailty, duration of
inpatient stay, and previous hospitalisations) should also be taken into consideration for any
treatment decision Furthermore, functional and nutritional status are of paramount [16].
importance. Additionally, it is essential to distinguish between withholding or withdrawal of
treatment, as well as to note that limitations of treatment (especially withholding of ICU care)
are not necessarily associated with an increased short-term mortality [17].
The fact that a north-south divide in EoLC practices exists in Europe is a well-known
phenomenon. Sprung et al. showed that the number of patients with treatment limitations
put in place is gradually increasing. This could be for a number of reasons. It is certainly not
only due to the ageing population of critically ill patients, often with significant comorbidities,
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but also due to increased public awareness, improved diagnostics, and the growing body of
guidelines and treatment recommendations, which allow for a more structured approach in
the daily intensive care routine [18,19].
Baseline risk distribution
This study observed discrete variations in patient-specific differences between the three
European regions studied, which may reflect regional variations in ICU admission policies
[2022]. In addition to the known increased number of critical care beds in Central Europe,
a higher proportion of patients over 80 years of age and individuals with previously known
frailty were found in this region [23]. Nevertheless, a significantthough not vast
difference was observed between the three regions with regards to median patient age and
the severity of the acute ilness. This also fits well with one of our preliminary studies, in
which we showed that countries with different health-care systems admitted patients with
different characteristics to their local intensive care units [24]. In addition to the expected
difference in GDP between Northern and Southern Europe, there is also a higher level of
health spending per capita in Central Europe. Whether the rate of admission of older and
more frail patients is a "phenomenon of prosperity" or due to cultural differences can only be
speculated. There may be a higher rate of time-limited ICU trials for older patients in Central
Europe or a more liberal ICU admission policy as a result of the larger number of ICU beds
available per capita; however, that is out of the scope of the present study.
Organ support and management
Furthermore, evidence of variations in intensive care management practices between
different regions was found. For example, the rate of mechanically ventilated patients was
significantly higher in Southern Europe, translating into a higher proportion of tracheostomies
performed. The number of patients who required circulatory support with vasopressors was
also higher in Southern Europe. This is particularly interesting given the relatively similar
median SOFA score, and may reflect different regional treatment strategies. When treating
COVID-19, the early or late use of mechanical ventilationwhich allows the application of
lung-protective ventilationand the optimal timing for tracheostomy are the subject of
ongoing scientific and clinical debate [25,26]. As is well known, Italy, Spain, Portugal and
Israelall countries in Southern Europewere hit hard relatively early in the COVID-19
pandemic, and this may have affected how treatment was delivered. Especially in severely
affected countries, conventional ICU measures such as non-invasive ventilation (NIV) were
often performed on normal wards during times of surge, whereas invasive mechanical
ventilation generally remained within the intensive care domain [27]. This could have
indirectly contributed to the higher numbers of intubated patients on mechanical ventilation in
Southern Europe. Finally, given the similar disease severity described above, a different
approach to time limits of NIV-trials and variations in intubation criteria must be considered.
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Treatment limitation analysis
The reasons for or against treatment limitations may be due to a patient's personal
preference, general ethical/intensive care considerations, and local ICU admission policies.
Inherent factors in the health systemsuch as the training and education of clinicians,
existing guidelines, or the weight given to the wishes of the patient's familymay also
influence the likelihood of a treatment [18]. In the Ethicus-2 study, it was suggested that the
similar observed differences within Europe could also be an expression of changing
practices over time, and that changes are implemented more quickly in Northern Europe
[28]. However, given the current data, this seems unlikely as the observed differences seen
now in 2020 and 2021 show a similar pattern to those seen in the Ethicus-2 study conducted
in 2015 and 2016. We therefore believe that the differences observednow repeatedly and
reproduciblyare more structural in nature, although the exact causes remain unclear.
Outcome analysis
Interestingly, the different rates of treatment limitations did not lead to a major difference in
regional 90-day mortality rates of critically ill older patients with COVID-19. Ultimately, one
can only speculate about the reasons for this finding. A slightly higher proportion of patients
over 80 years of age and patients with cardiovascular comorbidities was observed in Central
Europe compared with Northern and Southern Europe. Individual treatment strategies also
variedwhich may be due to different structures of care, but on the other hand could be due
to the stress put on intensive care units during surge. Another influencing factor could be
the differences in ICU bed numbers, as mentioned previously. Systems with a larger pre-
existing ICU bed base are more likely to be able to compensate quickly in times of crisis
compared with systems in which new capacity must be created. There may also be
differences in terms of staffing-patient ratios. Also, different ICU capacities may imply
different up- and down-stream structures (intermediate care or monitoring areas on normal
wards). Accordingly, patients with non-invasive ventilation may be admitted to an ICU in
some settings and may be cared for on a monitored normal ward in other systems. Thus, in
our collective, more patients in Central and Northern Europe received non-invasive
ventilation, whereas more individuals in Southern Europe required invasive ventilation.
Another interesting finding is that ICU length of stay varied significantly between regions,
with the longest lengths of stay in Southern Europe. This is noteworthy, as the rate of
treatment limitations was lower there, but there was no statistically significant difference in
terms of mortality. It must be taken into account that infrastructure and management vary
regionally, and not all details on local structures of care are available in our study.
Nevertheless, considering these findings, the structured use of treatment limitations may be
usefulespecially in times of overcrowding in ICUs in the context of the pandemicif a
possibly longer invasive treatment does not result in a survival advantage for the affected
patients. Because the secondary endpoints of 30- and 90-day mortality are not available for
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all patients, a selection or reporting bias may also exist. This study regardless shows that
the different systems had comparable results due to the large amount of time and work
invested during the pandemic. In general, a multinational comparison is difficult due to the
significantly different health-care systems and ICU bed capacities (as well as subsequent
care structures) in the various countries of the three European regions. Nevertheless, the
multivariable analysis corrected for all of these factors, which was intended to achieve a
balanced comparison across regions.
Conclusion
We conclude that rates of treatment limitations in older patients during the COVID-19
pandemic period in Europe show a north to south gradient. When performing such
evaluations, different health-care systems, patient characteristics, and local treatment
strategies must be considered. Interestingly, this did not lead to significant differences in
mortality rates in a multivariable analysis.
Limitations
One limitation is that we do not know the exact timing of when a treatment limitation was
pronounced. We also do not have detailed information on how a "treatment withhold" was
defined in an individual case, and it would likely not be possible to evaluate this uniformly for
such a large cohort. Unfortunately, we also do not have information on whether patients
were transferred to intermediate care units or elsewhere after a treatment limitation was
imposed. Further limitations of this studybesides its unblinded designare the lack of
knowledge about the functional outcome, and the problem of a potential self-fulfilling
prophecy as always with treatment limitations. Also, in addition to a comparison group with
younger patients, a long-term outcome would be interesting because of the often-protracted
hospital stay in severe COVID-19 infections. Although it is difficult to generalize qualitative
data such as treatment limitations, we think that our study provides useful data for health-
care providers and decision makers due to the large number of participants and good patient
characterization.
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Acknowledgments:
The map in the Graphical Abstract was generated using https://mapchart.net/ (licensed
under a Creative Commons Attribution-ShareAlike 4.0 International License). No (industry)
sponsorship has been received for this investigator-initiated study.
Data sharing:
All data relevant for this study will be given by the authors upon specific request without
restriction.
Author contributions:
Conceptualization: BW, HF, MJ, DWL, BG, CJ.
Methodology: BW, HF, RRB.
Validation: MB, SS, PVH, RM, AB, BG.
Formal analysis: BW.
Investigation: JF, AA, BBP, JCS, SS, PVH, WS, ME, MJ, SO, GW, BM, FHA, RM, SL, DWL,
BG, CJ.
Resources: HF, MJ, DWL, BG, CJ.
Data Curation: MB, AB.
Writing - Original Draft: BW, RR, SW.
Writing - Review & Editing: BW, RR, SW, SL, CJ.
Visualization: RR.
Supervision: BG, MK, DWL.
Project administration: BW, RR, CJ.
Conflict of Interest:
JCS (full departmental disclosure:) report grants from Orion Pharma, Abbott Nutrition
International, B. Braun Medical AG, CSEM AG, Edwards Lifesciences Services GmbH,
Kenta Biotech Ltd, Maquet Critical Care AB, Omnicare Clinical Research AG, Nestle, Pierre
Fabre Pharma AG, Pfizer, Bard Medica S.A., Abbott AG, Anandic Medical Systems, Pan
Gas AG Healthcare, Bracco, Hamilton Medical AG, Fresenius Kabi, Getinge Group Maquet
AG, Dräger AG, Teleflex Medical GmbH, Glaxo Smith Kline, Merck Sharp and Dohme AG,
Eli Lilly and Company, Baxter, Astellas, Astra Zeneca, CSL Behring, Novartis, Covidien,
This article is protected by copyright. All rights reserved.
Phagenesis, and Nycomed outside the submitted work. The money was paid into
departmental funds. No personal financial gain applied. The other authors declare that they
have no conflict of interests.
References
1. Avidan A, Sprung CL, Schefold JC, et al. Variations in end-of-life practices in intensive
care units worldwide (Ethicus-2): a prospective observational study. Lancet Respir Medicine
2021; 9(10):1101-1110.
2. Guidet B, Flaatten H, Boumendil A, et al. Withholding or withdrawing of life-sustaining
therapy in older adults (≥80 years) admitted to the intensive care unit. Intens Care Med
2018;44(7):102738.
3. Flaatten H, Heerden VV, Jung C, et al. The good, the bad and the ugly: pandemic priority
decisions and triage. J Med Ethics 2020;0:13.
4. Flaatten H, deLange D, Jung C, Beil M, Guidet B. The impact of end-of-life care on ICU
outcome. Intens Care Med 2021;47:624625.
5. Guidet B, Lange DW de, Flaatten H. Should this elderly patient be admitted to the ICU?
Intens Care Med 2018;44(11):19268.
6. Beil M, Sviri S, Flaatten H, et al. On predictions in critical care: The individual
prognostication fallacy in elderly patients. J Crit Care. 2021;61:348.
7. Ferrante LE, Pisani MA, Murphy TE, Gahbauer EA, Leo-Summers LS, Gill TM.
Functional Trajectories Among Older Persons Before and After Critical Illness. Jama Intern
Med 2015;175(4):5239.
8. Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty
in elderly people. Can Med Assoc J 2005;173(5):48995.
9. Guidet B, Lange DW de, Boumendil A, et al. The contribution of frailty, cognition, activity
of daily life and comorbidities on outcome in acutely admitted patients over 80 years in
European ICUs: the VIP2 study. Intens Care Med 2020;46(1):5769.
10. Flaatten H, Lange DWD, Morandi A, et al. The impact of frailty on ICU and 30-day
mortality and the level of care in very elderly patients (≥ 80 years). Intens Care Med
2017;43(12):18208.
This article is protected by copyright. All rights reserved.
11. Wagstaff A. Social Health Insurance Vs. Tax-Financed Health Systems - Evidence
From The OECD. Policy Res Work Pap 2009.
12. Katz S. Assessing Selfmaintenance: Activities of Daily Living, Mobility, and Instrumental
Activities of Daily Living. J Am Geriatr Soc 1983;31(12):7217.
13. International Monetary Fund. World Economic Outlook Reports 2020.
14. United Nations Development Program. Human Development Report 2020 [Internet].
2020 [cited 2021 May 13]. Available from: http://hdr.undp.org/sites/default/files/hdr2020.pdf
15. OECD. Health at a Glance [Internet]. 2020 [cited 2021 May 13]. Available from:
https://data.oecd.org/healthres/health-spending.htm
16. Friesenecker B, Fruhwald S, Hasibeder W, et al. Therapiezieländerungen auf der
Intensivstation: Definitionen, Entscheidungsfindung und Dokumentation. Anästhesiol
Intensivmed Notfallmed Schmerzther 2013;48(4):21623.
17. Rubio O, Arnau A, Cano S, et al. Limitation of life support techniques at admission to the
intensive care unit: a multicenter prospective cohort study. J Intensive Care 2018;6(1):24.
18. Sprung CL, Ricou B, Hartog CS, et al. Changes in End-of-Life Practices in European
Intensive Care Units From 1999 to 2016. Jama 2019;322(17):1692704.
19. Ehni H, Wiesing U, Ranisch R. Saving the most livesA comparison of European triage
guidelines in the context of the COVID19 pandemic. Bioethics 2021;35(2):12534.
20. Wilkinson D, Zohny H, Kappes A, Sinnott-Armstrong W, Savulescu J. Which factors
should be included in triage? An online survey of the attitudes of the UK general public to
pandemic triage dilemmas. Bmj Open 2020;10(12):e045593.
21. Netters S, Dekker N, Wetering K van de, et al. Pandemic ICU triage challenge and
medical ethics. Bmj Supportive Palliat Care 2021;11(2):1337.
22. Darvall JN, Bellomo R, Bailey M, Anstey J, Pilcher D. Long-term survival of critically ill
patients stratified by pandemic triage categories: a retrospective cohort study. Chest 2021;
160(2):538548.
23. Rhodes A, Ferdinande P, Flaatten H, Guidet B, Metnitz PG, Moreno RP. The variability
of critical care bed numbers in Europe. Intens Care Med 2012;38(10):164753.
24. Wernly B, Beil M, Bruno RR, et al. Provision of critical care for the elderly in Europe: a
retrospective comparison of national healthcare frameworks in intensive care units. Bmj
Open 2021;11(6):e046909.
This article is protected by copyright. All rights reserved.
25. Bavishi AA, Mylvaganam RJ, Agarwal R, et al. Timing of Intubation in Coronavirus
Disease 2019: A Study of Ventilator Mechanics, Imaging, Findings, and Outcomes. Critical
Care Explor 2021;3(5):e0415.
26. Schultz MJ, Teng MS, Brenner MJ. Timing of Tracheostomy for Patients With COVID-19
in the ICUSetting Precedent in Unprecedented Times. Jama Otolaryngology Head Neck
Surg 2020;146(10):8878.
27. Bellani G, Grasselli G, Cecconi M, et al. Noninvasive Ventilatory Support of Patients
with COVID-19 outside the Intensive Care Units (WARd-COVID). Ann Am Thorac Soc
2021;18(6):10206.
28. Avidan A, Sprung CL, Schefold JC, et al. Variations in end-of-life practices in intensive
care units worldwide (Ethicus-2): a prospective observational study. Lancet Respir Medicine
2021;9(10):110110.
Correspondence:
Professor Christian Jung, M.D., PhD
Division of Cardiology, Pulmonology, and Vascular Medicine
University Duesseldorf
Moorenstraße 5, 40225 Duesseldorf, Germany.
Email: christian.jung@med.uni-duesseldorf.de
Figure legends
Figure 1: Kaplan-Meier curve depicting survival (with 95% CI) of patients in Northern
Europe (red) and Central Europe (blue) and Southern Europe (green).
Abbreviations: CI: confidence interval;
Sensitivity analyses stratifying any treatment limitations in subgroups for Figure 2:
patient-specific characteristics using generalised estimation equations (GEE)
producing population average odds ratios. The depicted aORs from model-1 include
only the ICU as panel. Abbreviations: aOR: adjusted odds ratio; ICU: intensive care
unit;
This article is protected by copyright. All rights reserved.
Sensitivity analyses stratifying any treatment limitations in subgroups for Figure 3:
patient-specific characteristics using generalised estimation equations (GEE)
producing population average odds ratios. The depicted aORs from model-1 include
only the ICU as panel. Abbreviations: aOR: adjusted odds ratio; ICU: intensive care
unit;
Median length of stay in patients with no treatment limitation Figure Supplement 3:
versus individuals with any treatment limitation. Abbreviations: ICU: intensive care
unit; LOS: length of stay;
Central Europe
(n=1,573)
Northern Europe
(n=821)
Southern Europe
(n=711)
p-value
Patient-specific variables
Sex
0.43
Male - n (%)
1,117 (71)
594 (72)
493 (69)
Female - n (%)
456 (29)
227 (28)
218 (31)
Age at admission yrs.
76 (5)
75 (4)
75 (5)
<0.001
Age categories
<0.001
Age <80 years n (%)
1,211 (77)
703 (86)
595 (84)
Age >79 years n (%)
362 (23)
118 (14)
115 (16)
BMI kg/m2
28 (5)
28 (5)
29 (5)
0.007
Comorbidities
Arterial hypertension n (%)
1,084 (69)
467 (57)
509 (72)
<0.001
Diabetes (any type) n (%)
558 (36)
240 (29)
244 (34)
0.007
Ischemic heart disease n (%)
379 (24)
194 (24)
121 (17)
<0.001
Chronic heart failure n (%)
237 (15)
122 (15)
87 (12)
0.19
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Pulmonary comorbidity n (%)
359 (23)
206 (25)
134 (19)
0.011
Renal insufficiency n (%)
285 (18)
118 (14)
99 (14)
0.009
Frailty
Clinical Frailty Scale Score pts.
3 (2)
3 (1)
3 (1)
0.002
SOFA score on admission pts.
6 (3)
5 (3)
6 (3)
<0.001
Frailty categories
<0.001
Fit n (%)
941 (60)
507 (62)
496 (70)
Vulnerable n (%)
253 (16)
114 (14)
69 (9)
Frail n (%)
288 (18)
125 (15)
84 (12)
Frailty category unavailable n (%)
91 (6)
75 (9)
62 (9)
Region specific variables
ICU beds per 100,000 population no
12 (12-16)
7 (6-7)
10 (6-10)
<0.001
ICU beds categories
<0.001
<10 ICU beds per 100K n (%)
134 (9)
821 (100)
695 (98)
≥10 ICU beds per 100K – n (%)
1,439 (91)
0 (0)
16 (2)
HDI
0.92 (0.03)
0.94 (0.01)
0.89 (0.02)
<0.001
GDP per capita in US - $
41,897 (41,897-46,473)
52,646 (42,379-59,770)
29,993 (23,132-29,993)
<0.001
Health spending per capita in US - $
5,376 (5,376-6,646)
5,568 (4,653-5,765)
3,616 (3,379-3,616)
<0.001
Table 1: Baseline characteristics of the cohort. Abbreviations: BMI: body mass index; GDP:
gross domestic product; HDI: human development index; ICU: intensive care unit; SOFA:
sequential organ failure assessment;
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Northern Europe
(n=821)
Southern Europe
(n=711)
p-value
Management strategies
Non-invasive ventilation n (%)
220 (27)
160 (23)
0.077
Intubation and mechanical ventilation n (%)
550 (67)
602 (85)
<0.001
Tracheostomy n (%)
163 (20)
222 (31)
<0.001
Vasoactive drugs used n (%)
555 (68)
541 (77)
<0.001
Renal Replacement Therapy used n (%)
96 (12)
108 (15)
<0.001
ICU length of stay - d
15 (14)
21 (17)
<0.001
Primary endpoint
Any treatment limitation n (%)
396 (48)
172 (24)
<0.001
Secondary endpoints
Life sustaining care withheld n (%)
315 (38)
139 (20)
<0.001
Life sustaining care withdrawn n (%)
249 (30)
93 (13)
<0.001
ICU mortality n (%)
349 (43)
354 (51)
0.003
30-day-mortality n (%)
391 of 798 (49)
341 of 696 (49)
0.93
3-month-mortality n (%)
421 of 752 (56)
399 of 644 (62)
0.019
Table 2: Management strategies and endpoints. Abbreviations: ICU: intensive care unit.
Note: Due to missing values, 30-day- and 3-month outcomes are calculated only for the set
with available outcome value.
Primary endpoint
(Any treatment limitation)
Secondary endpoint
(90-day-mortality)
OR
95%CI
p-value
OR
95%CI
p-value
Model-1: ICU as panel variable
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Central Europe
-
-
-
-
-
-
Northern Europe
1.53
1.10-2.14
0.01
0.96
0.71-1.30
0.81
Southern Europe
0.56
0.38-0.83
0.004
1.31
0.92-1.87
0.14
Model-2: model-1 + age, sex, SOFA and CFS
Central Europe
-
-
-
-
-
-
Northern Europe
1.63
1.06-2.52
0.03
1.24
0.85-1.82
0.27
Southern Europe
0.48
0.26-0.88
0.02
1.43
0.97-2.11
0.07
Model-3: model-2 + ICU beds per 100,000 population
Central Europe
-
-
-
-
-
-
Northern Europe
0.87
0.52-1.46
0.60
0.84
0.53-1.35
0.48
Southern Europe
0.30
0.15-0.58
<0.001
1.11
0.71-1.73
0.66
Model-4: model-3 + HDI
Central Europe
-
-
-
-
-
-
Northern Europe
0.57
0.27-1.22
0.15
1.29
0.80-2.09
0.30
Southern Europe
0.39
0.21-0.73
0.004
1.07
0.66-1.73
0.78
Model-5: model-4 + any treatment limitation
Central Europe
N/A
N/A
N/A
-
-
-
Northern Europe
N/A
N/A
N/A
1.99
0.98-4.03
0.06
Southern Europe
N/A
N/A
N/A
1.40
0.75-2.61
0.29
Table 3: Generalized estimation equations (GEE) based analysis producing population
average odds ratios. Model-1 includes only the ICU as panel. Model-2 includes patient
specific factors (sex, age per year, SOFA score per point and frailty score per CFS point).
Model-3 adds the amount of ICU beds per 100,000 population. Model-4 includes the HDI.
Model-5 adds the rates of any treatment limitations and hence calculated only 90-day-
mortality. Adjusted odds ratios (aOR) and respective 95% confidence intervals (95%CI) were
obtained. Abbreviations: CFS: clinical frailty score; CI: confidence interval; HDI: human
development index; ICU: intensive care unit; OR: odds ratio; SOFA: sequential organ failure
assessment;
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Figure 1: Kaplan-Meier curve depicting survival (with 95% CI) of patients in Northern
Abbreviations: CI: confidence (red) and Central (blue) and Southern Europe (green).
interval;
This article is protected by copyright. All rights reserved.
Sensitivity analyses stratifying any treatment limitations in subgroups for Figure 2:
patient-specific characteristics using generalised estimation equations (GEE)
producing population average odds ratios. The depicted aORs from model-1 include
only the ICU as panel. Abbreviations: aOR: adjusted odds ratio; ICU: intensive care
unit;
This article is protected by copyright. All rights reserved.
Sensitivity analyses stratifying any treatment limitations in subgroups for Figure 3:
patient-specific characteristics using generalised estimation equations (GEE)
producing population average odds ratios. The depicted aORs from model-1 include
only the ICU as panel. Abbreviations: aOR: adjusted odds ratio; ICU: intensive care
unit;
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