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Health-related quality of life in older patients surviving ICU treatment for COVID-19: results from an international observational study of patients older than 70 years

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  • Viborg Regional Hospital

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Background: health-related quality of life (HRQoL) is an important patient-centred outcome in patients surviving ICU admission for COVID-19. It is currently not clear which domains of the HRQoL are most affected. Objective: to quantify HRQoL in order to identify areas of interventions. Design: prospective observation study. Setting: admissions to European ICUs between March 2020 and February 2021. Subjects: patients aged 70 years or older admitted with COVID-19 disease. Methods: collected determinants include SOFA-score, Clinical Frailty Scale (CFS), number and timing of ICU procedures and limitation of care, Katz Activities of Daily Living (ADL) dependence score. HRQoL was assessed at 3 months after ICU admission with the Euro-QoL-5D-5L questionnaire. An outcome of ≥4 on any of Euro-QoL-5D-5L domains was considered unfavourable. Results: in total 3,140 patients from 14 European countries were included in this study. Three months after inclusion, 1,224 patients (39.0%) were alive and the EQ-5D-5L from was obtained. The CFS was associated with an increased odds ratio for an unfavourable HRQoL outcome after 3 months; OR 1.15 (95% confidence interval (CI): 0.71-1.87) for CFS 2 to OR 4.33 (95% CI: 1.57-11.9) for CFS 7. The Katz ADL was not statistically significantly associated with HRQoL after 3 months.
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Age and Ageing 2022; 51: 110
https://doi.org/10.1093/ageing/afab278 © The Author(s) 2022. Published by Oxford University Press on behalf of the British Geriatrics
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RESEARCH PAPER
Health-related quality of life in older patients
surviving ICU treatment for COVID-19: results
from an international observational study of
patients older than 70 years
Ivo W. Soliman1,Susannah Leaver2,Hans Flaatten3,Jesper Fjølner4,Bernhard Wernly5,
Raphael R. Bruno6,Antonio Artigas7,Bernardo Bollen Pinto8,Joerg C. Schefold9,
Michael Beil10,Sigal Sviri10,Peter Vernon van Heerden11,Wojciech Szczeklik12,
Muhammed Elhadi13,Michael Joannidis14,Sandra Oeyen15,Tilemachos Zafeiridis16,,
Jakob Wollborn17,Maria Jose Arche Banzo18,Kristina Fuest19,Brian Marsh20,
Finn H. Andersen21,22,Rui Moreno23,Ariane Boumendil24,25,Bertrand Guidet24,25,
Christian Jung26,Dylan W. De Lange1, The COVIP-study group*
1Department of Intensive Care Medicine, University Medical Center, University of Utrecht, Utrecht, the Netherlands
2General Intensive Care, St George’s University Hospitals NHS Foundation Trust, London, UK
3Department of Clinical Medicine, University of Bergen, Department of Anaestesia and Intensive Care, Haukeland University
Hospital, Bergen, Norway
4Department of Intensive Care, Aarhus University Hospital, Aarhus, Denmark
5Department of Anaesthesiology, Perioperative Medicine and Intensive Care Medicine Paracelsus Medical University of Salzburg,
Salzburg, Austria
6Division of Cardiology, Pulmonology, and Vascular Medicine, University Duesseldorf, Duesseldorf, Germany
7Department of Intensive Care Medicine, CIBER Enfermedades Respiratorias, Corporacion Sanitaria Universitaria Parc Tauli,
Autonomous University of Barcelona, Sabadell, Spain
8Department of Acute Medicine, Geneva University Hospitals, Geneva, Switzerland
9Department of Intensive Care Medicine, Inselspital, Universitätsspital, University of Bern, Bern, Switzerland
10Department of Medical Intensive Care, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem,
Jerusalem, Israel
11General Intensive Care Unit, Department of Anesthesiology, Critical Care and Pain Medicine, Hadassah Medical Center and
Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
12Center for Intensive Care and Perioperative Medicine, Jagiellonian University Medical College, Krakow, Poland
13Faculty of Medicine, University of Tripoli, Tripoli, Libya
14Division of Intensive Care and Emergency Medicine, Department of Internal Medicine, Medical University Innsbruck, Innsbruck,
Austria
15Depar tment of Intensive Care 1K12IC, Ghent University Hospital, Ghent, Belgium
16Intensive Care Unit, General Hospital of Larissa, Larissa, Greece
17Depar tment of Anesthesiolgy, Perioperative and Pain Medicine , Brigham and Women’s Hospital, Havard Medical School, Boston,
MA, USA
18Servicio de Medicina Intensiva, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain
19Depar tment of Anesthesiology and Intensive Care, Technical University of Munich, Klinikum rechts der Isar, Munich, Germany
20Depar tment of Anesthesia and Intensive Care Medicine, Mater Misericordiae University Hospital, Dublin, Ireland
21Depar tment of Anaesthesia and Intensive Care, Ålesund Hospital, Ålesund, Norway
22Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
23Unidade de Cuidados Intensivos Neurocríticos e Trauma. Hospital de São José, Centro Hospitalar Universitário de Lisboa
Central, Faculdade de Ciências Médicas de Lisboa, Nova Médical School, Lisbon, Portugal
Downloaded from https://academic.oup.com/ageing/article/51/2/afab278/6523677 by guest on 20 February 2022
I. W. Soliman et al.
24Sorbonne Universités, UPMC Univ Paris 06, INSERM, UMR_S 1136, Institut Pierre Louis d’Epidémiologie et de Santé Publique,
Equipe: épidémiologie hospitalière qualité et organisation des soins, F-75012 Paris, France
25Assistance Publique - Hôpitaux de Paris, Hôpital Saint-Antoine, service de réanimation médicale, F-75012 Paris, France
26Division of Cardiology, Pulmonology, and Vascular Medicine, University Duesseldorf, Duesseldorf, Germany
Address correspondence to: D. W. de Lange, Department of Intensive Care Medicine, University Medical Center Utrecht,
Heidelberglaan 100, 3584 CX Utrecht,The Netherlands. Tel: +31 88 75 585 61; Fax: +31 88 75 556 77.
Email: d.w.delange@umcutrecht.nl
In memory of our colleague and friend Tilemachos Zafeiridis
Acknowledgement of the COVIP-study group collaborators: Philipp Eller, Michael Joannidis, Dieter Mesotten, Pascal
Reper, Sandra Oeyen, Walter Swinnen, Helene Brix, Jens Brushoej, Maja Villefrance, Helene Kor venius Nedergaard, Anders Thais
Bjerregaard, Ida Riise Balleby, Kasper Andersen, Maria Aagaard Hansen, Stine Uhrenholt, Helle Bundgaard, Jesper Fjølner, Aliae Ar
Hussein Mohamed, Rehab Salah, Yasmin Khairy NasrEldin Mohamed Ali, Kyrillos Wassim, Yumna A Elgazzar, Samar Tharwat,
Ahmed Y Azzam, Ayman Abdelmawgoad Habib, Hazem Maarouf Abosheaishaa, Mohammed A Azab, Susannah Leaver, Arnaud
Galbois, Bertrand Guidet, Cyril Charron, Emmanuel Guerot, Guillaume Besch, Jean-Philippe Rigaud, Julien Maizel, Michel Djibré,
Philippe Burtin, Pierre Garcon, Saad Nseir, Xavier Valette, Nica Alexandru, Nathalie Marin, Marie Vaissiere, Gaëtan Plantefeve,
Thierry Vanderlinden, Igor Jurcisin, Bruno Megarbane, Anais Caillard, Arnaud Valent, Marc Garnier, Sebastien Besset, Johanna
Oziel, Jean-Herlé Raphaelen, Stéphane Dauger, Guillaume Dumas, Bruno Goncalves, Gaël Piton, Christian Jung, Raphael Romano
Bruno, Malte Kelm, Georg Wolff, Eberhard Barth, Ulrich Goebel, Eberhard Barth, Anselm Kunstein, Michael Schuster, Martin
Welte, Matthias Lutz, Patrick Meybohm, Stephan Steiner, Tudor Poerner, Hendrik Haake, Stefan Schaller, Detlef Kindgen-Milles,
Christian Meyer, Muhammed Kur t, Karl Friedrich Kuhn, Winfried Randerath, Jakob Wollborn, Zouhir Dindane, Hans-Joachim
Kabitz, Ingo Voigt, Gonxhe Shala, Andreas Faltlhauser, Nikoletta Rovina, Zoi Aidoni, Evangelia Chrisanthopoulou, Antonios
Papadogoulas, Mohan Gurjar, Ata Mahmoodpoor, Abdullah Khudhur Ahmed, Brian Marsh, Ahmed Elsaka, Sigal Sviri, Vittoria
Comellini, Ahmed Rabha, Hazem Ahmed, Silvio A Namendys-Silva, Abdelilah Ghannam, Martijn Groenendijk, Marieke Zegers,
Dylan de Lange, Alex Cornet, Mirjam Evers, Lenneke Haas, Tom Dormans, Willem Dieperink, Luis Romundstad, Britt Sjøbø, Finn
H Andersen, Hans Frank Strietzel, Theresa Olasveengen, Michael Hahn, Miroslaw Czuczwar, Ryszard Gawda, Jakub Klimkiewicz,
Maria Campos de LurdesSantos, André Gordinho, Henrique Santos, Rui Assis, Ana Isabel Pinho Oliveira, Mohamed Raafat
Badawy, David Perez-Torres, Gemma Gomà, Mercedes Ibarz Villamayor, Angela Prado Mira, Patricia Jimeno Cubero, Susana Arias
Rivera, Teresa Tomasa, David Iglesias, Eric Mayor Vázquez, Cesar Aldecoa, Aida Fernández Ferreira, Begoña Zalba-Etayo, Isabel
Canas-Perez, Luis Tamayo-Lomas, Cristina Diaz-Rodriguez, Susana Sancho, Jesús Priego, Enas M Y Abualqumboz, Momin Majed
Yousuf Hilles, Mahmoud Saleh, Nawfel Ben-HAmouda, Andrea Rober ti, Alexander Dullenkopf, Yvan Fleury, Bernardo Bollen
Pinto, Joerg C Schefold, Mohammed Al-Sadawi, Nicolas Serck, Elisabeth Dewaele, Pritpal Kumar, Camilla Bundesen, Richard Innes,
James Gooch, Lenka Cagova, Elizabeth Potter, Michael Reay, Miriam Davey, Sally Humphreys, Caroline Hauw Berlemont, Benjamin
Glenn Chousterman, François Dépret, Alexis Ferre, Lucie Vettoretti, Didier Thevenin, Andreas Faltlhauser, Milena Milovanovic,
Philipp Simon, Marco Lorenz, Sandra Emily Stoll, Simon Dubler, Kristina Fuest, Francesk Mulita, Eumorifa Kondili, Ioannis
Andrianopoulos, Iwan Meynaar, Alexander Daniel Cornet, Britt Sjøbøe, Anna Kluzik, Paweł Zatorski, Tomasz Drygalski, Wojciech
Szczeklik, Joanna Solek-Pastuszka, Dariusz Onichimowski, Jan Stefaniak, Karina Stefanska-Wronka, Ewa Zabul, Filipe Sousa
Cardoso, Maria José Arche Banzo, Teresa Maria Tomasa-Irriguible, Ángela Prado Mira, Susana Arias-Rivera, Fernando Frutos-Vivar,
Sonia Lopez-Cuenca, Pablo Ruiz de Gopegui, Nour Abidi, Ivan Chau, Richard Pugh,Sara Smuts.
Abstract
Background: health-related quality of life (HRQoL) is an important patient-centred outcome in patients surviving ICU
admission for COVID-19. It is currently not clear which domains of the HRQoL are most affected.
Objective: to quantify HRQoL in order to identify areas of interventions.
Design: prospective observation study.
Setting: admissions to European ICUs between March 2020 and February 2021.
Subjects: patients aged 70 years or older admitted with COVID-19 disease.
Methods: collected determinants include SOFA-score, Clinical Frailty Scale (CFS), number and timing of ICU procedures
and limitation of care, Katz Activities of Daily Living (ADL) dependence score. HRQoL was assessed at 3 months after ICU
admission with the Euro-QoL-5D-5L questionnaire. An outcome of 4 on any of Euro-QoL-5D-5L domains was considered
unfavourable.
Results: in total 3,140 patients from 14 European countries were included in this study. ree months after inclusion, 1,224
patients (39.0%) were alive and the EQ-5D-5L from was obtained. e CFS was associated with an increased odds ratio for
an unfavourable HRQoL outcome after 3 months; OR 1.15 (95% confidence interval (CI): 0.71–1.87) for CFS 2 to OR 4.33
(95% CI: 1.57–11.9) for CFS 7. e Katz ADL was not statistically significantly associated with HRQoL after 3 months.
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Health-related quality of life in older patients surviving ICU treatment for COVID-19
Conclusions: in critically ill old intensive care patients suffering from COVID-19, the CFS is associated with the subjectively
perceived quality of life. e CFS on admission can be used to inform patients and relatives on the risk of an unfavourable
qualitative outcome if such patients survive.
Keywords: Intensive Care Unit (ICU), frailty, Quality of Life, Sur vival, Older people, COVID-19
Key Points
ree-month survival of patients 70 years admitted to the Intensive Care Unit for COVID-19 is limited (39%).
Half of the surviving patients have severe to extreme problems on at least one of the Health-related quality of life (HRQoL)
domains.
Frailty is associated with severe to extreme problems on the HRQoL outcome.
Introduction
In 2019, the novel corona virus (SARS-CoV-2) caused a
worldwide pandemic. Particularly older patients (>70 years)
became severely ill and a disproportionate number needed
admission to an intensive care unit (ICU) [1]. Despite
improvements in treatments, a large proportion of these
older patients still succumb to the disease and the 30-day
survival is 59% [2].
Patients who survive the ICU admission often experience
serious sequelae: post-traumatic stress disorder, anxiety,
depression, muscle weakness, cognitive impairment and
many others [3]. Additionally, a significant number of
patients developed what is now named ‘long-covid’, which
has a strong influence on the physical and mental aspects
of life and well-being. Such unwanted side effects of
ICU-treatment may profoundly impact the quality of
life of surviving patients [3,4]. However, which adverse
outcomes have the largest impact on self-perceived, health-
related quality of life is currently unclear. Without such
information, we cannot properly intervene to prevent
poor quality of life. Moreover, many older patients (and
their relatives) prioritise a good quality of life over long-
term survival [5]. Vice versa, there is a strong relationship
between quality of life after ICU survival and the self-
reported unacceptable outcome one year after intensive
care treatment [6]. We might even consider intensive care
treatment disproportionate if it only causes suffering and
anguish while not reaching the personal goal of a good
quality of life [7]. For all these reasons, an assessment of
the patient’s pre-ICU functional abilities at ICU admission
and assessments for post-intensive care syndrome-related
problems has been advocated [8].
is prospective multinational, observational study aims
to report on outcomes of the various domains of the self-
reported health-related quality of life assessment of former
critically ill COVID-19 patients. A second aim is to study
whether adverse outcomes are associated with frailty.
Methods
Design and settings
is multicentre, prospective study is part of the Very old
Intensive care Patients (VIP) project (www.vipstudy.org)and
was endorsed by the European Society of Intensive Care
Medicine. In short, the ‘COVID-19 in very old intensive
care patients’ (COVIP-study) was conceived in the first
months of 2021 when the first wave hit Europe. Basically, it
was an observational study looking at patients’ demograph-
ics, treatment modalities while admitted to the ICU and
outcomes of these patients up to 3 months after admission
to the ICU [2]. Such research was already performed in a
general but older ICU population (>80 years old) [2,9,
10]. As a consequence, research protocols could swiftly be
adapted to the COVID-19 population and ethical clearance
was swift because it had already been evaluated, albeit in
another domain of patients. e study was registered at
ClinicalTrials.gov (ID: NCT04321265) and adhered to the
European Union General Data Privacy Regulation (GDPR).
is investigation aimed to understand which factors are
associated with survival and quality of life in older COVID-
19 patients (the so-called COVIP-study, COVID-19 in VIP)
[2]. As in the previous VIP studies [2,9,10], national coor-
dinators recruited the ICUs, coordinated national and local
ethical permissions, and supervised patient recruitment at
the national level. Ethical approval was mandatory for study
participation and was obtained for each country separately.
Usually, informed consent was mandatory for inclusion, but
in some countries, this was not required with the reference to
the critical condition and difficulties with patients consent.
Study population
e COVIP-study recruited consecutive patients with a
positive COVID-19 test and aged 70 years or older who
were admitted to an ICU. e dataset was extracted from
the COVIP study database on 14th of May 2021 and
contained patients from 19th March to 4th February 2021.
Prospective data collection commenced at ICU admission.
e admission day was defined as day 1, and all consecutive
days were numbered sequentially from that date.
Data collection
e methods have been described in previous publications
[2]. In short, all centres used a uniform online electronic case
report form (eCRF). Of each patient, we recorded (i) infor-
mation present on admission (i.e. gender, weight, length,
days in hospital prior to ICU admission, days with symptoms
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I. W. Soliman et al.
prior to hospital admission, comorbidities, medication use,
habitat before admission, arterial blood gasses on admis-
sion, sequential organ failure assessment (SOFA), the clinical
frailty scale (CFS) and the Katz activities of daily living),
(ii) we collected data on treatments provided to (or withheld
from) the patient while being admitted to ICU (i.e. mechan-
ical ventilation, prone positioning, tracheostomy performed,
vasoactive drugs used, use of renal replacement treatment
used, use of non-invasive ventilation, use of extra-corporeal
membrane oxygenation, use of antimicrobials, and life sus-
taining treatments withheld or withdrawn while being in the
ICU) and (iii) outcomes of the patients (i.e. survival to ICU
discharge, ICU length of stay, vital status at 30 days, vital
status at 3 months and EuroQoL quality of life questionnaire
at 3 months).
Records of patients for whom data were not complete
were completed using multiple imputations (see Statistical
section). For the SOFA score on admission, each element
was entered and the eCRF calculated the total score. Fur-
thermore, we assessed the need for non-invasive or invasive
ventilation, prone positioning, tracheostomy, vasopressor use
and renal replacement therapy. e CRF also documented
any limitation of life-sustaining therapy during the ICU-stay.
Frailty and comorbidities
e frailty level prior to the acute illness and hospital admis-
sion was assessed using the CFS [2,9,10].
Data storage
e eCRF and database were hosted on a secure server in
Aarhus University, Denmark.
Health-related quality of life
e quality of life was assessed by the EurQol-5D-5L ques-
tionnaire at 3 months post-discharge. is is a self-reported
assessment of generic health that consists of five dimen-
sions (mobility, self-care, usual activities, pain/discomfort
and anxiety/depression), each with five levels of functioning
(e.g. n ‘no problems’, ‘slight problems’, ‘moderate problems’,
‘severe problems’ and ‘unable to’/‘extreme problems’ for all
of these dimensions) (see Supplementary Table 1, Supple-
mentary data are available in Age and Ageing online). is
health state classifier can describe 3,125 unique health states
that are often reported as vectors ranging from 11,111
(full health) to 55,555 (worst health). Numerous societal
value sets have been derived from population-based valu-
ation studies around the world that, when applied to the
health state vector, resulting in a preference-based score that
typically ranges from states worse than dead (<0) to 1
(full health), anchoring dead at 0. In addition, the measure
includes a visual analogue scale where health is rated by the
patient on a scale from 0 (worse imaginable health) to 100
(best imaginable health) [11].
On each of the 5 domains of the Euro-QoL-5D-5L
patients can answer on 5 different levels (from level 1: no
problem to level 5: extreme problems) (see Supplemental
Table 1, Supplementary data are available in Age and Ageing
online). e self-perceived quality of life is by definition
subjective, and therefore, there is no normative value at
which one can dichotomise quality of life into ‘good’ or
‘poor’ [12]. For study purpose, we defined a level 4 answer
or worse (‘I have severe/extreme problems’) as an outcome
considered ‘unfavourable’.
Statistical analysis
Descriptive data were computed based on all case records,
showing the amount of missing data per variable. Contin-
uous data points were expressed as median ±interquartile
range. Differences between independent groups were calcu-
lated using the Mann–Whitney U-test. Categorical data are
expressed as numbers (percentage). e Chi-square test was
applied to calculate differences between groups.
Multiple variables showed missing data. For the multivari-
able analysis of associating CFS to HRQoL (see below), the
complete cases considering all required variables numbered
585 of the total 1,224 patients (47.8%). We used multiple
imputation techniques to avoid the bias due to loss to follow-
up when a complete case analysis would have been per-
formed. A total of 100 imputed datasets were generated by
multiple imputations through chained equations using the
‘mice’ package in R v4.1.0. All imputation models allowed
for non-linear associations of any included continuous
variables.
Univariate and multivariable logistic regression analyses
were performed to assess associations with baseline vari-
ables and the studied HRQoL outcomes. We chose the co-
variables for the multivariable model (age, gender, Katz,
SOFA score and CFS) based on clinical experience and
previous literature [2,9,10]. In analyses where CFS was
shown per level, CFS 7, 8 and 9 were combined because
thereweretoofewpatientsinCFS8and9toallowa
proper analysis of these levels separately. Rubin’s rules were
used when pooling the results from the imputed datasets. All
tests were two-sided, and a P-value of <0.05 was considered
statistically significant. R version 4.1.0 was used for all
statistical analyses.
Results
Study population
In total, 3,140 patients 70 years old were included in the
COVIP-study. Ninety days after inclusion (ICU admission),
1,224 were still alive of which 707 (57.8% of the survivors)
answered the quality of life questionnaire. Figure 1 depicts
the flow of patients and Table 1 summarises the demograph-
ics of all the patients that were included in the study versus
those who survived up to 3 months (but did not answer
versus those who answered the questionnaire).
e patients who answered the questionnaire were statis-
tically similar to those who did not answer this questionnaire
except for age. ose who did not answer the questionnaire
were younger (non-responders were 73 versus 74 years of
the responders, P= 0.003). All other variables were equally
distributed among responders and non-responders.
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Health-related quality of life in older patients surviving ICU treatment for COVID-19
Ta b l e 1 . Patient demographics
Patient characteristic Total population missing data from
total population
Respon-
ders
Non-
responders
Pvalue for difference
between responders
and non-responders
...................................................................................
N 1,224 707 517
Age (years) 74 [72–77] 4 74
[72–77]
73 [71–77] 0.003
Sex (male) 856 (70%) 3 487
(69%)
369 (71%) 0.444
BMI 28 [25–31] 76 28
[25–31]
28 [25–31] 0.599
Proven COVID-19 1,198 (98%) 6 693
(98%)
505 (98%) 0.978
Hospital stay prior to ICU
admission (days)
2 [1–5] 9 2 [1–5] 2 [1–5] 0.13
Duration of symptoms prior to
hospital admission
7 [4–10] 97 7 [4–10] 7 [4–10] 0.5
Diabetes 360 (29%) 6 212
(30%)
148 (29%) 0.664
Heart failure 135 (11%) 11 87
(12%)
48 (9%) 0.122
Hypertension 802 (66%) 9 456
(64%)
346 (67%) 0.355
Ischemic heart disease 239 (20%) 14 137
(19%)
102 (20%) 1
Pulmonary failure 250 (20%) 5 156
(22%)
94 (18%) 0.11
Clinical Frailty Scale 1 119 (10%) 68 78
(11%)
41 (8%) 0.366
CFS 2 354 (29%) 222
(31%)
132 (26%)
CFS 3 427 (35%) 239
(34%)
188 (36%)
CFS 4 151 (12%) 93
(13%)
58 (11%)
CFS 5 43 (4%) 27 (4%) 16 (3%)
CFS 6 40 (3%) 27 (4%) 13 (3%)
CFS 7 21 (2%) 14 (2%) 7 (1%)
CFS 8 1 (0%) 1 (0%) 0 (0%)
CFS 9 0 (0%) 0 (0%) 0 (0%)
SOFA-score 4 [3–7] 20 4 [3–7] 4 [2–7] 0.129
Intubated 775 (63%) 5 448
(63%)
327 (63%) 0.947
Duration of invasive mechanical
ventilation (days)
14 [8–27] 26 14
[7–26]
14 [8–27] 0.478
Prone positioning 394 (32%) 454 227
(32%)
167 (32%) 0.858
Initiation of prone positioning after
admission to the ICU (days)
0 [0–0] 4 0 [0–0] 0 [0–0] 0.001
Vasoactive medication 735 (60%) 9 430
(61%)
305 (59%) 0.566
Duration of vasoactive medication
(days)
4 [2–9] 39 5 [2–10] 4 [2–8] 0.015
RRT 109 (9%) 3 68
(10%)
41 (8%) 0.345
Duration of RRT (days) 6 [2–15] 6 11
[3–18]
4 [2–8] 0.006
Non-invasive ventilation 303 (25%) 7 192
(27%)
111 (21%) 0.025
Duration of non-invasive
ventilation (days)
1 [1–3] 19 1 [1–3] 1 [1–3] 0.140
ECMO 2 (0%) 0 2 (0%) 0 (0%) 0.548
Length of stay on the ICU (days) 12 [6–27] 22 13
[6–27]
12 [6–27] 0.717
BMI means Body Mass Index, SOFA means Sequential Organ Failure Score, RRT means renal replacement therapy, ECMO means Extracorporeal Membrane
Oxygenation. All continuous variables are represented as median with [interquartile range].
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I. W. Soliman et al.
Figure 1. Flow of patients.
Health-related quality of life
e EQ-5D-5L questionnaire was completed by or com-
pleted for n= 707 patients. In n= 441 (62.4%), the ques-
tionnaire was completed by the patients, in n= 146 (20.7%)
by family or care-givers, in n= 114 (16.1%) extracted from
hospital records or by n= 6 (0.8%) through other means.
e patients that did answer the questionnaire (n= 707)
showed that n= 182 patients (25.7%) complained of having
animpairmentatalevel4or5answer(Ihavesevere/extreme
problems’) in at least one of the five domains (see Figure 2).
After correction for various variables in the multivariable
analysis, the odds of having a complaint of level 4 or worse
were correlated with increasing frailty scales: the odds for
CFS 2 were 1.17 (95% confidence interval (CI): 0.72–
1.91) and increased to an odds of 4.22 (95%CI: 1.06–16.8)
for patients with CFS 7 and over (combined group) (see
Table 2). If the patients with a CFS 1–3 are combined into
the reference group (‘non-frail’) then patients with CFS 4–5
have an odds of 1.61 (95%CI: 1.13–2.3) and patients with
aCFS7 have an odds of 2.99 (95%CI: 1.43–6.25) of
having a complaint of equal to or more than a level 4 in
any of the domains. e differences between these groups
are summarised in a spider graph depicting all 5 domains
(see Figure 3).
Discussion
In this multicentre study in patients older than 70 years
admitted to an ICU with COVID-19 found that only 39%
of the patients survived up to 90-days. Even worse, within
the group of surviving patients 48% (n= 592) experienced
‘severe problems’ or ‘extreme problems’ in at least one of
the five domains of the EQ-5D-5L questionnaire. ‘Severe
problems’ were mentioned by 41% (n= 496), and extreme
problems’ on one of the five domains in 30% (n= 371).
ere was a clear association between the level of frailty
before hospitalisation and reporting severe problems in any
one of the 5 domains of the health-related quality of life ques-
tionnaire. Particularly pre-frail and frail patients (defined as a
CFS of 4 or above) showed an independent association with
persisting problems after 90 days leading to a diminished
quality of life.
e association between frailty before acute ICU admis-
sion and outcome at 90-days was as expected. Frailty is
defined as ‘a clinically recognisable state of increased vul-
nerability resulting from ageing-associated decline in reserve
and function across multiple physiologic systems such that
the ability to cope with acute stressors is comprised’. Being
admitted with a severe disease such as COVID-19 definitely
fulfils the criteria of an acute stressor. However, this research
is one of the first to show which proportion of older patients
with COVID-19 will have persisting problems and their
severity. Previous research in a small cohort of a slightly
younger ICU population (mean age 65 years) showed that
67% of the surviving patients had a decreased HRQoL
and 62% had a decline in functional status [13]. ese
patients were asked to recall their quality of life prior to
becoming ill and compare it to their current functioning and
self-perceived quality of life. A small Italian observational
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Health-related quality of life in older patients surviving ICU treatment for COVID-19
Figure 2. Association between CFS and severe or worse complaints on any of the 5 domains of the Quality of Life questionnaire.
Percentage of patients complaining of any impairment more than or equal to level 4 answer (‘I have severe complaints ...’) on one
of the 5 domains of the Health-Related Quality of Life questionnaire (EuroQoL-5D-5L). Based on data after multiple imputation.
Ta b l e 2 . Odds of having at least one outcome in the quality of life domains of level 4 (moderate to extreme complaints)
corrected for Katz, age, gender and day 1 SOFA score
CFS score OR 95%CI lower limit 95%CI upper limit P-value
...................................................................................
2 1.17 0.72 1.91 0.526
3 1.62 1.01 2.6 0.046
4 1.89 1.08 3.29 0.025
5 3.77 1.66 8.59 0.002
6 4.17 1.71 10.17 0.002
7+4.22 1.06 16.78 0.041
study looked at the HRQoL of surviving adult patients and
compared that to age- and gender-matched controls [3]. In
that study, survival at 90-days was 58% and the response
rate among survivors was impressive; 78% of the surviving
patients answered their questionnaire. ey showed that
surviving COVID-19 patients had a HRQoL that was (sta-
tistically) worse than an age- and gender-matched control
patients not affected by COVID-19. However, in another,
younger age group (mean age 48 years), the HRQoL was
reduced in one third of the patients, but the patients assessed
their overall health-related quality of life as expressed by an
visual analogue scale (the EQ-VAS) to be equivalent to nor-
mative values for the general adult Italian population [14].
e authors of that study realised that this rather young and
7
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I. W. Soliman et al.
Figure 3. Spider graph depicting outcome of quality of life in non-frail, pre-frail and frail patients. Patients with a CFS 1-3
are considered ‘non-frail’ (green line). Patients with a CFS 4-5 are considered ‘pre-frail’ (purple line) and patients with CFS 7+are
considered ‘frail’ (orange line). On the QoL domains, 1 denotes ‘no problems’, 2 ‘slight problems’, 3 ‘moderate problems’, 4 ‘severe
problems’ and 5 ‘extreme problems’.
previously healthy population might not be representative
for a frailer population.
However, this points towards one of the most important
limitations of self-perceived and self-reported HRQoL: it is
subjective, cultural and changes over time [12]. While the
Euro-QoL questionnaire tries to objectify certain persisting
complaints, the normative societal values for the HRQoL
index are different for various countries. is implies that
various countries, religions and/or cultures value outcomes
differently. It, therefore, remains difficult to compare out-
comes between individuals or between countries. is is
illustrated by the fact that many European countries do
not yet have a normative societal value sets to calculate
an HRQoL index based on the EQ5D five-level variant,
specially stratified by age. We have chosen to circumvent this
limitation by assessing which proportion of patients will have
severe problems in one of the five domains of the HRQoL.
We find this to be clinically relevant regardless of country
and might be used to counsel older patients or their relatives.
Many critically ill patients and particularly their relatives
want to know the chances of survival and their health-
related quality of life if they survive COVID-19. Depending
upon the level of frailty prior to admission, we can now
inform which proportion of patients will have persisting
problems (see Supplementary Table 2, Supplementary data
are available in Age and Ageing online).
Limitations and strengths
One of the major limitations of this study is that it has been
performed in a ‘selected ICU population’. is means that
the patients had been subject to triage decisions prior to ICU
admission. Some patients have been considered to be ‘too
good’ or ‘too bad’ for ICU admission and these patients were
never part of the study population. is might result in a
potentially biased study population, with a better outcome
than the overall patient group of 70 years. is, however, is
reality in every day ICU care and is true for every ICU study.
Indeed, the starting point of our study was the older patient
that had been admitted to the ICU.
e follow-up was limited to 3 months. Some disabilities
require more time to improve and it is, therefore, con-
ceivable that some of the patients will experience further
improvements of their present health-related quality of life.
Another limitation is that in most countries, written
informed consent was mandatory. We know, from previous
research, that this results in a selected study population that
is less ill than when written informed consent would not
have been necessary (the real ICU population will be more
severely ill) [15].
And finally, association should not be confused with
causation. e fact that frail patients with end up with more
severe problems in the health-related quality of life domains
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Health-related quality of life in older patients surviving ICU treatment for COVID-19
does not mean that frailty is causing this or will always
cause this.
However, the strong features of this observational cohort
are that it is performed in many European countries, is
quite large and represents a diverse mixture of cultures and
religions. e results of this research can be translated into
advice for older patients in whom are admitted to the ICU
and want to discuss outcome trajectories. Again, particularly,
older patients (and their relatives) prioritise a good quality
of life over long-term survival [5]. Future research might
focus on a certain quality of life that is unacceptable for
individual patients and we need to establish predictors for
such unwanted outcomes.
Conclusion
Half of the older critically ill old patients who have sur-
vived COVID-19 reported severe to extreme problem after
3 months, which was associated with the level of frailty:
frail patients had a larger proportion of patients experiencing
severe to extreme problems on any of the HRQoL domains.
Such information is important to convey to older patients (or
their relatives) when they need to be admitted to the ICU.
Supplementary Data: Supplementary data mentioned in
the text are available to subscribers in Age and Ageing online.
Acknowledgements: Individual participant data that
underlie the results reported in this article are available
to investigators whose proposed use of the data has
been approved by the COVIP steering committee. e
anonymised data can be requested from the authors if
required, provided that both parties will adhere to all the
legal GDPR requirements.
Declaration of Conflicts of Interest: e authors declare
that they have no competing interests. J.C.S. reports grants
(full departmental disclosure) from Orion Pharma, Abbott
Nutrition International, B. Braun Medical AG, CSEM
AG, Edwards Lifesciences Services GmbH, Kenta Biotech
Ltd, Maquet Critical Care AB, Omnicare Clinical Research
AG, Nestle, Pierre Fabre Pharma AG, Pfizer, Bard Medica
S.A., Abbott AG, Anandic Medical Systems, Pan Gas AG
Healthcare, Bracco, Hamilton Medical AG, Fresenius Kabi,
Getinge Group Maquet AG, Dräger AG, Teleflex Medical
GmbH, Glaxo Smith Kline, Merck Sharp and Dohme AG,
Eli Lilly and Company, Baxter, Astellas, Astra Zeneca, CSL
Behring, Novartis, Covidien, Philips Medical, Phagenesis
Ltd, Prolong Pharmaceuticals and Nycomed outside the
submitted work. e money went into departmental funds.
No personal financial gain applied.
Declaration of Sources of Funding: No (industry) spon-
sorship has been received for this investigator-initiated study.
is study was endorsed by the European Society of Intensive
Care Medicine (ESICM). Free support for running the
electronic database and was granted from the Department
of Epidemiology, University of Aarhus, Denmark. e
study was supported 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. is work was supported
by the Forschungskommission of the Medical Faculty of
the Heinrich-Heine-University Düsseldorf, No. 2018-32 to
G.W. and No. 2020-21 to R.R.B. for a Clinician Scientist
Track.
References
1. Reports of the Dutch ICU registry. https://stichting- nice.nl/
COVID_report.pdf (26 July 2021, date last accessed).
2. Jung C, Flaatten H, Fjølner J et al. e impact of
frailty on survival in elderly intensive care patients with
COVID-19: the COVIP study. Crit Care 2021; 25: 149.
https://doi.org/10.1186/s13054-021-03551-3.
3. Gamberini L, Mazzoli CA, Sintonen H et al. Quality of life
of COVID-19 critically ill survivors after ICU discharge: 90
days follow-up. Qual Life Res 2021; 30: 2805–17.
4. Albu S, Zozaya NR, Murillo N, García-Molina A, Chacón
CAF, Kumru H. What’s going on following acute covid-19?
Clinical characteristics of patients in an out-patient rehabili-
tation program. NeuroRehabilitation 2021; 48: 469–80.
5. Heyland DK, Dodek P, Mehta S et al. Admission of the very
elderly to the intensive care unit: family members’ perspectives
on clinical decision-making from a multicenter cohort study.
Palliat Med 2015; 29: 324–35.
6. Kerckhoffs MC, Kosasi FFL, Soliman IW et al. Determinants
of self-reported unacceptable outcome of intensive care treat-
ment 1 year after discharge. Intensive Care Med 2019; 45:
806–14.
7. Kon AA, Shepard EK, Sederstrom NO et al. Defining futile
and potentially inappropriate interventions: a policy state-
ment from the Society of Critical Care Medicine Ethics
Committee. Crit Care Med 2016; 44: 1769–74.
8. Mikkelsen ME, Still M, Anderson BJ et al. Society of Critical
Care Medicine’s international consensus conference on predic-
tion and identification of long-term impairments after critical
illness. Crit Care Med 2020; 48: 1670–9.
9. Flaatten H, De Lange DW, Morandi A et al. e impact of
frailty on ICU and 30-day mortality and the level of care in
very elderly patients (80 years). Intensive Care Med 2017;
43: 1820–8.
10. Guidet B, de Lange DW, Boumendil A et al. e 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. Intensive Care Med 2020;
46: 57–69.
11. van Hout B, Janssen MF, Feng YS et al. Interim scoring for
the EQ-5D-5L: mapping the EQ-5D-5L to EQ-5D-3L value
sets. Value Health 2012; 15: 708–15.
12. Carr AJ, Gibson B, Robinson PG. Measuring quality of life: is
quality of life determined by expectations or experience? BMJ
2001; 322: 1240–3.
9
Downloaded from https://academic.oup.com/ageing/article/51/2/afab278/6523677 by guest on 20 February 2022
I. W. Soliman et al.
13. Taboada M, Moreno E, Cariñena A et al. Quality of life,
functional status, and persistent symptoms after intensive care
of COVID-19 patients. Br J Anaesth 2021; 126: e110–3.
14. Carenzo L, Protti A, Dalla Corte F et al. Short-term health-
related quality of life, physical function and psychological
consequences of severe COVID-19. Ann Intensive Care 2021;
11: 91. https://doi.org/10.1186/s13613-021-00881-x.
15. Kho ME, Duffett M, Willison DJ, Cook DJ, Brouwers MC.
Written informed consent and selection bias in observational
studies using medical records: systematic review. BMJ 2009;
338: b866. https://doi.org/10.1136/bmj.b866.
Received 29 September 2021; editorial decision 17
November 2021
10
Downloaded from https://academic.oup.com/ageing/article/51/2/afab278/6523677 by guest on 20 February 2022
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Purpose Premorbid conditions affect prognosis of acutely-ill aged patients. Several lines of evidence suggest geriatric syndromes need to be assessed but little is known on their relative effect on the 30-day survival after ICU admission. The primary aim of this study was to describe the prevalence of frailty, cognition decline and activity of daily life in addition to the presence of comorbidity and polypharmacy and to assess their influence on 30-day survival. Methods Prospective cohort study with 242 ICUs from 22 countries. Patients 80 years or above acutely admitted over a six months period to an ICU between May 2018 and May 2019 were included. In addition to common patients’ characteristics and disease severity, we collected information on specific geriatric syndromes as potential predictive factors for 30-day survival, frailty (Clinical Frailty scale) with a CFS > 4 defining frail patients, cognitive impairment (informant questionnaire on cognitive decline in the elderly (IQCODE) with IQCODE ≥ 3.5 defining cognitive decline, and disability (measured the activity of daily life with the Katz index) with ADL ≤ 4 defining disability. A Principal Component Analysis to identify co-linearity between geriatric syndromes was performed and from this a multivariable model was built with all geriatric information or only one: CFS, IQCODE or ADL. Akaike’s information criterion across imputations was used to evaluate the goodness of fit of our models. Results We included 3920 patients with a median age of 84 years (IQR: 81–87), 53.3% males). 80% received at least one organ support. The median ICU length of stay was 3.88 days (IQR: 1.83–8). The ICU and 30-day survival were 72.5% and 61.2% respectively. The geriatric conditions were median (IQR): CFS: 4 (3–6); IQCODE: 3.19 (3–3.69); ADL: 6 (4–6); Comorbidity and Polypharmacy score (CPS): 10 (7–14). CFS, ADL and IQCODE were closely correlated. The multivariable analysis identified predictors of 1-month mortality (HR; 95% CI): Age (per 1 year increase): 1.02 (1.–1.03, p = 0.01), ICU admission diagnosis, sequential organ failure assessment score (SOFA) (per point): 1.15 (1.14–1.17, p < 0.0001) and CFS (per point): 1.1 (1.05–1.15, p < 0.001). CFS remained an independent factor after inclusion of life-sustaining treatment limitation in the model. Conclusion We confirm that frailty assessment using the CFS is able to predict short-term mortality in elderly patients admitted to ICU. Other geriatric syndromes do not add improvement to the prediction model. Since CFS is easy to measure, it should be routinely collected for all elderly ICU patients in particular in connection to advance care plans, and should be used in decision making.