Clinical Frailty Scale (CFS) Indicated Frailty is Associated with Increased In-Hospital and 30-Day Mortality in COVID-19 Patients: A Systematic Review and Meta-Analysis

Abstract

Background: The concept of frailty provides an age-independent, easy-to-use tool for risk stratification. We aimed to summarize the evidence regarding the use of frailty tools in COVID-19, assessing the risk of frail patients for in-hospital and 30-day mortality, intensive care unit (ICU) admission, and length of hospitalization (LOH).

Methods: The protocol was prospectively registered via PROSPERO (CRD42021241544). Studies reporting on frailty in COVID-19 patients were eligible. We compared in-hospital and 30-day mortality, lengths of stay and ICU admission in frail and non-frail COVID-19 patients. We also compared average frailty in COVID-19 survivors and non-survivors. MEDLINE (via PubMed), EMBASE, Scopus, CENTRAL, and Web of Science were searched up to 3 February 2021 with terms related to COVID-19 and frail*. Search, selection, data extraction and risk of bias assessment were conducted in duplicate by two independent review authors. The QUIPS tool was used for the risk of bias assessment. Odds ratios (OR) and weighted mean differences (WMD) were calculated with 95% confidence intervals (CI) using a random effect model. Heterogeneity was assessed using the I² and χ² tests.

Results: From 1693 records identified, 27 were included in the qualitative and 21 in the quantitative synthesis. Clinical Frailty Scale (CFS) was used in 24 studies, the Hospital Frailty Risk Score (HFRS) by one and the Frail Non-Disabled questionnaire by another one. One study reported both CFS and HFRS. We found that frail patients (CFS 5–9) compared to non-frail patients (CFS 1–4) have a higher risk for both in-hospital (OR: 2.77; CI: 1.86–4.15) and 30-day mortality (OR: 1.47; CI:1.05–2.06). Frail patients (CFS 5–9) were less likely to be admitted to ICU (OR 0.05, CI: 0.01–0.16). Statistical heterogeneity was not present for CFS 5–9 30-day mortality OR and ICU admission OR (CFS 1–3 vs 4–9), and was moderate for in-hospital mortality WMD. Quantitative synthesis for LOH was not feasible. Most studies carried a high risk of bias.

Conclusions: As determined by CFS, frailty is strongly associated with in-hospital and 30-day mortality; hence, investigating its use in deciding on ICU admission further in COVID-19 is warranted.

Background

Since the outbreak of the SARS-CoV-2 pandemic, healthcare systems around the world face shortage of resources; therefore, identifying factors that predict negative outcomes in COVID-19 is essential. The use of risk stratification tools for protocolized admission and determination of ceiling of care could help the decision-making and create transparency in these uncertain times.

Clinical frailty describes a state of reduced physical, physiologic, and cognitive reserve [1]. The concept of frailty provides an age-independent measure for risk stratification in the form of fast, easy-to-use tools. However, the wide variety of frailty tools makes frailty assessment heterogenous. The clinical frailty scale (CFS) was created by Rockwood et al. in 2005 to provide a simple approach with good predictive value [2]. The original 7-point scale was later upgraded to 9-points, one for the "severely frail", "very severely frail", and one for the "terminally ill" [3]. In the terminology used until 2020, points 1 to 4 covered patients described as "very fit", "well", "managing well", and "vulnerable". The revision published by Rockwood and Theou classified formerly "well" patients to "fit" and "vulnerable" patients to "living with very mild frailty" [4]. The score also assesses comorbidities, physical and cognitive abilities.

The CFS is widely used in different clinical settings [5]. CFS outperformed the Charlson comorbidity index and age in predicting in-hospital mortality of patients older than 75 years with emergency hospital admission [6] and is an independent predictor of short- and long-term mortality in patients over 70 admitted to the ICU [7]. The Hospital Frailty Risk Score (HFRS) was developed to assess frailty in older individuals automatically from routinely collected data (using ICD codes).

Frailty assessment was adopted in many guidelines in the triage of COVID-19 patients to aid decision-making regarding intensive care admission or the commencement of mechanical ventilation [8]. Recent studies and a meta-analysis reported higher odds and hazard ratios for mortality in frail COVID-19 patients [9–11].

We aimed to provide a detailed summary on the use of frailty tools in COVID-19, assessing the odds of frail patients for in-hospital and 30-day mortality, ICU admission, and length of hospitalization (LOH).

Methods

Eligibility And Definitions

We formulated our clinical question using the PECO format. Based on preliminary searches, we chose to use two PECOs. We selected studies reporting on adult hospitalized patients with COVID-19, comparing frail (or frailer) patients to not frail (or less frail) patients. The assessed outcomes were all-cause in-hospital and 30-day mortality, ICU admission, and LOH. In our other analysis, the average frailty score of deceased COVID-19 patients was compared to survivors'.

COVID-19 positivity was defined as clinical, radiological, or laboratory diagnosis [13]. Any validated frailty scores and indexes were included, as well as non-validated ones, if the record contained sufficient information on the used index.

Studies with original data reporting on at least ten patients were eligible independently of study design. Abstracts and full-texts were both accepted.

Search And Selection

We searched MEDLINE (via PubMed), EMBASE, Scopus, The Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science on the 3rd of February 2021 for eligible articles. We used “Title, Abstract, Keywords” filter in Scopus. No other filters or restrictions were applied. We also scanned the reference lists of the included studies and their citations in Google Scholar. The following search key was used: ("covid 19" OR "Wuhan virus" OR coronavirus OR "2019 nCoV" OR SARS-cov-2) AND frail*.

After removing duplicates using a reference management software (EndNote X9, Clarivate Analytics), two review authors (MR and TL) independently screened titles, abstracts, and then full-texts against predefined eligibility criteria. Discrepancies were resolved by a third review author (ZM). Inter-rater reliability was determined at every phase by Cohen's kappa coefficient, where values 0.01–0.20 indicate slight, 0.21–0.40 indicate fair, 0.41–0.60 indicate moderate, 0.61–0.80 indicate substantial and 0.81–1.00 indicate almost perfect or perfect agreement, respectively [14].

Outcomes reported by at least three studies using the same frailty score comparing identical frailty subgroups were included in the meta-analysis. All other eligible studies were incorporated into the qualitative synthesis.

Data Collection

Data on the first author, publication year, countries, study design, number of patients in each comparison group, their baseline characteristics (sex, age), type of frailty score used, and available primary and secondary outcome parameters were extracted by two independent review authors (MR and LT) in duplicate using our standardized data collection form in Microsoft Excel. Disagreements were resolved by a third independent investigator (KO). Data from studies reporting individual patient data or raw data were regrouped if statistically feasible. Overlapping populations were identified, and the study with the largest sample size was included in the analyses.

Risk Of Bias

Following the recommendations of the Cochrane Collaboration, the Quality in Prognosis Studies (QUIPS) tool was used by MR and TL independently [15]. Disagreements were resolved by ZM. In the study participation domain, gender, age, ethnicity, and comorbidities were taken into account. Study attrition was not judged for retrospective studies. In the prognostic factor measurement domain, the specification of the frailty assessor, information about their training, and missing data on frailty were considered. Less than 10% missing data were considered low risk, 10–20% some concerns, and more than 20% resulted in high risk for the whole domain. Outcome measurement and statistical analysis domains carried low risk in most cases because mortality is a hard outcome, and we mostly used raw data. In the case of ICU admission, a detailed protocol for ICU admission was needed. In the study confounding domain, studies separately reporting baseline information for the frailty groups were judged low risk if no clinically significant differences were seen, some concerns if some differences were seen, and high risk if no data was reported. The overall risk of bias was calculated using the suggestions of Grooten et al. [16].

Results

Discussion

In this systematic review and meta-analysis on the relationship between frailty and mortality, ICU admission, and LOH in COVID-19 patients, with the inclusion of 27 studies and 28,400 subjects, we found that frail patients have significantly elevated odds for both in-hospital and 30-day mortality in COVID-19. Another important finding of our study is that frail patients are less likely to be admitted to the ICU.

Despite advances in emergency and intensive therapy, mortality of the critically ill in general and also in COVID-19 remains high [47-49]. Advanced organ support – the cornerstone of intensive care –, may interfere with human dignity. The relatively high mortality and the required work intensity means a burden for the staff and relatives alike, and is, last but not least, costly [50, 51]. Therefore, prolonged, advanced organ support can be regarded as medically futile in those cases, whose chances are extremely limited for survival [52, 53]. Hence predictors of survival have been extensively researched. Due to the unprecedented load on ICUs during the pandemic of COVID-19, implementing a reliable tool to identify those who could not benefit from intensive care would be of utmost help for clinicians, patients, and relatives alike.

It has been known for long that age on its own can be misleading in outcome prediction [54]. A potential alternative is the frailty assessment, a concept that has already been supported during the COVID-19 pandemic by some studies and a recent meta-analysis [9-11]. The results of the current meta-analysis are in line with these findings and provide further support that frailty assessment could serve as a useful tool in predicting outcomes. Our results also suggest that the CSF could potentially help in the selection process of those patients who could not benefit from intensive care.

However, frailty assessment-based decision-making has not been implemented worldwide. According to our literature search (up to February 2021), studies from the UK, the Netherlands, Belgium, and France, COVID-19 guidelines advised the use of CFS in decision-making [55-57]. These countries had high-quality health care and much better resources even before the COVID-19 pandemic as compared to countries with less developed healthcare systems.

As the clinically more aggressive variants are spreading across the world, including Eastern and Central Europe – the home region of the authors –, the effective allocation of resources would be of utmost importance. These countries were more-or-less speared during the first wave and also in the second wave when mortality rates were higher in Western and Northern European countries [58-60]. However, the third wave proved devastating in this region of Europe from both the ICU-burden and the survival perspectives [61].

Although ethical concerns were raised against frailty-based decision-making, this method potentially provides a professional and transparent scaffold for health care providers [62, 63]. It has also been shown that the decision to withhold or withdraw life-sustaining treatment from patients older than 80 years in the ICU correlates with income and religious influence. In countries with lower income and higher religiosity, high-intensity critical care treatments are less frequently withdrawn, and the decision does not depend on age and ICU bed availability [64]. In a recent multicenter, multinational prospective observational study on 1346 elderly (>70) ICU COVID-19 patients, frailty provided relevant prognostic information in addition to age and comorbidities [65]. This data supports our findings that frailty could be a valuable tool in risk stratification in this patient population. Furthermore, an indirect comparison by Kow et al. has indicated that frail individuals may be overrepresented among the COVID-19 patient population and given a rather strong hint that the presence of frailty may lead to a higher risk of acquisition of COVID-19 [66]. In addition to frailty, there are other tools such as the 4C mortality score with good performance [67]; however, they were never directly compared with frailty assessment tools.

Finally, it would be desirable that based on the available scientific evidence, health authorities encouraged and supported the implementation of frailty-based risk assessment into national guidelines.

Strengths And Limitations

To our knowledge, this is the most detailed evaluation of frailty in COVID-19, separately analysing 30-day and in-hospital mortality, studies from and outside of the UK and age groups. We also assessed the relationship between frailty and ICU admission. Another methodological strength is that leave-one-out sensitivity analysis was also performed to identify influential studies. The majority of the included studies were retrospective and carried high risk of bias, therefore could introduce bias in our analysis. Nevertheless, one of the most important limitations of our study is the considerable heterogeneity that was a common feature in many of the analyses. The explanation could lie in standard medical practice, age distribution, and nurse-to-patient ratio, which can differ between countries and hospitals. Furthermore, studies implementing frailty assessment in COVID-19 were not published from Central and Eastern European countries; therefore, they were not represented in the quantitative analyses.

Conclusion

To the best of our knowledge, this is the largest, most recent and comprehensive meta-analysis of studies in this topic today in COVID-19 patients. Our results show that frailty as determined by CFS is strongly associated with in-hospital and 30-day mortality and may also play an important role in determining eligibility for ICU admission in patients suffering from COVID-19. These findings have implications for research: they render the need for further comparisons of different frailty scores in COVID-19 patients in order to determine the most appropriate. Based on our results, the effects of frailty-based patient management on ICU admission, mortality and hospital readmission rate should also be investigated in the future. Regarding implications for practice, we believe that frailty-based patient management should be included in international COVID-19 treatment guidelines.

Abbreviations

ICU: intensive care unit; LOH: length of hospitalization; CENTRAL: The Cochrane Central Register of Controlled Trials; QUIPS: Quality in Prognosis Studies tool; OR: odds ratio; WMD: weighted mean difference; CI: confidence interval; CFS: Clinical Frailty Scale; HFRS: Hospital Frailty Risk Score; ICD: international classification of diseases; PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PECO: Patient, Exposure, Control, Outcome; SD: standard deviation; FiND: Frail Non-Disabled questionnaire; IQ: interquartile; UK: United Kingdom; n/a: not available; n/r: no restriction; P/C: prospective cohort; R/C: retrospective cohort; PCR: polymerase chain reaction; Clin: clinical; Rad: radiological; CT: computer tomography.

Declarations

Ethics approval and consent to participate

Given the nature of this study, ethic approval and consent were not required.

Consent for publication

All authors consent to publication of the manuscript and support material.

Availability of data and materials

Original data is available from the corresponding author on reasonable request.

Competing interests

We declare no competing interests.

Funding

This work was funded by the Economic Development and Innovation Operational Programme Grant (GINOP-2.3.2-15-2016-00048 – STAY ALIVE and GINOP-2.3.4-15-2020-00010 Competence Center for Health Data Analysis, Data Utilisation and Smart Device and Technology Development at the University of Pécs). The funding body did not take part in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Authors' contributions

RM performed the systematic search and selection, data extraction, risk of bias assessment and wrote the methods and results sections of the manuscript. KO provided methodological counsel in all phases from preliminary searches to writing of the manuscript and wrote the introduction and discussion sections of the manuscript. AG performed the statistical analyses. TL performed the systematic search and selection, data extraction, risk of bias assessment and provided expert opinion on the use of frailty in intensive care. MV prepared the figures. PH, TM and BE provided expert opinion during the writing of the manuscript. ZM coordinated the work and provided expert opinion. All authors read and approved the manuscript.

Acknowledgements

 We would like to thank Szabolcs Kiss and Fanni Dembrovszky for the methodological workshops and advice.

References

1. Xue Q-L. The frailty syndrome: definition and natural history. Clin Geriatr Med. 2011;27(1):1–15.
2. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A. A global clinical measure of fitness and frailty in elderly people. Can Med Assoc J. 2005;173(5):489.
3. Pulok MH, Theou O, van der Valk AM, Rockwood K. The role of illness acuity on the association between frailty and mortality in emergency department patients referred to internal medicine. Age Ageing. 2020;49(6):1071–9.
4. Rockwood K, Theou O. Using the Clinical Frailty Scale in Allocating Scarce Health Care Resources. Can Geriatr J. 2020;23(3):210–5.
5. Church S, Rogers E, Rockwood K, Theou O. A scoping review of the Clinical Frailty Scale. BMC Geriatr. 2020;20(1):393.
6. Wallis SJ, Wall J, Biram RW, Romero-Ortuno R. Association of the clinical frailty scale with hospital outcomes. QJM: monthly journal of the Association of Physicians. 2015;108(12):943–9.
7. Silva-Obregón JA, Quintana-Díaz M, Saboya-Sánchez S, Marian-Crespo C, Romera-Ortega M, Chamorro-Jambrina C, Estrella-Alonso A, Andrés-Esteban EM. Frailty as a predictor of short- and long-term mortality in critically ill older medical patients. J Crit Care. 2020;55:79–85.
8. Maltese G, Corsonello A, Di Rosa M, Soraci L, Vitale C, Corica F, Lattanzio F. Frailty and COVID-19: A Systematic Scoping Review. Journal of Clinical Medicine 2020, 9(7).
9. Kow CS, Hasan SS, Thiruchelvam K, Aldeyab M. Association of frailty and mortality in patients with COVID-19: a meta-analysis. Br J Anaesth 2020.
10. Zhang X-M, Jiao J, Cao J, Huo X-P, Zhu C, Wu X-J, Xie X-H. Frailty as a predictor of mortality among patients with COVID-19: a systematic review and meta-analysis. BMC Geriatrics. 2021;21(1):186.
11. Pranata R, Henrina J, Lim MA, Lawrensia S, Yonas E, Vania R, Huang I, Lukito AA, Suastika K, Kuswardhani RAT, et al: Clinical frailty scale and mortality in COVID-19: A systematic review and dose-response meta-analysis: Clinical Frailty Scale in COVID-19. Archives of Gerontology and Geriatrics 2021, 93.
12. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.
13. Byrne D, Neill SBO, Müller NL, Müller CIS, Walsh JP, Jalal S, Parker W, Bilawich AM, Nicolaou S. RSNA Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19: Interobserver Agreement Between Chest Radiologists. Can Assoc Radiol J. 2021;72(1):159–66.
14. Landis JR, Koch GG. The Measurement of Observer Agreement for Categorical Data. Biometrics. 1977;33(1):159–74.
15. Hayden JA, Côté P, Bombardier C. Evaluation of the Quality of Prognosis Studies in Systematic Reviews. Ann Intern Med. 2006;144(6):427–37.
16. Grooten WJA, Tseli E, Äng BO, Boersma K, Stålnacke B-M, Gerdle B, Enthoven P. Elaborating on the assessment of the risk of bias in prognostic studies in pain rehabilitation using QUIPS—aspects of interrater agreement. Diagnostic Prognostic Research. 2019;3(1):5.
17. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14(1):135.
18. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.
19. Thabane L, Mbuagbaw L, Zhang S, Samaan Z, Marcucci M, Ye C, Thabane M, Giangregorio L, Dennis B, Kosa D, et al. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med Res Methodol. 2013;13(1):92.
20. Apea VJ, Wan YI, Dhairyawan R, Puthucheary ZA, Pearse RM, Orkin CM, Prowle JR. Ethnicity and outcomes in patients hospitalised with COVID-19 infection in East London: an observational cohort study. BMJ Open. 2021;11(1):e042140.
21. Aw D, Woodrow L, Ogliari G, Harwood R. Association of frailty with mortality in older inpatients with Covid-19: a cohort study. Age Ageing. 2020;49(6):915–22.
22. Bielza R, Sanz J, Zambrana F, Arias E, Malmierca E, Portillo L, Thuissard IJ, Lung A, Neira M, Moral M, et al: Clinical Characteristics, Frailty, and Mortality of Residents With COVID-19 in Nursing Homes of a Region of Madrid. J Am Med Dir Assoc 2020.
23. Blomaard LC, van der Linden CMJ, van der Bol JM, Jansen SWM, Polinder-Bos HA, Willems HC, Festen J, Barten DG, Borgers AJ, Bos JC, et al: Frailty is associated with in-hospital mortality in older hospitalised COVID-19 patients in the Netherlands: the COVID-OLD study. Age Ageing 2021.
24. Bradley P, Frost F, Tharmaratnam K, Wootton DG. Utility of established prognostic scores in COVID-19 hospital admissions: Multicentre prospective evaluation of CURB-65, NEWS2 and qSOFA. BMJ Open Respiratory Research 2020, 7(1).
25. Brill SE, Jarvis HC, Ozcan E, Burns TLP, Warraich RA, Amani LJ, Jaffer A, Paget S, Sivaramakrishnan A, Creer DD. COVID-19: a retrospective cohort study with focus on the over-80s and hospital-onset disease. BMC Med. 2020;18(1):194.
26. Burns GP, Lane ND, Tedd HM, Deutsch E, Douglas F, West SD, Macfarlane JG, Wiscombe S, Funston W. Improved survival following ward-based non-invasive pressure support for severe hypoxia in a cohort of frail patients with COVID-19: retrospective analysis from a UK teaching hospital. BMJ Open Respir Res 2020, 7(1).
27. Chinnadurai R, Ogedengbe O, Agarwal P, Money-Coomes S, Abdurrahman AZ, Mohammed S, Kalra PA, Rothwell N, Pradhan S. Older age and frailty are the chief predictors of mortality in COVID-19 patients admitted to an acute medical unit in a secondary care setting- a cohort study. BMC Geriatr. 2020;20(1):409.
28. Davis P, Gibson R, Wright E, Bryan A, Ingram J, Lee RP, Godwin J, Evans T, Burleigh E, Wishart S, et al: Atypical presentations in the hospitalised older adult testing positive for SARS-CoV-2: a retrospective observational study in Glasgow, Scotland. Scott Med J 2020:36933020962891.
29. De Smet R, Mellaerts B, Vandewinckele H, Lybeert P, Frans E, Ombelet S, Lemahieu W, Symons R, Ho E, Frans J, et al. Frailty and Mortality in Hospitalized Older Adults With COVID-19: Retrospective Observational Study. J Am Med Dir Assoc. 2020;21(7):928–32.e921.
30. Fagard K, Gielen E, Deschodt M, Devriendt E, Flamaing J. Risk factors for severe COVID-19 disease and death in patients aged 70 and over: a retrospective observational cohort study. Acta Clin Belg 2021:1–8.
31. Gilis M, Chagrot N, Koeberle S, Tannou T, Brunel AS, Chirouze C, Bouiller K. Older adults with SARS-CoV-2 infection: Utility of the clinical frailty scale to predict mortality. J Med Virol 2020.
32. Hewitt J, Carter B, Vilches-Moraga A, Quinn TJ, Braude P, Verduri A, Pearce L, Stechman M, Short R, Price A, et al. The effect of frailty on survival in patients with COVID-19 (COPE): a multicentre, European, observational cohort study. Lancet Public Health. 2020;5(8):e444–51.
33. Hoek RAS, Manintveld OC, Betjes MGH, Hellemons ME, Seghers L, Van Kampen JAA, Caliskan K, van de Wetering J, van den Hoogen M, Metselaar HJ, et al. COVID-19 in solid organ transplant recipients: a single-center experience. Transpl Int. 2020;33(9):1099–105.
34. Knights H, Mayor N, Millar K, Cox M, Bunova E, Hughes M, Baker J, Mathew S, Russell-Jones D, Kotwica A. Characteristics and outcomes of patients with COVID-19 at a district general hospital in Surrey, UK. Clin Med (Lond). 2020;20(5):e148–53.
35. Kundi H, Çetin E, Canpolat U, Aras S, Celik O, Ata N, Birinci S, Çay S, Özeke Ö, Tanboğa IH, et al. The role of Frailty on Adverse Outcomes Among Older Patients with COVID-19. J Infect. 2020;81(6):944–51.
36. Marengoni A, Zucchelli A, Vetrano DL, Armellini A, Botteri E, Nicosia F, Romanelli G, Beindorf EA, Giansiracusa P, Garrafa E, et al: Beyond chronological age: Frailty and multimorbidity predict in-hospital mortality in patients with coronavirus disease 2019. J Gerontol A Biol Sci Med Sci 2020.
37. McWilliams D, Weblin J, Hodson J, Veenith T, Whitehouse T, Snelson C. Rehabilitation Levels in Patients with COVID-19 Admitted to Intensive Care Requiring Invasive Ventilation An Observational Study. Annals of the American Thoracic Society. 2021;18(1):122–9.
38. Mendes A, Serratrice C, Herrmann FR, Genton L, Périvier S, Scheffler M, Fassier T, Huber P, Jacques MC, Prendki V, et al. Predictors of In-Hospital Mortality in Older Patients With COVID-19: The COVIDAge Study. J Am Med Dir Assoc. 2020;21(11):1546–54.e1543.
39. Moledina SM, Maini AA, Gargan A, Harland W, Jenney H, Phillips G, Thomas K, Chauhan D, Fertleman M. Clinical Characteristics and Predictors of Mortality in Patients with COVID-19 Infection Outside Intensive Care. Int J Gen Med. 2020;13:1157–65.
40. Moloney E, Eustace J, O’ Caoimh R, O’connor K, O’sullivan C, Jackson A, McGrath K, Lapthorne S, Mahony DO, Sullivan PO, et al. Frailty, covid-19 disease severity and outcome among hospitalised older adults. Ir Med J. 2020;113(10):1–11.
41. Osuafor CN, Davidson C, Mackett AJ, Goujon M, Van Der Poel L, Taylor V, Preller J, Goudie RJB, Keevil VL. Clinical Features, Inpatient Trajectories and Frailty in Older Inpatients with COVID-19: A Retrospective Observational Study. Geriatrics (Basel) 2021, 6(1).
42. Owen RK, Conroy SP, Taub N, Jones W, Bryden D, Pareek M, Faull C, Abrams KR, Davis D, Banerjee J. Comparing associations between frailty and mortality in hospitalised older adults with or without COVID-19 infection: a retrospective observational study using electronic health records. Age Ageing 2020.
43. Piers R, Janssens W, Cobbaert K, Pattyn I, Westhovens I, Martens H, Van Puyvelde K, Maertens S, Guyssens V, Baeyens H, et al: Letter to the Editor: Premorbid Frailty is a better Prognostic Indicator than Age in Oldest-Old Hospitalized with COVID-19. J Am Med Dir Assoc 2021.
44. Steinmeyer Z, Vienne-Noyes S, Bernard M, Steinmeyer A, Balardy L, Piau A, Sourdet S. Acute Care of Older Patients with COVID-19: Clinical Characteristics and Outcomes. Geriatrics (Basel) 2020, 5(4).
45. Straw S, McGinlay M, Drozd M, Slater TA, Cowley A, Kamalathasan S, Maxwell N, Bird RA, Koshy AO, Prica M, et al. Advanced care planning during the COVID-19 pandemic: ceiling of care decisions and their implications for observational data. BMC Palliat Care. 2021;20(1):10.
46. Tehrani S, Killander A, Åstrand P, Jakobsson J, Gille-Johnson P. Risk factors for death in adult COVID-19 patients: Frailty predicts fatal outcome in older patients. Int J Infect Dis. 2021;102:415–21.
47. Máca J, Jor O, Holub M, Sklienka P, Burša F, Burda M, Janout V, Ševčík P. Past and Present ARDS Mortality Rates: A Systematic Review. Respir Care. 2017;62(1):113–22.
48. Armstrong RA, Kane AD, Cook TM. Outcomes from intensive care in patients with COVID-19: a systematic review and meta-analysis of observational studies. Anaesthesia. 2020;75(10):1340–9.
49. Seymour CW, Kennedy JN, Wang S, Chang C-CH, Elliott CF, Xu Z, Berry S, Clermont G, Cooper G, Gomez H, et al. Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis. JAMA. 2019;321(20):2003–17.
50. Rangachari P. J LW: Preserving Organizational Resilience, Patient Safety, and Staff Retention during COVID-19 Requires a Holistic Consideration of the Psychological Safety of Healthcare Workers. Int J Environ Res Public Health 2020, 17(12).
51. Bartsch SM, Ferguson MC, McKinnell JA, O'Shea KJ, Wedlock PT, Siegmund SS, Lee BY. The Potential Health Care Costs And Resource Use Associated With COVID-19 In The United States. Health Aff (Millwood). 2020;39(6):927–35.
52. Cardona M, Anstey M, Lewis ET, Shanmugam S, Hillman K, Psirides A. Appropriateness of intensive care treatments near the end of life during the COVID-19 pandemic. Breathe. 2020;16(2):200062.
53. Savulescu J, Vergano M, Craxì L, Wilkinson D. An ethical algorithm for rationing life-sustaining treatment during the COVID-19 pandemic. Br J Anaesth. 2020;125(3):253–8.
54. Chelluri L, Grenvik A, Silverman M. Intensive care for critically ill elderly: mortality, costs, and quality of life. Review of the literature. Arch Intern Med. 1995;155(10):1013–22.
55. Azoulay É, Beloucif S, Guidet B, Pateron D, Vivien B, Le Dorze M. Admission decisions to intensive care units in the context of the major COVID-19 outbreak: local guidance from the COVID-19 Paris-region area. Crit Care. 2020;24(1):293.
56. Excellence NIfhaC. COVID-19 rapid guideline: critical care in adults, 2020. In.
57. Geert Meyfroidt EV, Biston P, Decker KD, Wittebole X, Collin V, Depuydt P, Nam ND, Hermans G, Jorens P. Didier Ledoux, Fabio Taccone, Ignaas Devisch: Ethical principles concerning proportionality of critical care during the 2020 COVID-19 pandemic in Belgium: advice by the Belgian Society of Intensive care medicine.
58. Villani L, McKee M, Cascini F, Ricciardi W, Boccia S. Comparison of Deaths Rates for COVID-19 across Europe During the First Wave of the COVID-19 Pandemic. Frontiers in Public Health 2020, 8(910).
59. Comparisons of all-cause mortality between European countries and regions: 2020 https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/comparisonsofallcausemortalitybetweeneuropeancountriesandregions/2020.
60. James N, Menzies M, Radchenko P. COVID-19 second wave mortality in Europe and the United States. Chaos. 2021;31(3):031105.
61. https://ourworldindata.org/covid-deaths.
62. Wilkinson DJC. Frailty Triage: Is Rationing Intensive Medical Treatment on the Grounds of Frailty Ethical? The American Journal of Bioethics 2020:1–22.
63. De Biasio JC, Mittel AM, Mueller AL, Ferrante LE, Kim DH, Shaefi S. Frailty in Critical Care Medicine: A Review. Anesthesia Analgesia. 2020;130(6):1462–73.
64. Guidet B, Flaatten H, Boumendil A, Morandi A, Andersen FH, Artigas A, Bertolini G, Cecconi M, Christensen S, Faraldi L, et al. Withholding or withdrawing of life-sustaining therapy in older adults (≥ 80 years) admitted to the intensive care unit. Intensive Care Med. 2018;44(7):1027–38.
65. Jung C, Flaatten H, Fjølner J, Bruno RR, Wernly B, Artigas A, Bollen Pinto B, Schefold JC, Wolff G, Kelm M, et al. The impact of frailty on survival in elderly intensive care patients with COVID-19: the COVIP study. Crit Care. 2021;25(1):149.
66. Kow CS, Hasan SS. PREVALENCE OF FRAILTY IN PATIENTS WITH COVID-19: A META-ANALYSIS. Journal of Frailty & Aging.
67. Knight SR, Ho A, Pius R, Buchan I, Carson G, Drake TM, Dunning J, Fairfield CJ, Gamble C, Green CA, et al. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. BMJ. 2020;370:m3339.