Frailty and mortality in patients with COVID-19
Bibliographic record
Abstract
We read with interest the study by Jonathan Hewitt and colleagues,1Hewitt J Carter B Vilches-Moraga 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: e444-e451Summary Full Text Full Text PDF PubMed Scopus (422) Google Scholar investigating the association of frailty with mortality in 1564 patients diagnosed with COVID-19. We are interested in how treatment varied between patients in different frailty categories. Although the outcomes were adjusted for baseline patient factors, including age and comorbidities, we note that neither illness severity nor important process measures after admission to hospital, such as intensive care unit admission or mechanical ventilation, were reported. Without these data, it is hard to say to what degree frailty per se is intrinsically associated with increased mortality in COVID-19, or whether the reported mortality is perhaps related to the combination of a more severe illness and less intensive treatment type offered to patients with advanced frailty, consistent with the guidance from the National Institute for Health and Care Excellence and earlier work, which show greater treatment limitations in older, frail cohorts.2National Institute for Health and Care ExcellenceCOVID-19 rapid guideline: critical care in adults. NICE guideline [NG159].https://www.nice.org.uk/guidance/ng159Date: March 20, 2020Date accessed: March 29, 2020Google Scholar, 3Darvall JN Bellomo R Paul E et al.Frailty in very old critically ill patients in Australia and New Zealand: a population-based cohort study.Med J Aust. 2019; 211: 318-323Crossref PubMed Scopus (57) Google Scholar The latest intensive care national audit & research centre report (published on June 19, 2020), incorporating 9949 critically ill UK patients with COVID-19, reports a median patient age of 60 years (vs 74 years in Hewitt and colleagues' cohort), with 90% of patients fully independent ("able to live without assistance in daily activities") before hospitalisation.4Intensive care national audit & research centreICNARC report on COVID-19 in critical care.https://www.icnarc.org/DataServices/Attachments/Download/da19fd54-70b2-ea11-9127-00505601089bDate: June 19, 2020Date accessed: July 3, 2020Google Scholar These numbers compare with 40% of this study's cohort being classified as having a Clinical Frailty Scale score of 6 or more (at least moderately frail); by definition requiring considerable assistance with daily activities.5Rockwood K Song X MacKnight C et al.A global clinical measure of fitness and frailty in elderly people.CMAJ. 2005; 173: 489-495Crossref PubMed Scopus (4951) Google Scholar Hewitt and colleagues'1Hewitt J Carter B Vilches-Moraga 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: e444-e451Summary Full Text Full Text PDF PubMed Scopus (422) Google Scholar study cohort, then, is not typical of the COVID-19 population being managed in UK intensive care units. Without more detail on how patients in this study were treated, and what effect this had on mortality, we are concerned that the statement "these findings support the use of frailty as a trigger for specialist resource allocation" is not supported by the data presented. KR reports personal fees from Clinical Cardio Day-Cape Breton University, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Jackson Lab (Bar Harbor, MA, USA), MouseAge (Rome, Italy), Lundbeck, Frontemporal Dementia Study Group, and SunLife Insurance (Japan); and is the President and Chief Science Officer of DGI Clinical, which in the last 5 years has contracts with pharmaceutical and device manufacturers (Baxter, Baxalta, Shire, Hollister, Nutricia, Roche, and Otsuka) on individualised outcome measurement; attended an advisory board meeting with Lundbeck in 2017; is the Associate Director of the Canadian Consortium on Neurodegeneration in Aging, which is funded by the Canadian Institutes of Health Research, and with additional funding from the Alzheimer Society of Canada and several other charities, as well as, in its first phase (2013-2018), from Pfizer Canada and Sanofi Canada; receives career support from the Dalhousie Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research, and research support from the Canadian Institutes of Health Research, the QEII Health Science Centre Foundation, the Capital Health Research Fund and the Fountain Family Innovation Fund of the QEII Health Science Centre Foundation. All other authors declare no competing interests. Frailty and mortality in patients with COVID-19The COVID-19 in Older People (COPE) study by Jonathan Hewitt and colleagues1 supports the use of the Clinical Frailty Scale (CFS) for prognostication. These findings support our recently published single-center experience.2 However, several aspects of this study deserve clarification. Full-Text PDF Open AccessFrailty and mortality in patients with COVID-19 – Authors' replyWe thank all the research teams who have submitted letters in response to our frailty and COVID-19 paper.1 This study has generated discussion in scientific journals, lay press, and social media. We welcome the opportunity to further discuss our methods and results. Full-Text PDF Open AccessThe effect of frailty on survival in patients with COVID-19 (COPE): a multicentre, European, observational cohort studyIn a large population of patients admitted to hospital with COVID-19, disease outcomes were better predicted by frailty than either age or comorbidity. Our results support the use of CFS to inform decision making about medical care in adult patients admitted to hospital with COVID-19. Full-Text PDF Open AccessFrailty and mortality in patients with COVID-19Jonathan Hewitt and colleagues1 found, in a multicentre cohort study, that frailty strongly predicts mortality in patients with COVID-19. We would like to further comment on their study. Full-Text PDF Open Access
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".