Frailty as a Predictor for Mortality Among Patients With COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract BackgroundA great number of studies have explored the association between frailty and mortality among COVID-19 patients, suggesting inconsistent results. The aim of this meta-analysis was to synthesize the evidence on this issue. MethodsThree databases, including PubMed, Embase, and Cochrane Library from inception to 20th October, 2020 were conducted to search for relevant literature. The Newcastle–Ottawa Scale (NOS) was used to assess quality bias, and STATA was employed to pool the effect size. Additionally, potential publication bias and sensitivity analysis was performed.ResultsThere are 11 studies that were included, with a total of 22105 COVID-19 patients for quantitative analysis. Overall, the pooled prevalence of frailty was 51% (95%CI:42%-60%). Patients infected with COVID-19 with frailty had an increased risk of mortality, compared to those without frailty, and the pooled HR was 2.27 (95%CI:1.79-2.89). In addition, subgroup analysis based on population showed that the pooled HR for hospitalized patients and nursing home residents was 2.24 (95%CI:1.74-2.89) and 2.95 (95%CI:1.19-7.32), respectively. Subgroup analysis using the frailty assessment tool indicated that this association still existed when using the clinical frailty scale (CFS)(HR=2.41;95%CI:1.60-3.62), frailty index(HR=2.95;95%CI:1.19-7.32), hospital frailty risk score (HR=1.96;95%CI:1.79-2.15) and palliative performance scale (HR= 2.89;95%CI:1.42-5.87). ConclusionOur study indicates that frailty was an independent predictor for mortality among patients with COVID-19. Thus, frailty could be a prognostic factor for clinicians to stratify high-risk groups, and remind doctors and nurses to perform early screening and corresponding interventions urgently needed to reduce mortality rates in patients infected by SARS-CoV-2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".