Frailty and mortality associations in patients with COVID‐19: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Observational data during the pandemic have demonstrated mixed associations between frailty and mortality. AIM: To examine associations between frailty and short-term mortality in patients hospitalised with coronavirus disease 2019 (COVID-19). METHODS: In this systematic review and meta-analysis, we searched PubMed, Embase and the COVID-19 living systematic review from 1 December 2019 to 15 July 2021. Studies reporting mortality and frailty scores in hospitalised patients with COVID-19 (age ≥18 years) were included. Data on patient demographics, short-term mortality (in hospital or within 30 days), intensive care unit (ICU) admission and need for invasive mechanical ventilation (IMV) were extracted. The quality of studies was assessed using the Newcastle-Ottawa Scale. RESULTS: Twenty-five studies reporting 34 628 patients were included. Overall, 26.2% (n = 9061) died. Patients who died were older (76.7 ± 9.6 vs 69.2 ± 13.4), more likely male (risk ratio (RR) = 1.08; 95% confidence interval (CI): 1.06-1.11) and had more comorbidities. Fifty-eight percent of patients were frail. Adjusting for age, there was no difference in short-term mortality between frail and non-frail patients (RR = 1.04; 95% CI: 0.84-1.28). The non-frail patients were commonly admitted to ICU (27.2% (4256/15639) vs 29.1% (3567/12274); P = 0.011) and had a higher mortality risk (RR = 1.63; 95% CI: 1.30-2.03) than frail patients. Among patients receiving IMV, there was no difference in mortality between frail and non-frail (RR = 1.62; 95% CI 0.93-2.77). CONCLUSION: This systematic review did not demonstrate an independent association between frailty status and short-term mortality in patients with COVID-19. Patients with frailty were less commonly admitted to ICU and non-frail patients were more likely to receive IMV and had higher mortality risk. This finding may be related to allocation decisions for patients with frailty amidst the pandemic.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".