The Impact of Frailty on COVID-19 Outcomes: A Systematic Review and Meta-analysis of 16 Cohort Studies
Bibliographic record
Abstract
BACKGROUND: Frail patients are increasingly vulnerable to stress, which is mainly manifested by a reduced physiologic reserve in metabolic and immune systems and neuromuscular system. Several studies found a significant association of frailty with COVID-19 severity to support the evidence for the application of frailty assessment. However, there were contradictory results in other studies. Thus we conducted a systematic review and meta-analysis to synthesize the current studies to investigate impact of frailty on COVID-19 outcomes and provide evidence-based decisions in clinical practice. OBJECTIVE: We aimed to synthesize the current studies to investigate impact of frailty on COVID-19 outcomes and provide evidence-based decisions in clinical practice. DESIGN: A systematic review and Meta-analysis of 16 cohort studies. PARTICIPANTS: Patients with COVID-19. METHODS: A systematic retrieving for potential literature was conducted in several public electronic databases, including Medline(OvidSP), EMBASE, Pubmed and Chinese databases(China National Knowledge Infrastructure,Wanfang and Weipu) on August 1, 2020.The literature research was updated on October 26, 2020. Newcastle Ottawa Scale for cohort studies was used for quality assessment. RevMan (Version 5.3) and Stata 14.0 were used to synthesize the pooled effects. RESULTS: According to the predefined inclusion and exclusion criteria, sixteen studies of 4324 patients were included in the final analysis. Frailty was significantly associated with increased risk of all-cause mortality among patients with COVID-19, with pooled adjusted odds ratios of 1.81 (95% confidence intervals:1.48,2.21, I2=87.0%, P<0.001). The result was consistent in stratified analysis to according to age, patient source, definitions of frailty, study quality, and adjustment method. Frailty was significant associated with an increased risk of COVID-19 severity, admission to intensive care unit, application of invasive mechanical ventilation, long-length stay. CONCLUSIONS: In this meta-analysis, we found frailty was significantly associated with an increased risk of clinical adverse events (all- cause mortality, COVID-19 severity, admission to the intensive care unit, application of invasive mechanical ventilation, long-length stay). Given the epidemic of COVID-19 and shortage of medical resources, paying more attention to screening frailty would contribute to disease management and resource allocation among patients with COVID-19.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".