Prognostic impact of obesity in cancer patients with COVID-19 infection: A systematic review and meta-analysis.
Bibliographic record
Abstract
e18578 Background: Obesity is a bona fide risk factor for ICU admission, mechanical ventilation, and mortality in patients (pts) with COVID-19 in the general population. However, whether obesity is a risk factor in cancer pts remains unknown. Herein, we have conducted a systematic review/meta-analysis of obesity and all-cause mortality in cancer pts with COVID-19. Methods: Following PRISMA guidelines,a systematic search of PubMed and Embase as well as major conference proceedings (ASCO/ESMO/AACR) was conducted for publications from inception to 14 January 2020. Observational studies that reported all-cause mortality in cancer pts with lab confirmation or clinical diagnosis of COVID-19 and BMI (obese (>30 kg/m2) vs. non-obese) were included in the analysis. The pooled odds ratio (OR) and 95% confidence interval (CI) were calculated with the fixed-effects model based on low heterogeneity. Small sample publication bias was evaluated using the Begg’s Funnel Plot and Egger’s test. Results: After reviewing 3387 studies,3 retrospective cohort studies of 419 obese and 1694 non-obese cancer pts (N=2117) with COVID-19 in both inpatient/outpatient settings that reported outcomes based on obesity were found. The 3 studies were conducted multi-nationally in North America, in France, and in the Netherlands respectively. The median ages of the cohorts ranged 66-68. All studies included various cancers of various stages and were of high quality per Newcastle Ottawa scale (scores 7-9). Fixed effects meta-analysis showed no association between obesity and all-cause mortality (OR 0.95, 95% CI 0.74-1.23) in cancer pts with COVID-19. Heterogeneity was low (I2 = 33%). No significant funnel plot asymmetry was detected per Egger’s test (P=0.2273). The reported OR of each study is outlined in the table. Conclusions: In contrast to the general population, our analysis reveals that obesity is not associated with increased all-cause mortality in cancer pts with COVID-19. Limitations of this study include a limited number of included studies, reliance on retrospective studies, non-use of ethnicity-specific WHO BMI criteria, and limited granularity of the study-reported BMI. Future prospective studies are warranted to assess the complex interplay among anthropomorphic measures, cachexia/sarcopenia, comorbidities associated with the metabolic syndrome, and COVID-19 outcomes in the cancer pt population.[Table: see text]
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".