BMI and pneumonia outcomes in critically ill COVID‐19 patients: An international multicenter study
Bibliographic record
Abstract
Abstract Objective Previous studies have unveiled a relationship between the severity of coronavirus disease 2019 (COVID‐19) pneumonia and obesity. The aims of this multicenter retrospective cohort study were to disentangle the association of BMI and associated metabolic risk factors (diabetes, hypertension, hyperlipidemia, and current smoking status) in critically ill patients with COVID‐19. Methods Patients admitted to intensive care units for COVID‐19 in 21 centers (in Europe, Israel, and the United States) were enrolled in this study between February 19, 2020, and May 19, 2020. Primary and secondary outcomes were the need for invasive mechanical ventilation (IMV) and 28‐day mortality, respectively. Results A total of 1,461 patients were enrolled; the median (interquartile range) age was 64 years (40.9‐72.0); 73.2% of patients were male; the median BMI was 28.1 kg/m2 (25.4‐32.3); a total of 1,080 patients (73.9%) required IMV; and the 28‐day mortality estimate was 36.1% (95% CI: 33.0‐39.5). An adjusted mixed logistic regression model showed a significant linear relationship between BMI and IMV: odds ratio = 1.27 (95% CI: 1.12‐1.45) per 5 kg/m2. An adjusted Cox proportional hazards regression model showed a significant association between BMI and mortality, which was increased only in obesity class III (≥40; hazard ratio = 1.68 [95% CI: 1.06‐2.64]). Conclusions In critically ill COVID‐19 patients, a linear association between BMI and the need for IMV, independent of other metabolic risk factors, and a nonlinear association between BMI and mortality risk were observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".