Impact of Body Mass Index on COVID-19-Related In-Hospital Outcomes and Mortality
Bibliographic record
Abstract
Background: Given the high prevalence of obesity around the globe, patients with coronavirus disease 2019 (COVID-19) are at an increased risk of devastating complications. Methods: A retrospective cohort study was performed to determine the association of basal metabolic index (body mass index (BMI)) with the need for invasive mechanical ventilation (IMV), dialysis, upgrade to an intensive care unit (ICU) and mortality. Independent t -test and multivariate logistic regression analysis were performed to calculate mean differences and adjusted odds ratios (aORs) with its 95% confidence interval (CI), respectively. Results: A total of 176 consecutive patients with confirmed COVID-19 diagnosis were included. The mean age was 62.2 years, with 51% being male patients. The mean BMI for non-surviving patients was significantly higher compared to patients surviving on the seventh day of hospitalization (35 vs. 30 kg/m 2 , P = 0.022). Similarly, patients requiring IMV had a higher BMI (33 vs. 29, P = 0.002) compared to non-intubated patients. The unadjusted OR for patients with a higher BMI requiring IMV (56% vs. 28%, OR: 3.3, 95% CI: 1.6 - 7.0, P = 0.002) and upgrade to ICU (46% vs. 28%, OR; 2.2, 1.07 - 4.6, P = 0.04) were significantly higher compared to patients with a lower BMI. Similarly, patients with a higher BMI had higher in-hospital mortality (21% vs. 9%, OR: 3.2, 95% CI: 1.3 - 8.2, P = 0.01) compared to patients with a normal BMI. Despite a numerical advantage in the lower BMI group, there was no significant difference between the two groups in terms of the need for dialysis (5% vs. 13%, OR: 3.8, 13% vs. 4%, 1.1 - 14.1, P = 0.07). aORs controlled for baseline comorbidities and medications mirrored the overall results, except for the need to upgrade to ICU. Conclusions: In patients with confirmed COVID-19, morbid obesity serves as an independent risk factor of high in-hospital mortality and the need for IMV. J Clin Med Res. 2021;13(4):230-236 doi: https://doi.org/10.14740/jocmr4239
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".