The Relationship Between Body Mass Index and In-Hospital Mortality in Patients Following Coronary Artery Bypass Grafting Surgery
Bibliographic record
Abstract
Background: The association between Body Mass Index (BMI) and clinical outcomes following coronary artery bypass grafting (CABG) remains controversial. Our objective was to investigate the real-world relationship between BMI and in-hospital clinical course and mortality, in patients who underwent CABG. Methods: A sampled cohort of patients who underwent CABG between October 2015 and December 2016 was identified in the National Inpatient Sample (NIS) database. Outcomes of interest included in-hospital mortality, peri-procedural complications and length of stay. Patients were divided into 6 BMI (kg/m 2 ) subgroups; (1) under-weight ≤19, (2) normal-weight 20–25, (3) over-weight 26–30, (4) obese I 31–35, (5) obese II 36–39, and (6) extremely obese ≥40. Multivariable logistic regression model was used to identify predictors of in-hospital mortality. Linear regression model was used to identify predictors of length of stay (LOS). Results: An estimated total of 48,710 hospitalizations for CABG across the U.S. were analyzed. The crude data showed a U-shaped relationship between BMI and study population outcomes with higher mortality and longer LOS in patients with BMI ≤ 19 kg/m 2 and in patients with BMI ≥40 kg/m 2 compared to patients with BMI 20–39 kg/m 2 . In the multivariable regression model, BMI subgroups of ≤19 kg/m 2 and ≥40 kg/m 2 were found to be independent predictors of mortality. Conclusions: A complex, U-shaped relationship between BMI and mortality was documented, confirming the “obesity paradox” in the real-world setting, in patients hospitalized for CABG.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".