The Relationship Between Body Mass Index and In-Hospital Mortality in the Contemporary Era of an Acute Myocardial Infarction Management
Bibliographic record
Abstract
Background: The association between body mass index (BMI) and clinical outcomes following an acute myocardial infarction (AMI) remains controversial. Our objective was to investigate the relationship between BMI and AMI presentation, in-hospital clinical course and mortality in the contemporary era of AMI management. Methods: Patients, hospitalized for an AMI between October 2015 and December 2016, were identified in the National Inpatient Sample (NIS) database. Socio-demographic and clinical data, including BMI, were collected and outcomes, including length of stay and mortality, were analyzed. Patients were divided into 6 BMI (kg/m 2 ) subgroups; under-weight (≤ 19), normal-weight (20– 25), over-weight (26– 30), obese I (31– 35), obese II (36– 39) and 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 125,405 hospitalizations for an AMI across the US were analyzed. Compared to the other BMI subgroups, the under-weight, normal-weight and extremely obese groups presented with a non-ST segment elevation AMI (NSTEMI) more frequently and were less likely to undergo coronary revascularization. The data show a J-shaped relationship between BMI and study outcomes with lower mortality in patients with BMI over 25 compared to normal- and low-weight patients. In the multivariate regression model, BMI group was found to be an independent predictor of mortality. Conclusion: J-shaped relationship between BMI and mortality was documented in patients hospitalized for an AMI in the recent years. These findings confirm that the “obesity paradox” persists during the contemporary era of an AMI management. Keywords: body mass index, BMI, acute myocardial infarction, obesity paradox
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".