Relationship Between Body Mass Index and Outcomes in Acute Myocardial Infarction
Bibliographic record
Abstract
Background: The prevalence of obesity in the United States is high. Obesity is one of the leading risk factors in the development of acute myocardial infarction (AMI). Nevertheless, how obesity impacts AMI in-hospital outcomes remains controversial. Methods: Using National Inpatient Sample (NIS) database, we identified patients diagnosed with AMI from the year 2015 to 2018. We divided these patients into five subgroups based on their body mass index (BMI). We compared outcomes such as mortality, length of inpatient stay, and inpatient complications between our subgroups. Statistical analysis was done using the program STATA. Our nationally representative analysis included 561,535 patients who had an AMI event across various weight classes. Results: Most of our sample was obese (BMI > 30 kg/m 2 ) and male. Obese patients were significantly younger than the rest. Length of stay (LOS) for AMI was highest for those with a BMI of less than 24 kg/m 2 . In-hospital mortality is highest for those with a BMI of < 30 kg/m 2 and lowest for those with a BMI of 30 - 40 kg/m 2 . Inpatient complications are highest in the lower BMI population (BMI < 24 kg/m 2 ). Conclusion: The current analysis of a nationally representative sample showed the clinical implications of BMI in patients with AMI. Patients with a BMI of 30 - 40 kg/m 2 had more favorable LOS, inpatient complications, and in-hospital mortality when compared to those with an ideal body weight. Hence, this supports and expands on the concept of the “obesity paradox”. Further studies are needed to further investigate the possible mechanism behind this. J Clin Med Res. 2022;14(11):458-465 doi: https://doi.org/10.14740/jocmr4818
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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 | 0.006 |
| 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) | 0.002 | 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".