Clinical characteristics and cardiovascular outcomes among young patients with acute myocardial infarction in Kerala, India: A secondary analysis of ACS QUIK trial
Bibliographic record
Abstract
Background Limited data exist on the risk profile and outcomes among young patients with acute myocardial infarction(AMI) in low-and middle-income countries(LMICs). This study explored differences in the clinical characteristics, medical care, and outcomes of AMI in young adults in India with a subanalysis focusing on sex disparities amongst the young. Methods Using the Acute Coronary Syndrome Quality Improvement in Kerala trial database, we compared baseline characteristics, management, and outcomes amongst the young patients(≤50 years) and their older counterparts. The primary outcomes were the rates of in-hospital and 30-day composite of in-hospital major adverse cardiovascular events(MACE). Results Of the 21,374 adults enrolled, 4762(22%) were young, of which 614 (12.9%) were females. Young patients with AMI were more likely to be smokers(41.9% vs. 27.8%; P < 0.001) and undergo coronary angiography (66.3%vs.57.3%; P < 0.001) and percutaneous coronary intervention (PCI)(57.5% vs. 47.0%; P < 0.001), compared to older patients. After adjustment for potential confounders, younger patients had a lower likelihood of in-hospital (RR = 0.49; 95%CI 0.40–0.61; P < 0.001) and 30-day MACE (RR = 0.54; 95%CI 0.46–0.64; P < 0.001). Subgroup analysis comparing young males and females revealed worse cardiovascular risk profile among young women except for smoking. In-hospital MACE(RR = 1.60; 95%CI, 1.0–2.45; P = 0.048) were higher for young women compared to men. Conclusion Young AMI patients had higher prevalence of modifiable risk factors, were more likely to receive reperfusion therapy, and had better short and intermediate outcomes, compared to older patients. Compared to young men with AMI, young women had worse cardiovascular risk profile, were less likely to be treated with diagnostic angiography or PCI and experienced higher in-hospital death and MACE.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".