Impact of South Asian Ethnicity on Long‐Term Outcomes After Coronary Artery Bypass Grafting Surgery: A Large Population‐Based Propensity Matched Study
Bibliographic record
Abstract
BACKGROUND: Ethnicity is an important predictor of coronary artery bypass graft surgery (CABG) outcomes. South Asians (SA), one of the largest ethnic groups with a high burden of cardiovascular disease, are hypothesized to have inferior outcomes after CABG compared to other ethnic groups. Given the paucity and controversy of literature in this area, the objective of this study was to examine the impact of SA versus the general population (GP) on long-term outcomes following CABG. METHOD AND RESULTS: Using administrative databases and a surname algorithm, 83 850 patients (SA: 2653, GP: 81 197) who underwent isolated CABG in Ontario, Canada from 1996 to 2007 were identified; mean follow-up was 9.1±3.9 years. SA were younger (SA: 61.7±9.4, GP: 64.1±10.0 years, standardized difference=0.25) with more cardiac risk factors, including diabetes (SA: 54.1%, GP: 34.9%, standardized difference =0.40). Propensity-score matching resulted in 2473 matched pairs between SA and GP with all baseline covariates being balanced (standardized difference <0.1). Being a SA compared to the GP was protective against freedom from major adverse cardiac and cerebrovascular events, defined by all-cause death, myocardial infarction, stroke, or coronary reintervention: Adjusted Cox-proportional hazard ratio 0.91, 95% CI (0.83-0.99), adjusted-P=0.04; this was also true for freedom from all-cause mortality: hazard ratio 0.81, 95% CI (0.72-0.91), adjusted P=0.0004. The adjusted proportion of major adverse cardiac and cerebrovascular events was lower in the SA (SA: 34.7%, GP: 37.8%, McNemar P=0.03), driven largely by all-cause mortality (SA: 20.4%, GA: 24.3%, McNemar P=0.001). CONCLUSIONS: Contrary to existing notions, our study finds that being a SA is protective with respect to freedom from long-term major adverse cardiac and cerebrovascular events and mortality after CABG. More studies are required to corroborate and explore causal factors of these findings.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".