Abstract 18244: The Association Between South Asian Ethnicity and Long-term Survival Among Patients Undergoing Coronary Artery Bypass Grafting
Bibliographic record
Abstract
Introduction: South Asians (SA) have a high burden of coronary artery disease (CAD) often requiring coronary artery bypass grafting (CABG). However, CABG outcomes in this population have not been explored. The objective of this study was to assess the survival of patients of SA descent undergoing CABG in a large, well described Canadian cohort. Methods: All patients who underwent CABG between 1996 and 2010 were identified from the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease registry. Ethnicity was determined using the Nam Pehchan surname software. Differences in long-term survival were compared between SA and European (EC) patients using adjusted and then unadjusted cox proportional hazards models. SA subjects were then matched 1:1 with EC counterparts using a non-parsimonious propensity model to balance groups; models were then re-run with the matched cohort. Results: Of the 20608 patients undergoing CABG, 608 were SA. SA patients were younger (63.8 vs 65.8 years) and had a higher prevalence of diabetes (39.5 vs 27.7%), but a lower prevalence of smoking (13.3 vs 25.3%) than EC patients (all p<0.001). There were 5382 deaths long-term with a median follow-up of 7.2 years. Ninety-one deaths occurred in SA patients, 5291 in the EC patients. Kaplan-Meier survival analysis demonstrated a lower incidence of death in the SA patients (log rank, p<0.0001) (Figure). In the unadjusted Cox PH model, SA ethnicity was associated with reduced mortality (Hazard Ratio 0.53, 95% CI 0.43, 0.66), an association that persisted in the adjusted model (HR 0.63, 95%CI 0.51, 0.78). All SA subjects were matched to EC counterparts. In the matched cohort SA ethnicity remained associated with reduced mortality (HR 0.68, 95%CI 0.52, 0.90). Conclusion: In a large prospective Canadian registry, SA undergoing CABG appear to have improved long-term survival compared to EC patients. The reasons behind this striking difference in mortality deserve further exploration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".