Procedural Volume and Outcomes After Primary Percutaneous Coronary Intervention for ST‐Segment–Elevation Myocardial Infarction in Kerala, India: Report of the Cardiological Society of India–Kerala Primary Percutaneous Coronary Intervention Registry
Bibliographic record
Abstract
Background There are limited data to inform policy mandating primary percutaneous coronary intervention ( PPCI ) volume benchmarks for catheterization laboratories in low‐ and middle‐income countries. Methods and Results This prospective state‐wide registry included ST‐segment–elevation myocardial infarction patients with symptoms of <12 hours, or with ongoing ischemia at 12 to 24 hours, reperfused with PPCI . From June 2013 to March 2016, we recruited 5560 consecutive patients. We categorized hospitals on the basis of annual PPCI volumes into low, medium, and high volume (<100, 100–199, and ≥200 PPCIs per year, respectively). Kaplan‐Meier curves and Cox regression models were used to examine the association between PPCI volume and 1‐year mortality. Among 42 recruiting hospitals, there were 24 (57.2%) low‐volume, 8 (19%) medium‐volume, and 10 (23.8%) high‐volume hospitals. The median (25th–75th percentile) TIMI (Thrombolysis in Myocardial Infarction) ST‐segment–elevation myocardial infarction risk score was 3 (2–5). Cardiac arrest before admission occurred in 4.2%, 2.1%, and 2.9% of cases at low‐, medium‐, and high‐volume hospitals, respectively ( P =0.02). Total ischemic time differed significantly among low‐volume (median [25th–75th percentile], 3.5 [2.4–5.5] hours), medium‐volume (median, 3.8 [25th–75th percentile, 2.58–6.05] hours), and high‐volume hospitals (median, 4.16 [25th–75th percentile 2.8–6.3] hours) ( P =0.01). Vascular access was radial in 61.5%, 71.3%, and 63.2% of cases at low‐, medium‐, and high‐volume hospitals, respectively ( P =0.01). The observed 1‐year mortality rate was 6.5%, 3.4%, and 8.6% at low‐, medium‐ and high‐volume hospitals, respectively ( P <0.01), and the difference did not attenuate after multivariate adjustment (low versus medium: hazard ratio [95% CI ], 1.80 [1.12–2.90]; high versus medium: hazard ratio [95% CI ], 2.53 [1.78–3.58]) ( P <0.01). Conclusions Low‐ and middle‐income countries, like India, may have a nonlinear relationship between institutional PPCI volume and outcomes, partly driven by procedural variations and inequalities in access to care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".