Population Density Analysis of Percutaneous Coronary Intervention for ST‐Segment–Elevation Myocardial Infarction in Japan
Bibliographic record
Abstract
Background Despite recent progress in the treatment of ST‐segment–elevation myocardial infarction, data on geographic disparities application of the evidence‐based therapy remain limited. Methods and Results The J‐PCI (Japanese Percutaneous Coronary Intervention) registry is a nationwide registry to assure the quality of delivered care. Between January 2014 and December 2018, 209 521 patients underwent percutaneous coronary intervention for ST‐segment–elevation myocardial infarction in 1126 institutions. The patients were divided into tertiles according to the population density (PD) of the percutaneous coronary intervention institution location (low: <951.7/km 2 , n = 69 797; medium: 951.7–4729.7/km 2 , n = 69 750; high: ≥4729.7/km 2 , n = 69 974). Patients treated in high PD administrative districts were younger and more likely to be male. No significant correlation was observed between PD and door‐to‐balloon time (regression coefficients: 0.036 per 1000 people/km 2 ; 95% CI, −0.232 to 0.304; P = 0.79). Patients treated in low‐PD areas had higher crude in‐hospital mortality rates than those treated in high‐PD areas (low: 2.89%; medium: 2.60%; high: 2.38%; P < 0.001); PD and in‐hospital mortality had a significantly inverse association, before and after adjusting for baseline characteristics (crude odds ratio [OR], 0.983 per 1000/km 2 ; 95% CI, 0.973–0.992; P < 0.001; adjusted OR, 0.980 per 1000/km 2 ; 95% CI, 0.964–0.996; P = 0.01, respectively). Higher‐PD districts had more operators per institution (low: 6; interquartile range, 3–10; medium: 7; IQR, 3–13; high: 8; IQR, 5–13; P < 0.001), suggesting an inverse association with in‐hospital mortality (OR, 0.992; 95% CI, 0.986–0.999; P = 0.03). Conclusions Geographic inequality was observed in in‐hospital mortality of patients with ST‐segment–elevation myocardial infarction who underwent percutaneous coronary intervention. Variation in the number of operators per institution, rather than traditional quality indicators (eg, door‐to‐balloon time) might explain the difference in in‐hospital mortality.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".