A Model for Predicting Mortality in Acute ST-Segment Elevation Myocardial Infarction Treated With Primary Percutaneous Coronary Intervention
Bibliographic record
Abstract
Background— Accurate models to predict mortality are needed for risk stratification in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI). Methods and Results— We examined 5745 patients with STEMI undergoing primary PCI in the Assessment of Pexelizumab in Acute Myocardial Infarction Trial within 6 hours of symptom onset. A Cox proportional hazards model incorporating regression splines to accommodate nonlinearity in the log hazard ratio (HR) scale was used to determine baseline independent predictors of 90-day mortality. At 90 days, 271 (4.7%) of 5745 patients died. Independent correlates of 90-day mortality were (in descending order of statistical significance) age (HR, 2.03/10-y increments; 95% CI, 1.80 to 2.29), systolic blood pressure (HR, 0.86/10-mm Hg increments; 95% CI, 0.82 to 0.90), Killip class (class 3 or 4 versus 1 or 2) (HR, 4.24; 95% CI, 2.97 to 6.08), heart rate (>70 beats per minute) (HR, 1.45/10-beat increments; 95% CI, 1.31 to 1.59), creatinine (HR, 1.23/10-μmol/L increments >90 μmol/L; 95% CI, 1.13 to 1.34), sum of ST-segment deviations (HR, 1.25/10-mm increments; 95% CI, 1.11 to 1.40), and anterior STEMI location (HR, 1.47; 95% CI, 1.12 to 1.93) (c-index, 0.82). Internal validation with bootstrapping confirmed minimal overoptimism (c-index, 0.81). Conclusions— Our study provides a practical method to assess intermediate-term prognosis of patients with STEMI undergoing primary PCI, using baseline clinical and ECG variables. This model identifies key factors affecting prognosis and enables quantitative risk stratification that may be helpful in guiding clinical care and for risk adjustment for observational analyses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".