Predictors of Sudden Cardiac Death in High-Risk Patients Following a Myocardial Infarction
Bibliographic record
Abstract
Abstract Aims To develop a risk model for sudden cardiac death (SCD) in high-risk acute myocardial infarction (AMI) survivors. Methods and results Data from the Effect of Carvedilol on Outcome After Myocardial Infarction in Patients With Left Ventricular Dysfunction trial (CAPRICORN) and the Valsartan in Acute Myocardial Infarction Trial (VALIANT) were used to create a SCD risk model (with non-SCD as a competing risk) in 13 202 patients. The risk model was validated in the Eplerenone Post-AMI Heart Failure Efficacy and Survival Study (EPHESUS). The rate of SCD was 3.3 (95% confidence interval 3.0–3.5) per 100 person-years over a median follow-up of 2.0 years. Independent predictors of SCD included age > 70 years; heart rate ≥ 70 bpm; smoking; Killip class III/IV; left ventricular ejection fraction ≤30%; atrial fibrillation; history of prior myocardial infarction, heart failure or diabetes; estimated glomerular filtration rate < 60 mL/min/1.73 m2; and no coronary reperfusion or revascularisation therapy for index AMI. The model was well calibrated and showed good discrimination (C-statistic = 0.72), including in the early period after AMI. The observed 2-year event rates increased steeply with each quintile of risk score (1.9%, 3.6%, 6.2%, 9.0%, 13.4%, respectively). Conclusion An easy to use SCD risk score developed from routinely collected clinical variables in patients with heart failure, left ventricular systolic dysfunction or both, early after AMI was superior to left ventricular ejection fraction. This score might be useful in identifying patients for future trials testing treatments to prevent SCD early after AMI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".