A Prediction Model for Sudden Cardiac Death in Patients with Heart Failure and Preserved Ejection Fraction
Bibliographic record
Abstract
AIMS: Sudden cardiac death (SCD) accounts for ∼ 25% of all deaths in heart failure with preserved ejection fraction (HFpEF). However, strategies to identify HFpEF patients at a higher risk of SCD have not been developed. METHODS AND RESULTS: We studied 4128 patients with HFpEF enrolled in the Irbesartan in Patients with Heart Failure and Preserved Ejection Fraction (I-PRESERVE) trial. All SCDs were adjudicated by a clinical endpoint committee. Cumulative incidences of SCD were estimated counting other deaths as competing risks. Cox regression analysis was used to generate a risk model for SCD. During a mean follow-up of 4.1 years, 231 (5.6%) patients died suddenly and 650 (15.7%) died non-suddenly. A multivariable model in 3480 patients including age, gender, history of diabetes and myocardial infarction, LBBB on ECG, and the natural logarithm of NT-proBNP identified a subgroup of 837 (24%) patients with ≥10% cumulative incidence of SCD over 5 years, accounting for other deaths as competing risk (Harrell's C index 0.75). The 5-year cumulative incidences of SCD in the higher and lower risk groups were 11% and 4%, respectively. In the higher risk group, 32% of deaths were SCD compared with 26% in the entire I-PRESERVE cohort. CONCLUSIONS: A multivariable prediction model identified patients with HFpEF who have a ≥10% risk of SCD over 5 years, similar to the risk of SCD in the Sudden Cardiac Death in Heart Failure (SCD-Heft) trial. This model may be useful for selecting patients with HFpEF for SCD prevention trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".