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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".