Clinical Risk Stratification for Primary Prevention Implantable Cardioverter Defibrillators
Bibliographic record
Abstract
BACKGROUND: A conceptualized model may be useful for understanding risk stratification of primary prevention implantable cardioverter defibrillators considering the competing risks of appropriate implantable cardioverter defibrillator shock versus mortality. METHODS AND RESULTS: In a prospective, multicenter, population-based cohort with left ventricular ejection fraction ≤35% referred for primary prevention implantable cardioverter defibrillator, we developed dual risk stratification models to determine the competing risks of appropriate defibrillator shock versus mortality using a Fine-Gray subdistribution hazard model. Among 7020 patients referred, 3445 underwent defibrillator implant (79.7% men, median, 66 years [25th, 75th: 58-73]). During 5918 person-years of follow-up, appropriate shock occurred in 204 patients (3.6 shocks/100 person-years) and 292 died (4.9 deaths/100 person-years). Competing risk predictors of appropriate shock included nonsustained ventricular tachycardia, atrial fibrillation, serum creatinine concentration, digoxin or amiodarone use, and QRS duration near 130-ms peak. One-year cumulative incidence of appropriate shock was 0.9% in the lowest risk category, and 1.7%, 2.5%, 4.9%, and 9.3% in low, intermediate, high, and highest risk groups, respectively. Hazard ratios for appropriate shock ranged from 4.04 to 7.79 in the highest 3 deciles (all P≤0.001 versus lowest risk). Cumulative incidence of 1-year death was 0.6%, 1.9%, 3.3%, 6.2%, and 17.7% in lowest, low, intermediate, high, and highest risk groups, respectively. Mortality hazard ratios ranged from 11.48 to 36.22 in the highest 3 deciles (all P<0.001 versus lowest risk). CONCLUSIONS: Simultaneous estimation of risks of appropriate shock and mortality can be performed using clinical variables, providing a potential framework for identification of patients who are unlikely to benefit from prophylactic implantable cardioverter defibrillator.
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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 | 0.005 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".