Abstract 12086: Predicting Long-Term Survival After De Novo Cardioverter-Defibrillator Implantation for Primary Prevention
Bibliographic record
Abstract
Introduction: Current guidelines recommend the use of implantable cardioverter-defibrillators (ICDs) for primary prevention of arrhythmia in patients with reduced ejection fraction cardiomyopathy. Little is known about the long-term outcomes of patients with ICDs in clinical practice. Objective: To develop a risk prediction model for 10-year survival after ICD implantation for primary prevention. Methods: In this population-based registry of all ICD patients across 18 centers in Ontario, CA, 5097 patients receiving ICD implant for primary prevention from February 2007 to March 2011 were followed for up to 10 years. Patients were randomly split 2:1 into derivation and internal validation cohorts to develop and validate a prognostic model using Cox regression and predictors measured at initial ICD evaluation. Results: Mean age was 65.3 years (SD 11.0), 664 patients were female (19.5%) and 2344 patients (69.0%) had ischemic cardiomyopathy for primary disease indication. 10-year survival was 45.7% (95% CI 44.0%-47.4%).The final prediction model included age, sex, disease indication, comorbidities and biomarkers at the time of ICD assessment ( Table 1 ). This model had good discrimination in derivation (AUC 0.80; 95% CI 0.78-0.81) and validation samples (0.79, 95% CI 0.77-0.81) and good calibration. Sensitivity analyses showed that the addition of device type, provider factors (implantation site, implanter volume, physician main specialty), sex-related interaction terms, and frailty scores did not significantly improve the prediction model. Conclusion: A combination of demographic and patient factors determined at baseline device evaluation enabled the prediction of 10-year survival in patients undergoing ICD for primary prevention in the clinical practice. Our findings may help identify and monitor individuals at risk of long-term mortality and may be useful in targeting future prevention strategies to enhance longevity in this high-risk population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".