Abstract 15961: Multicenter Prospective Prevention of Sudden Death in High Risk Patients Utilizing Enhanced ACC/AHA Risk Model
Bibliographic record
Abstract
Introduction: Strategies for reliable selection of high-risk hypertrophic cardiomyopathy (HCM) patients for prevention of sudden cardiac death (SCD) with implantable cardioverter-defibrillators (ICDs) continue to be debated. Objective: Assess the sensitivity of sudden death risk strategies in predicting SCD events (appropriate ICD shocks, sudden death or out of hospital cardiac arrest) among a large multicenter cohort of high-risk HCM patients. Methods: Observational longitudinal study from 6-HCM centers in North America and Europe to determine outcomes in consecutive HCM patients considered high risk for sudden death based on an enhanced ACC/AHA (U.S./Canada) guidelines-based risk factor algorithm with primary prevention ICD placement. ESC risk score was retrospectively calculated in this cohort and compared to ACC/AHA risk factor method for predicting SCD events. Results: Of 1185 patients with primary prevention ICDs implanted based on ≥ 1 major risk marker, 162 (14%) experienced device therapy terminating VT/VF episodes at 49 ± 18 years of age and 4.6 ± 4.2 years after device implant. Within the 6 HCM centers, only 28 other patients not implanted with ICD died suddenly or had resuscitated cardiac arrests, including 19 (68%) with risk-markers who declined ICDs. Of these 190 high risk patients with SCD or SCD events, 67 (35%) had ESC risk-scores scores ≥6%/5-years, considered sufficient to recommend a prophylactic ICD, while 83 (44%) had low risk scores (<4%/5-years) that likely would have excluded an ICD recommendation. Compared to enhanced ACC/AHA risk factors, the ESC risk-score was less sensitive than ACC/AHA (35% vs. 95%, p<0.01), consistent with identifying fewer high-risk patients with events. Conclusion: In this large multicenter study of high-risk HCM patients, an enhanced ACC/AHA risk factor strategy was superior to the ESC risk score in identifying patients at greatest risk for SCD events.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".