Abstract 11595: Predicting Appropriate Shocks In Patients With Heart Failure: Patient Level Meta-analysis From SCD-HeFT and Madit II
Bibliographic record
Abstract
Background: Some patients implanted with a primary prevention implantable cardioverter defibrillator (ICD) due to heart failure receive an appropriate shock, but no precise tools exist to predict this outcome. Methods: Using patient level data from the Multicenter Automatic Defibrillator Implantation Trial II (MADIT II) and the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT), we identified patients with any appropriate ICD shock. A variety of clinical and demographic variables were included in a logistic regression model to predict appropriate ICD shocks (Table). Results: There were 1,463 patients randomized to ICD from the two included trials; 285 (19%) had ≥1 appropriate shock over a median follow up of 2.59 years. Compared with patients with no appropriate shocks, patients who received any appropriate ICD shock tended to have NYHA class II or III heart failure symptoms, beta blocker therapy, lower LVEF, wider QRS duration, and a single versus dual chamber ICD (79% of all patients had a single chamber ICD). Other comorbidities were similar between groups. Significant independent predictors of appropriate ICD shocks included NYHA class (NYHA II vs I: OR 1.65, 95% CI 1.07-2.55; NYHA III vs I: OR 1.74, 95% CI 1.10-2.76), LVEF based on one unit change (higher vs lower LVEF: OR 0.96, 95% CI 0.94-0.98), beta blocker therapy (presence vs absence: OR 0.96, 95% CI 0.94-0.98), and single chamber ICD (OR 1.67, 95% CI 1.13-2.45). Conclusion: In this meta-analysis of patient level data from MADIT-II and SCD-HeFT, appropriate ICD shocks were significantly predicted by NYHA class, LVEF, beta blocker therapy, and single chamber ICD. There is a compelling need for a large, prospective study to better define the risk of appropriate ICD shocks in patients meeting criteria for a primary prevention ICD. Until such time, stratification of patients by these factors may better define risk for potentially mortal rhythm events that might be prevented by a primary prevention ICD.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".