The Effect of Shock Burden on Heart Failure and Mortality
Bibliographic record
Abstract
Background Prior studies have demonstrated an association between appropriate implantable cardioverter defibrillator (ICD) shocks and mortality in clinical trials. The effect of shock burden on heart failure and mortality has not been previously studied in a large population-based cohort. Methods The cohort was derived using a comprehensive prospective ICD registry in the province of Nova Scotia with a mean follow-up of 4 ± 2.3 years. With the use of time-varying analysis, the relationship among shock burden, mortality, and heart failure hospitalization was determined. Results A total of 776 patients (mean age of 64.8 years) were included in the study, of whom 37% received appropriate therapy during follow-up. A single ICD shock did not confer an increased mortality risk compared with no therapy (hazard ratio [HR], 1.23; 95% confidence interval [CI], 0.84-1.79; P = 0.3), but mortality risk was significantly increased with ≥ 2 shocks (HR, 3.23; 95% CI, 2.04-5.09; P < 0.0001). There was a significant increase in heart failure hospitalization associated with receiving 1 ICD shock (HR, 2.05; 95% CI, 1.46-2.89; P < 0.0001) or more than 1 ICD shock (HR, 4.36; CI, 2.53-7.52; P < 0.0001) compared with patients receiving no ICD therapy. Patients who received antitachycardia pacing alone showed no difference in heart failure hospitalization (HR, 0.93; CI, 0.67-1.29; P = 0.7) and improved survival (HR, 0.69; CI, 0.5-0.96; P = 0.03) compared with those receiving no ICD therapy. Conclusion Ventricular arrhythmia treated with appropriate ICD shocks is associated with an increased risk of heart failure hospitalization, whereas recurrent episodes of ventricular arrhythmia requiring shocks are associated with both higher mortality and higher heart failure hospitalization rates.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".