Incidence and Predictors of Implantable Cardioverter-defibrillator Therapies After Generator Replacement—A Pooled Analysis of 31,640 Patients’ Data
Bibliographic record
Abstract
Among primary prevention implantable cardioverter-defibrillator (ICD) recipients, 75% do not experience any appropriate ICD therapies during their lifetime, and nearly 25% have improvements in their left ventricular ejection fraction (LVEF) during the lifespan of their first generator. The practice guidelines concerning this subgroup's clinical need for generator replacement (GR) remain unclear. We conducted a proportional meta-analysis to determine the incidence and predictors of ICD therapies after GR and compared this to the immediate and long-term complications. A systematic review of existing literature on ICD GR was performed. Selected studies were critically appraised using the Newcastle-Ottawa scale. Outcomes data were analyzed by random-effects modeling using R (R Foundation for Statistical Computing, Vienna, Austria), and covariate analyses were conducted using the restricted maximum likelihood function. A total of 31,640 patients across 20 studies were included in the meta-analysis with a median (range) follow-up of 2.9 (1.2-8.1) years. The incidences of total therapies, appropriate shocks, and anti-tachycardia pacing post-GR were approximately 8, 4, and 5 per 100 patient-years, respectively, corresponding to 22%, 12%, and 12% of patients of the total cohort, with a high level of heterogeneity across the studies. Greater anti-arrhythmic drug use and previous shocks were associated with ICD therapies post-GR. The all-cause mortality was approximately 6 per 100 patient-years, corresponding to 17% of the cohort. Diabetes mellitus, atrial fibrillation, ischemic cardiomyopathy, and the use of digoxin were predictors of all-cause mortality in the univariate analysis; however, none of these were found to be significant predictors in the multivariate analysis. The incidences of inappropriate shocks and other procedural complications were 2 and 2 per 100 patient-years, respectively, which corresponded to 6% and 4% of the entire cohort. Patients undergoing ICD GR continue to require therapy in a significant proportion of cases without any correlation with an improvement in LVEF. Further prospective studies are necessary to risk-stratify ICD patients undergoing GR.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.045 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".