Risk of Appropriate Therapy and Death Before Therapy After Implantable Cardioverter-Defibrillator Generator Replacement
Bibliographic record
Abstract
Background The decision to initially implant an implantable cardioverter-defibrillator (ICD) is informed by robust randomized controlled trials, but no such data exist to guide the decision to replace an ICD generator. In this study, we aimed to determine outcomes after ICD generator replacement. Methods All patients with ischemic or nonischemic cardiomyopathy who underwent ICD generator replacement from 2001 to 2011 at Mayo Clinic, MN, or Beth Israel Deaconess Medical Center, MA, were included. Outcomes included (1) appropriate therapy after generator replacement and (2) death before appropriate therapy after generator replacement. Cox proportional hazards modeling was used to determine the associations between patient characteristics and outcomes. Results In 1421 patients undergoing ICD generator replacement (mean±SD age 69.6±12.1 years, 81% male), appropriate therapy occurred after replacement in 435 patients (30.6%) over a mean follow-up of 2.7±2.6 years. Associated factors included lower left ventricular ejection fraction and history of appropriate therapy before generator replacement. Death before appropriate ICD therapy occurred in 336 (23.7%) patients. Older age, lower left ventricular ejection fraction, and noncardiac comorbidities, including diabetes mellitus, chronic lung disease, peripheral vascular disease, lower hemoglobin, and lower glomerular filtration rate, were associated with greater risk of death before appropriate therapy. A progressive increase in mortality was observed with aggregation of these noncardiac comorbidities. Conclusions The decision to replace the ICD should take into consideration not only left ventricular ejection fraction and history of ventricular arrhythmias, but also comorbid illnesses that may impact the duration and the quality of life.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".