Implantable cardiac defibrillator outcomes in octogenarians
Bibliographic record
Abstract
Abstract Background Implantable cardiac defibrillators (ICDs) are essential for preventing sudden cardiac death. Despite inclusion in national guidelines, older adults are often underrepresented in trials. Evaluating ICD use in the aging population, particularly with advancements in heart failure treatment, is crucial. Objective This study examines outcomes in octogenarians undergoing ICD implantation for primary and secondary prevention, focusing on ICD therapies and mortality timing. Methods A retrospective observational study at a single Canadian academic center included patients ≥80 years old at ICD implantation, excluding those with <30 days follow‐up. Data on demographics, comorbidities, mortality, and ICD therapies were collected from electronic medical records. Clinical frailty was assessed using the Dalhousie Clinical Frailty Scale. Results We identified 143 patients (mean age 82.6 ± 2.2 years, 14% female) from May 2015 to October 2023. ICDs were implanted for primary prevention in 63 patients (44%) and secondary prevention in 80 patients (56%). Thirty‐seven patients were excluded due to insufficient follow‐up. ICD therapies occurred in 30 patients (25%) through anti‐tachycardia pacing (ATP) and in 19 patients (18%) through shocks. The mean time to first ATP was 16.9 ± 21.0 months, and to first shock, 21.2 ± 23.6 months. Among 66 patients with mortality data, 19 (24%) died at 31.3 ± 30.4 months. Patients with non‐ischemic cardiomyopathy experienced earlier shocks (7.7 vs. 32.2 months, p < 0.05). Conclusion Elderly patients undergoing ICD implantation have multiple comorbidities and competing causes of mortality. Device use is overall infrequent but occurs well before observed mortality. Prospective clinical trials are needed to determine ICD benefits in this age cohort.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".