Association of Frailty With Clinical Outcomes in Patients Receiving Primary Prevention Implantable Cardioverter Defibrillators: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Frailty predisposes individuals to morbidity and mortality. Increasing numbers of elderly and comorbid individuals are undergoing primary prevention implantable cardioverter defibrillator (ICD) device placement. Little is known about the association of frailty with post-device implantation outcomes. METHODS: We conducted a single-center, prospective cohort study of 71 patients who underwent primary prevention ICD insertion and who had their baseline frailty status assessed using the Fried index. Participants were followed for a median period of 7.8 years. RESULTS: The mean age (± SD) was 70.6 ± 4.5 years. 12 (17%) patients met the criteria for frailty. 23 (33%) patients received cardiac resynchronization therapy. Frailty was associated with a significantly higher incidence of mortality (HR [95% CI]; 3.9 [1.2-12.1]), ED visits (2.7 [1.1-6.7]), and hospitalizations (2.8 [1.1-7.6]). Within the non-frail cohort, there was no association between Fried frailty scores and adverse outcomes. None of the frail patients received appropriate shock therapy. CONCLUSION: Among primary prevention ICD recipients, frailty is associated with worse mortality and morbidity. Clinicians should consider frailty when discussing risks and benefits with this patient population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".