Contemporary Use of Implantable Cardioverter-Defibrillators in the Era of 4-Pillar Heart Failure Therapy---an International Survey
Bibliographic record
Abstract
Background: Optimized 4-pillar guideline-directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF) has significantly altered clinical practice, with a coinciding decrease in sudden cardiac death. The continued role for implantable cardioverter-defibrillators (ICDs) in primary prevention of sudden death has recently been debated in the context of residual arrhythmic risk. This survey explored contemporary attitudes toward primary prevention ICD use in ischemic and nonischemic cardiomyopathy. Methods: An international, REDCap-based survey targeting clinicians involved in HFrEF management assessed the impact of GDMT on ICD decision-making, clinical thresholds used for implantation, and willingness to participate in randomized controlled trials. Results: = 0.003). Clinical thresholds based on left ventricular ejection fraction and New York Heart Association class were common, whereas age, renal function, and late gadolinium enhancement cut-offs were used less frequently. Willingness to randomize patients into ICD vs no-ICD trials was moderate for ischemic cardiomyopathy (38.8% for all patients, 31.8% for select patients). In nonischemic cardiomyopathy, willingness was higher, with 51.2% willing to randomize all patients and only 9.3% declining. Free-text responses emphasized individualized decision-making and the growing role of imaging and genetics. Conclusions: In the era of optimized GDMT, practice patterns regarding primary prevention ICD implantation are increasingly heterogeneous. These findings underscore the need for nuanced shared decision-making and well-designed randomized controlled studies to guide future practice.
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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 | 0.002 | 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 | 0.000 | 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) | 0.002 | 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".