Arrhythmic Risk Following Recovery of Left Ventricular Ejection Fraction in Patients with Primary Prevention ICD
Bibliographic record
Abstract
BACKGROUND: Left ventricular ejection fraction (LVEF) recovers during follow-up in a significant proportion of patients implanted with a cardioverter defibrillator (ICD) for primary prevention. Little is known about the midterm arrhythmic risk in this population, particularly in relation to the presence or absence of ischemic cardiomyopathy. METHODS AND RESULTS: We retrospectively analyzed 286 patients with an ICD implanted for primary prevention between 2002 and 2010. Patients were divided into two groups based on their last LVEF assessment: (1) Recovery, defined as an LVEF > 35%; and (2) No-Recovery, defined as an LVEF ≤ 35%. Kaplan-Meir curves and multivariate Cox regression analysis were performed separately for patients with ischemic (211 patients) and nonischemic (75 patients) cardiomyopathy. Forty-nine patients (17.1%) had LVEF recovery to >35% at last follow-up. Overall, 72 patients (25.2%) experienced ventricular arrhythmias requiring ICD therapy during a median follow-up of 4.4 years. With multivariate Cox regression, LVEF recovery was associated with a lower arrhythmic risk in the whole cohort (hazard ratio [HR]: 0.38 [0.13-0.85]; P = 0.02) and in the nonischemic cardiomyopathy cohort (HR: 0.10 [0.005-0.55]; P = 0.005), but not in the ischemic cardiomyopathy cohort (HR: 0.84 [0.25-2.10]; P = 0.74). CONCLUSION: In conclusion, patients with nonischemic cardiomyopathy who improved their LVEF to >35% after primary prevention ICD implantation were at very low absolute arrhythmic risk. Our study raises the possibility that the LVEF cutoff to safely withhold ICD replacement might be higher in patients with ischemic compared to nonischemic cardiomyopathy. This will need to be confirmed in prospective studies.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".