Adaptive cardiac resynchronization therapy is associated with decreased risk of incident atrial fibrillation compared to standard biventricular pacing: A real-world analysis of 37,450 patients followed by remote monitoring
Bibliographic record
Abstract
BACKGROUND: The AdaptivCRT algorithm (aCRT) automatically adjusts atrioventricular delays each minute to achieve ventricular fusion through left ventricular (LV) or biventricular (BiV) pacing. aCRT is associated with superior clinical outcomes compared to standard BiV pacing, but the association of aCRT and subsequent atrial fibrillation (AF) in a real-world population has not been fully evaluated. OBJECTIVE: The purpose of this study was to investigate the incidence of AF ≥48 hours with aCRT vs standard BiV pacing after implant. METHODS: Patients implanted with a cardiac resynchronization therapy (CRT) device between 2013 and 2016 were studied via the de-identified Medtronic CareLink database. For univariate and multivariate survival analyses, Kaplan-Meier and Cox proportional hazards were used, respectively. RESULTS: Of 37,450 patients (mean age 69.1 ± 11.0 years; 67.9% male) followed for a mean 15.5 ± 9.1 months, 9.7% (n = 3647) developed ≥48 hours of AF. In univariate analysis, compared with standard BiV pacing, the aCRT BiV and LV mode was associated with a 54% lower risk of ≥48 hours of AF (P <.001) at 2 years, which persisted after multivariate adjustment (hazard ratio 0.53; 95% confidence interval 0.49-0.57; P <.001), even when stratified by sensed PR interval ≤200 ms and >200 ms. Higher percentages of LV-only pacing with aCRT were associated with lower incidence of AF (comparing >92% LV-only pacing vs 0%-5% LV-only pacing: HR 0.05; 95% CI 0.04-0.06; P <.001). CONCLUSION: In a large, real-world population of CRT recipients, aCRT pacing compared to standard BiV pacing was associated with a lower incidence of AF in patients with both long and short PR intervals. A higher percentage of LV-only pacing during aCRT was also associated with lower incidence of AF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".