Ventricular Proarrhythmic Effects of Atrial Fibrillation are Modulated by Depolarization and Repolarization Anomalies in Patients with Left Ventricular Dysfunction
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) may have a ventricular proarrhythmic effect, particularly in the setting of heart failure. We assessed whether AF predicts appropriate implantable cardioverter-defibrillator (ICD) shocks in patients with left ventricular dysfunction and explored modulators of risk. METHODS AND RESULTS: A retrospective cohort study was conducted on 215 consecutive patients with ICDs for primary prevention having a left ventricular ejection fraction < or = 35%. Mean age at ICD implantation was 61.0 +/- 9.7 years and 17% were women. Overall, 22 patients (10.2%) experienced appropriate ICD shocks over a follow-up of 1.3 +/- 0.7 years, corresponding to an actuarial event-rate of 5.8% per year. In univariate analysis, AF was associated with a 3.6-fold increased risk of appropriate shocks (P = 0.0037). Annual rates of appropriate ICD shocks in patients with and without AF were 12.9% and 3.5%, respectively (P = 0.0200). In multivariate stepwise Cox regression analyses controlling for baseline imbalances, demographic parameters, underlying heart disease, and therapy, history of AF independently predicted appropriate shocks (hazard ratio 2.7, P = 0.0278). Prolonged QRS duration (>130 ms) and QTc (>440 ms) modulated the effect of AF on appropriate shocks. Patients with both AF and QRS > 130 ms were more than five times more likely to receive an appropriate ICD shock (hazard ratio 5.4, P = 0.0396). Patients with AF and QTc > 440 ms experienced a greater than 12-fold increased risk of appropriate shocks (hazard ratio 12.7, P = 0.0177). CONCLUSION: In prophylactic ICD recipients with left ventricular dysfunction, AF is associated with increased risk for ventricular tachyarrhythmias, particularly when combined with conduction and/or repolarization abnormalities.
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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 | 0.003 |
| 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 | 0.001 | 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".