P1024Catheter ablation is associated with reduced all-cause mortality in a real-world cohort of patients with atrial fibrillation and heart failure
Bibliographic record
Abstract
Abstract Background Atrial fibrillation (AF) and heart failure (HF) are common co-existing conditions. Randomized trial data suggests a reduction in all-cause mortality with catheter ablation (CA) in selected patients, however, whether these results are replicable in a real-world population and persist in the long-term remains to be shown. Purpose To evaluate the long-term effectiveness of CA in AF-HF patients in reducing the incidence of: a) all-cause mortality b) HF hospitalizations, and c) major morbidities (stroke/transient ischemic attack (TIA) and major bleeding). Methods A population-based administrative cohort was created of AF-HF patients with government prescription coverage in Quebec, Canada (1999–2015). Patients who underwent CA (cases) were matched 1:2 to controls using risk-set sampling. Cases were matched on time in the cohort and frequency of hospitalizations. Measured time-invariant confounders were controlled for using inverse probability of treatment weighting (IPTW) and included age, sex, clinical characteristics, presence of cardiac implantable electronic devices, and medication use. Multivariable Cox models adjusted the association of CA with the outcomes for the time varying confounders of the presence of an implantable cardioverter defibrillator (ICD) or cardiac resynchronization therapy (CRT), anticoagulation use (warfarin or direct oral anticoagulation), and any antiarrhythmic (AAD) use during follow-up. For non-fatal outcomes, the competing risk of death was accounted for using the Lunn-McNeil approach. Results Of the 87,676 AF-HF patients, 298 underwent CA and were matched to 591 controls. After IPTW, the distribution of covariates was balanced between cases and controls [age 65.6±11.0 vs 61.6±11.6; women 24% vs 20%; CHA2DS2-Vasc score 3.2±2.3 vs 2.9±2.1; CA vs non-CA, respectively; standardized mean differences <0.1 for all]. Over a median follow-up of 3.3 (IQR 1.1–6.4) years, 19 (7.3%) of CA patients died compared to 144 (24.6%) non-CA patients. After weighting and adjustment, CA was associated with a statistically significant reduction in the incidence of all-cause mortality [adjusted HR 0.5 (95% CI 0.3–0.9)]. In addition, there was no statistically significant difference in the incidence of HF hospitalizations over the follow-up [CA: 22.5% vs non-CA: 27.1%; adjusted HR 0.9 (95% CI 0.6–1.2)]. The incidences of stroke/TIA (1.7% vs 6.8%) and major bleeding (1.7% vs 4.9%) for CA vs non-CA were not statistically different. Conclusion In a matched population-based AF-HF cohort, CA was associated with a reduced risk of all-cause mortality compared to patients who did not undergo CA. Although no difference in the risk of HF hospitalizations, stroke/TIA, and major bleeding was detected between CA and non-CA patients, larger studies are warranted. Acknowledgement/Funding Canadian Institute of Health Research; Fonds de recherché du Quebec-Santé, Clinical Research Scholar Award (V. Essebag) and Doctoral Award (M. Samuel)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".