Background: Catheter ablation improves symptoms and quality of life in atrial fibrillation patients, but its effect on adverse cardiovascular outcomes and mortality remains uncertain. Bayesian analysis of randomized controlled trials offers a deeper understanding of treatment effects beyond conventional p-value thresholds.

Methods: We conducted a post hoc Bayesian reanalysis of CABANA and four similar trials to estimate catheter ablation's effect on cardiovascular and survival outcomes. Using publicly available, trial-level data, we fitted ordinal Bayesian regression models to assess the impact of catheter ablation on the primary composite outcome-comprising all-cause mortality, stroke with disability, serious bleeding, and cardiac arrest-as well as mortality alone. We considered two sets of prior distributions: (1) a noninformative prior, where all effect sizes are equally probable and inference is primarily based on trial data, and (2) a treatment effect distribution derived from four trials using a random effects model.

Results: In this analysis, refined probability distributions for treatment effects were obtained by integrating data from CABANA with diverse priors through Bayes' theorem, offering a novel, nuanced probabilistic understanding of the potential impact of ablation compared with medical therapy on cardiovascular outcomes and all-cause mortality. In contrast to CABANA's original frequentist estimates, which were inconclusive, Bayesian analyses indicated probabilities of 82.6% and 81.1% that ablation is superior in reducing adverse cardiovascular outcomes and mortality, respectively. Incorporating results from four other similar trials increased the probability of improved effects on mortality to 86.0%.

Conclusions: Bayesian analysis augmented the interpretation of previously inconclusive findings, suggesting a clinically relevant probability of benefit from catheter ablation compared to medical therapy in a broad population with atrial fibrillation.

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http://dx.doi.org/10.1111/jce.16552DOI Listing

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