Application of artificial intelligence to analyze data from randomized controlled trials: An example from DECAAF II

Background: Causal machine learning (ML) provides an efficient way of identifying heterogeneous treatment effect groups from hundreds of possible combinations, especially for randomized trial data. Objective: The aim of this paper is to illustrate the potential of applying causal ML on the DECAAF II trial data. We proposed a causal ML model to predict the treatment response heterogeneity. Methods: We applied causal tree learning to the DECAAF II trial data as an example of real applications, identifying subgroups that may be superior when subject to one of the treatments over the other through an easily interpretable process. For each subgroup identified, the characteristics were summarized, and the relationship between treatment arms and risk for recurrence of atrial tachyarrhythmia (aTA) among subjects was assessed. Results: Causal tree learning demonstrated that, among all the preablation predictors, dividing subgroups according to age, with a cutoff of 58 years, provides the most heterogeneous subgroups in response to fibrosis-guided ablation in addition to pulmonary vein isolation (PVI) compared with PVI alone. The difference in the risk of recurrence of aTA between 2 treatments was nonsignificant in older patients (hazard ratio [HR] 1.06; 95% confidence interval [CI] 0.77–1.47; P = .72). However, among the younger patients, the risk of aTA recurrence was significantly lower in the fibrosis-guided ablation group compared with PVI-only (HR 0.50; 95% CI 0.28–0.90); P = .02). Conclusion: Applying causal ML on random controlled trial datasets helped us identify groups of patients that profited from the treatment of interest in an efficient and unbiased manner.