Abstract

Randomized controlled trials of continuous positive airway pressure (CPAP) therapy for cardiovascular disease (CVD) prevention among patients with obstructive sleep apnea (OSA) have been largely neutral. However, given OSA is a heterogeneous disease, there may be unidentified subgroups demonstrating differential treatment effects. Apply a novel data-drive approach to identify non-sleepy OSA subgroups with heterogeneous effects of CPAP on CVD outcomes within the ISAACC study. Participants were randomly partitioned into two datasets. One for training (70%) our machine learning model and a second (30%) for validation of significant findings. Model-based recursive partitioning was applied to identify subgroups with heterogeneous treatment effects. Survival analysis was conducted to compare treatment (CPAP versus usual care [UC]) outcomes within subgroups. A total of 1,224 non-sleepy OSA participants were included. Of fifty-five features entered into our model only two appeared in the final model (i.e., average OSA event duration and hypercholesterolemia). Among participants at or below the model-derived average event duration threshold (19.5 seconds), CPAP was protective for a composite of CVD events (training Hazard Ratio [HR] 0.46, p=0.002). For those with longer event duration (>19.5 seconds), an additional split occurred by hypercholesterolemia status. Among participants with longer event duration and hypercholesterolemia, CPAP resulted in more CVD events compared to UC (training HR 2.24, p=0.011). The point estimate for this harmful signal was also replicated in the testing dataset (HR 1.83, p=0.118). We discovered subgroups of non-sleepy OSA participants within the ISAACC study with heterogeneous effects of CPAP. Among the training dataset, those with longer OSA event duration and hypercholesterolemia had nearly 2.5-times more CVD events with CPAP compared to UC, while those with shorter OSA event duration had roughly half the rate of CVD events if randomized to CPAP.

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