Rationale: 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 that OSA is a heterogeneous disease, there may be unidentified subgroups demonstrating differential treatment effects. Objectives: We sought to apply a novel data-drive approach to identify nonsleepy OSA subgroups with heterogeneous effects of CPAP on CVD outcomes within the Impact of Sleep Apnea Syndrome in the Evolution of Acute Coronary Syndrome (ISAACC) study. Methods: 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 vs. usual care [UC]) outcomes within subgroups. Results: A total of 1,224 nonsleepy OSA participants were included. Of 55 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 s), 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 s), an additional split occurred by hypercholesterolemia status. Among participants with longer event duration and hypercholesterolemia, CPAP resulted in more CVD events compared with 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). Conclusions: We discovered subgroups of nonsleepy 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 with UC, whereas those with shorter OSA event duration had roughly half the rate of CVD events if randomized to CPAP.

Purpose: We designed a study investigating the cardioprotective role of sleep apnea (SA) in patients with acute myocardial infarction (AMI), focusing on its association with infarct size and coronary collateral circulation. Methods: We recruited adults with AMI, who underwent Level-III SA testing during hospitalization. Delayed-enhancement cardiac magnetic resonance (CMR) imaging was performed to quantify AMI size (percent-infarcted myocardium). Rentrop Score quantified coronary collateralization (scores 0– 3, higher scores indicating augmented collaterals). Group differences in Rentrop grade and infarct size were compared using the Wilcoxon Rank-Sum test and Fisher’s Exact test as appropriate, with a significance threshold set at p < 0.05. Results: Among 33 adults, mean age was 54.4± 11.5 and mean BMI was 28.4± 5.9. 8 patients (24%) had no SA, and 25 (76%) had SA (mild n=10, moderate n=8, severe n=7). 66% (n=22) underwent CMR, and all patients had Rentrop scores. Median infarct size in the no-SA group was 22% versus 28% in the SA group (p=0.79). While we did not find statistically significant differences, moderate SA had a trend toward a smaller infarct size (median 15.5%; IQR 9.23) compared to the other groups (no SA [22.0%; 16.8,31.8], mild SA [27%; 23.8,32.5], and severe SA [34%; 31.53], p=0.12). A higher proportion of moderate SA patients had a Rentrop grade > 0, with a trend toward significance (moderate SA versus other groups: 62.5% versus 28%, p=0.08). Conclusion: Our study did not find statistically significant differences in cardiac infarct size and the presence of coronary collaterals by sleep apnea severity among patients with AMI. However, our results are hypothesis-generating, and suggest that moderate SA may potentially offer cardioprotective benefits through enhanced coronary collaterals. These insights call for future research to explore the heterogeneity in ischemic preconditioning by SA severity and hypoxic burden to guide tailored clinical strategies for SA management in patients with AMI.

Background: Accurate quantification of visceral (VAT) and subcutaneous adipose tissue (SAT) is critical for understanding the cardiometabolic consequences of obstructive sleep apnea (OSA) and other chronic diseases. This study validates a customization framework using pre-trained networks for the development of automated VAT/SAT segmentation models using hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) data from OSA patients. While the widespread adoption of deep learning models continues to accelerate the automation of repetitive tasks, establishing a customization framework is essential for developing models tailored to specific research questions. Methods: A UNet-ResNet50 model, pre-trained on RadImageNet, was iteratively trained on 59, 157, and 328 annotated scans within a closed-loop system on the Discovery Viewer platform. Model performance was evaluated against manual expert annotations in 10 independent test cases (with 80–100 MR slices per scan) using Dice similarity coefficients, segmentation time, intraclass correlation coefficients (ICC) for volumetric and metabolic agreement (VAT/SAT volume and standardized uptake values [SUVmean]), and Bland–Altman analysis to evaluate the bias. Results: The proposed deep learning pipeline substantially improved segmentation efficiency. Average annotation time per scan was 121.8 min (manual segmentation), 31.8 min (AI-assisted segmentation), and only 1.2 min (fully automated AI segmentation). Segmentation performance, assessed on 10 independent scans, demonstrated high Dice similarity coefficients for masks (0.98 for VAT and SAT), though lower for contours/boundary delineation (0.43 and 0.54). Agreement between AI-derived and manual volumetric and metabolic VAT/SAT measures was excellent, with all ICCs exceeding 0.98 for the best model and with minimal bias. Conclusions: This scalable and accurate pipeline enables efficient abdominal fat quantification using hybrid PET/MRI for simultaneous volumetric and metabolic fat analysis. Our framework streamlines research workflows and supports clinical studies in obesity, OSA, and cardiometabolic diseases through multi-modal imaging integration and AI-based segmentation. This facilitates the quantification of depot-specific adipose metrics that may strongly influence clinical outcomes.