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.

Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea–hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.

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.

Rationale: Obstructive sleep apnea (OSA) is associated with major adverse cardiac events (MACE); continuous positive airway pressure (CPAP) has not reduced MACE in randomized trials. We hypothesized that multidomain machine learning would identify OSA subgroups differing in cardiovascular risk and CPAP response. Methods: We conducted a secondary analysis of SAVE trial data. After preprocessing, Gaussian mixture models were fitted; Bayesian information criterion and C-index selected five prototypes. Cox models estimated MACE risk by prototype. Baseline quality of life (EQoL), Epworth Sleepiness Scale (ESS), and Framingham Risk Score (FRS) were compared; an existing causal survival forest model estimated prototype-specific CPAP effects on cardiovascular risk. Measurements and Main Results: After preprocessing, five OSA prototypes were identified among 2,676 participants using 88 baseline variables. Prototypes differed in selected demographic, cardiometabolic, medication, and sleep variables, including smoking status, diabetes, prior myocardial infarction/stroke, antiplatelet/lipid-lowering therapy, BMI, oxygenation metrics, apnea-hypopnea index, and ESS (overall p<0.001). They also differed in baseline EQoL, FRS, MACE rate, and predicted CPAP response. Prototype 1 had the highest ESS, FRS, and MACE rate, whereas Prototype 5 had the lowest FRS and MACE rate. Using the causal survival forest model to assess prototype-specific CPAP effects on cardiovascular risk, Prototype 5 was predicted to benefit from CPAP (p<0.05), whereas Prototypes 1, 3, and 4 showed no benefit or possible harm. Conclusion: These findings demonstrate heterogeneity in cardiovascular risk and predicted CPAP response, potentially explaining neutral trial results. After external validation, these prototypes could guide trial enrichment and targeted CPAP use to improve cardiovascular outcomes. Impact Statement: This study advances clinical medicine by identifying multidomain OSA prototypes that differ in baseline cardiovascular risk and predicted cardiovascular response to CPAP, potentially supporting more precise treatment selection and future cardiovascular-outcomes trials. It adds to the disease knowledge base by showing that OSA-related cardiovascular risk is not captured by conventional severity metrics alone, but reflects interactions among sleep, cardiometabolic, demographic, and clinical characteristics. These findings provide a framework for mechanistic and prospective studies of the biological pathways underlying heterogeneous cardiovascular susceptibility and CPAP response in OSA.