Abstract
Background
Continuous positive airway pressure (CPAP) remains the cornerstone of therapy for obstructive sleep apnea, yet its impact on preventing cardiovascular disease remains uncertain. Despite widespread clinical use, randomized controlled trials have not shown cardiovascular benefits with CPAP. Emerging evidence suggests that obstructive sleep apnea is a heterogeneous disease, and a uniform approach to treatment may obscure potential benefits or harm for individuals.
Methods
To address this, we applied causal survival forest analysis to data from the SAVE trial (n = 2,687), the largest clinical trial evaluating CPAP for cardiovascular disease prevention, to estimate individualized treatment effect scores for each participant.
Results
Our model reveals significant heterogeneity in treatment response across the cohort (area under the target operator characteristic curve 2.6; 95% confidence interval 2.03-4.55; p < 0.001). Survival analysis demonstrates that participants in the tertile predicted to benefit from CPAP experienced a 100-fold improvement in event-free survival when randomized to CPAP (p < 0.001), whereas those in the tertile predicted to be harmed experienced a > 100-fold increase in major adverse cardiovascular outcomes (p < 0.001).
Conclusions
To our knowledge, these findings provide the first evidence of individualized treatment effect estimates for CPAP therapy in obstructive sleep apnea. These results also highlight the potential for precision medicine approaches to guide treatment decisions, reduce cardiovascular disease risk, and avoid harm in susceptible individuals.
Plain Language Summary
Obstructive sleep apnea (OSA) is a common condition linked to heart disease and stroke. The main treatment, continuous positive airway pressure (CPAP), essentially eliminates breathing disturbances during sleep caused by OSA. However, large studies have not shown that CPAP lowers heart disease and stroke risk for all patients with OSA. In this study, we used machine learning to create a tool that predicts how CPAP might affect an individual’s cardiovascular health. Using data from a large clinical trial, the model estimates each patient’s likely benefit or risk from CPAP based on their sleep and health information. With further testing, this tool could help patients and doctors decide when CPAP should be used to prevent heart disease and strokes.
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Data availability
Requests for source data should be made to the SAVE investigators.
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Acknowledgements
We thank the SAVE trial participants, investigators, and coordinators for their contributions to this study and for sharing their data.We thank the following funding agencies for their support of this work including the Stony-Wold Herbert Fund (Fellowship Award), American Academy of Sleep Medicine Foundation (AASMF Physician Scientist Training Award), and NHLBI (T32HL160511-02) (O.C.); NHLBI (R01HL143221) and ASMF (250-SR-21) (N.A.S.). Funding Supported by the Stony-Wold Herbert Fund Fellowship Award, and NHLBI (T32HL160511-02) (O.C.); NHLBI (R01HL143221) and ASMF (250-SR-21) (N.A.S.).
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Study concept and design: O.C., Z.A.T., V.K., K.D., P.M.R., C.S.A., M.B., K.L., D.M., M.S.F., and N.A.S. Data acquisition and transfer: S.K., V.L., M.B., K.L., R.D.M., and N.A.S. Data Analysis: O.C., Z.A.T., and M.S.F. Manuscript drafting: O.C., V.K., S.K., K.D., V.L., M.S.F., and N.A.S. All authors contributed to manuscript revisions and final approval.
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Cohen, O., Al-Taie, Z., Kundel, V. et al. Individualized treatment effects of CPAP on secondary cardiovascular outcomes in non-sleepy obstructive sleep apnea patients. Commun Med (2026). https://doi.org/10.1038/s43856-026-01457-1
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DOI: https://doi.org/10.1038/s43856-026-01457-1
