2026-04-09 マウントサイナイ医療システム（MSHS）
＜関連情報＞
- https://www.mountsinai.org/about/newsroom/2026/mount-sinai-researchers-develop-machine-learning-model-to-predict-how-cpap-affects-cardiovascular-disease-risk-in-patients-with-obstructive-sleep-apnea
- https://www.nature.com/articles/s43856-026-01457-1
睡眠時無呼吸症候群（非眠気性）患者におけるCPAPの二次的心血管アウトカムに対する個別化治療効果 Individualized treatment effects of CPAP on secondary cardiovascular outcomes in non-sleepy obstructive sleep apnea patients
Oren Cohen,Zainab Al-Taie,Vaishnavi Kundel,Samira Khan,Kavya Devarakonda,Vi Le,Philip M. Robson,Craig S. Anderson,Mathias Baumert,Kelly Loffler,R. Doug McEvoy,Mayte Suárez-Fariñas & Neomi A. Shah
Communications Medicine Published:13 March 2026
DOI:https://doi.org/10.1038/s43856-026-01457-1 Unedited version
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.
