Predicting All-Cause Mortality in Patients With Obstructive Sleep Apnea Using Sleep-Related Features:A Machine-Learning Approach
- Author:
Hyun-Ji KIM
1
;
Hakseung KIM
;
Dong-Joo KIM
Author Information
- Publication Type:ORIGINAL ARTICLE
- From:Journal of Clinical Neurology 2025;21(1):53-64
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background:and Purpose Obstructive sleep apnea (OSA) is associated with an increased risk of adverse outcomes, including mortality. Machine-learning algorithms have shown potential in predicting clinical outcomes in patients with OSA. This study aimed to develop and evaluate a machine-learning algorithm for predicting 10- and 15-year all-cause mortality in patients with OSA.
Methods:Patients with OSA were stratified into deceased and alive groups based on mortality outcomes. Various sleep-related features were analyzed, including objective sleep measures and the heart-rate variability during various sleep stages. The light gradient-boosting machine (LGBM) algorithm was employed to construct a risk-stratification model. The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC) for predicting mortality over 10 and 15 years. Survival analysis was conducted using Kaplan–Meier plots and Cox proportional-hazards model.
Results:This study found that parasympathetic activity was higher in OSA patients with worse outcomes than in those with better outcomes. The LGBM-based prediction model with sleeprelated features was moderately accurate, with a mean AUC of 0.806 for predicting 10- and 15-year mortality. Furthermore, survival analysis demonstrated that LGBM could significantly distinguish the high- and low-risk groups, as evidenced by Kaplan–Meier plots and Cox regression results.
Conclusions:This study has confirmed the potential of sleep-related feature analysis and the LGBM algorithm for evaluating the mortality risk in OSA patients. The developed risk-stratification model offers an efficient and interpretable tool for clinicians that emphasizes the significance of patient-specific autonomic responses in mortality prediction. Incorporating survival analysis further validated the robustness of the model in predicting long-term outcomes.
