Kim Hyun-Ji, Kim Hakseung, Kim Dong-Joo
Institute for Brain and Cognitive Engineering, Korea University, Seoul, Korea.
Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
J Clin Neurol. 2025 Jan;21(1):53-64. doi: 10.3988/jcn.2024.0038.
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.
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.
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 sleep-related 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.
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.
阻塞性睡眠呼吸暂停（OSA）与包括死亡率在内的不良后果风险增加相关。机器学习算法在预测OSA患者的临床结局方面已显示出潜力。本研究旨在开发并评估一种用于预测OSA患者10年和15年全因死亡率的机器学习算法。
根据死亡率结局将OSA患者分为死亡组和存活组。分析了各种与睡眠相关的特征，包括客观睡眠指标以及不同睡眠阶段的心率变异性。采用轻梯度提升机（LGBM）算法构建风险分层模型。使用受试者工作特征曲线下面积（AUC）评估模型对10年和15年死亡率的预测性能。使用Kaplan-Meier曲线和Cox比例风险模型进行生存分析。
本研究发现，结局较差的OSA患者的副交感神经活动高于结局较好的患者。基于LGBM的具有睡眠相关特征的预测模型具有中等准确性，预测10年和15年死亡率的平均AUC为0.806。此外，生存分析表明，LGBM能够显著区分高风险组和低风险组，Kaplan-Meier曲线和Cox回归结果证明了这一点。
本研究证实了睡眠相关特征分析和LGBM算法在评估OSA患者死亡风险方面的潜力。所开发的风险分层模型为临床医生提供了一种高效且可解释的工具，强调了患者特异性自主反应在死亡率预测中的重要性。纳入生存分析进一步验证了该模型在预测长期结局方面的稳健性。
