Pan Yuanhang, Zhao Di, Zhang Xinbo, Yuan Na, Yang Lei, Jia Yuanyuan, Guo Yanzhao, Chen Ze, Wang Zezhi, Qu Shuyi, Bao Junxiang, Liu Yonghong
Department of Neurology, Xijing Air Force Medical University, Xi'an, People's Republic of China.
Encephalopathy Department No.2, Baoji Hospital of Traditional Chinese Medicine, Baoji, People's Republic of China.
Nat Sci Sleep. 2024 May 31;16:639-652. doi: 10.2147/NSS.S456903. eCollection 2024.
Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1.
The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early.
Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP).
Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA.
The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices.
日间过度嗜睡（EDS）是阻塞性睡眠呼吸暂停（OSA）和发作性睡病1型（NT1）的常见症状，而后者可能一直被忽视。机器学习（ML）模型能够实现对这些病症的早期检测，但此前从未应用于NT1的诊断。
本研究旨在开发ML预测模型，以帮助非睡眠专科临床医生早期识别OSA患者合并NT1的高概率情况。
共收集了三个睡眠中心246例OSA患者的临床特征，并对九个ML模型的开发进行了分析。采用LASSO回归进行特征选择。使用各种指标，如受试者操作特征曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）来评估和比较这些ML模型的性能。通过Shapley加性解释（SHAP）展示模型的可解释性。
基于AUC、DCA和校准曲线分析，梯度提升机（GBM）模型与其他机器学习（ML）模型相比表现出卓越的性能。GBM模型中按特征重要性排名的前五个特征分别是发病年龄、总肢体运动指数、睡眠潜伏期、非快速眼动（REM）睡眠2期和OSA严重程度。
本研究产生了一个简单可行的基于ML的筛查模型，用于早期识别OSA患者中的NT1，这需要在更广泛的临床实践中进一步验证。
