Machine learning-Based model for prediction of Narcolepsy Type 1 in Patients with Obstructive Sleep Apnea with Excessive Daytime Sleepiness

Abstract

Yuanhang Pan,1,* Di Zhao,1,* Xinbo Zhang,1,* Na Yuan,1 Lei Yang,1 Yuanyuan Jia,2 Yanzhao Guo,3 Ze Chen,1 Zezhi Wang,1 Shuyi Qu,1 Junxiang Bao,4 Yonghong Liu1 1Department of Neurology, Xijing Air Force Medical University, Xi’an, People’s Republic of China; 2Encephalopathy Department No.2, Baoji Hospital of Traditional Chinese Medicine, Baoji, People’s Republic of China; 3Encephalopathy Department No.10, Xi’an Hospital of Traditional Chinese Medicine, Xi’an, People’s Republic of China; 4Department of Aerospace Hygiene, Air Force Medical University, Xi’an, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yonghong Liu, Department of Neurology, Xijing hospital, Air Force Medical University, Xi’an, People’s Republic of China, Tel [phone], Email [email protected] Junxiang Bao, Department of Aerospace Hygiene, Air Force Medical University, Xi’an, People’s Republic of China, Tel [phone], Email [email protected]: 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.Objective: 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.Methods: 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).Results: 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.Conclusion: 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.Keywords: obstructive sleep apnea, narcolepsy, machine learning, prediction model, sleep disorder

Keywords

• obstructive sleep apnea
• narcolepsy
• machine learning
• prediction model
• sleep disorder