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

Abstract

Background

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

Highlights

The first study utilizes ML to predict comorbid NT1 in patients with OSA.

GBM model is the best model for prediction.

Age of onset, total limb movements index, sleep latency and stage 2 of sleep time play important role in ML model.

Abbreviations

ML, machine learning; NT1, narcolepsy type 1; OSA, obstructive sleep apnea; EDS, Excessive daytime sleepiness; ROC, Receiver operator characteristic; AUC, Area under the receiver operating curve; DCA, Decision curve analysis; SHAP, SHapley Additive exPlanations; CPAP, Continuous positive airway pressure; MSLT, Multiple sleep latency test; CSF, Cerebrospinal fluid; PSG, Polysomnography; AHI, Apnea-hypopnea index; HSAT, Home sleep apnea testing; BMI, Body mass index; ICSD-3, International classification of sleep disorders-3; ESS, Epworth sleepiness scale; EEG, Electroencephalograms; ECG, Electrocardiogram; EMG, Electromyography; EOG, Electro-oculogram; ILMs, Isolated limb movements; PLMs, Periodic limb movements; RRLMs, Respiratory-related limb movements; REM, Rapid eye movement; DT, Decision tree; RF, Rand forest; XGBoost, eXtreme gradient boosting; SVM, Support vector machine; MLP, Multilayer perceptron; GBM, Gradient boosting machine; KNN, K-Nearest neighbor; PPV, Positive predictive value; NPV, Negative predictive curve.

Data Sharing Statement

The data that support the findings of this study are available from the corresponding author (Yonghong Liu) upon reasonable request.

Ethical Publication Statement

We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Acknowledgments

Yuanhang Pan, Di Zhao and Xinbo Zhang are co-first authors for this study. Anyone else who contributed to the manuscript but did not qualify for authorship had been acknowledged with their permission. All listed authors had made a significant scientific contribution to the research in the manuscript approved its claims and agreed to be an author.

Disclosure

The authors report no potential conflicts of interest in this work.

Additional information

Funding

This study was funded by the National Key R&D Program of China (2022YFC2503806); Major Research Project in Aviation Medicine (2019ZTB03, 2020JSTS21) and Key R&D Plan of Shaanxi Province (2023-YBSF-199).