BASH-GN: a new machine learning–derived questionnaire for screening obstructive sleep apnea

• Published in: Sleep & breathing Vol. 27; no. 2; pp. 449 - 457
• Main Authors: Huo, Jiayan, Quan, Stuart F., Roveda, Janet, Li, Ao
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.05.2023; Springer Nature B.V
• Subjects: Apnea; Dentistry; Humans; Internal Medicine; Learning algorithms; Machine Learning; Mass Screening; Medicine; Medicine & Public Health; Neurology; Otorhinolaryngology; Pediatrics; Phenotypes; Pneumology/Respiratory System; Questionnaires; Risk factors; ROC Curve; Sleep and wakefulness; Sleep apnea; Sleep Apnea, Obstructive - diagnosis; Sleep Breathing Physiology and Disorders • Original Article; Sleep disorders; Surveys and Questionnaires; Screening; Questionnaire; Obstructive sleep apnea; Machine learning
• ISSN: 1520-9512; 1522-1709; 1522-1709
• DOI: 10.1007/s11325-022-02629-8

Purpose This study aimed to develop a machine learning–based questionnaire (BASH-GN) to classify obstructive sleep apnea (OSA) risk by considering risk factor subtypes. Methods Participants who met study inclusion criteria were selected from the Sleep Heart Health Study Visit 1 (SHHS 1) database. Other participants from the Wisconsin Sleep Cohort (WSC) served as an independent test dataset. Participants with an apnea hypopnea index (AHI) ≥ 15/h were considered as high risk for OSA. Potential risk factors were ranked using mutual information between each factor and the AHI, and only the top 50% were selected. We classified the subjects into 2 different groups, low and high phenotype groups, according to their risk scores. We then developed the BASH-GN, a machine learning–based questionnaire that consists of two logistic regression classifiers for the 2 different subtypes of OSA risk prediction. Results We evaluated the BASH-GN on the SHHS 1 test set ( n  = 1237) and WSC set ( n  = 1120) and compared its performance with four commonly used OSA screening questionnaires, the Four-Variable, Epworth Sleepiness Scale, Berlin, and STOP-BANG. The model outperformed these questionnaires on both test sets regarding the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). The model achieved AUROC (SHHS 1: 0.78, WSC: 0.76) and AUPRC (SHHS 1: 0.72, WSC: 0.74), respectively. The questionnaire is available at https://c2ship.org/bash-gn . Conclusion Considering OSA subtypes when evaluating OSA risk may improve the accuracy of OSA screening.