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

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.National Science Foundation12 month embargo; published: 28 April 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu