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

  • Sleep Breathing Physiology and Disorders • Original Article
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Abstract

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.

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Data availability

The datasets analyzed during the current study are publicly accessible via https://sleepdata.org/datasets/shhs and https://sleepdata.org/datasets/wsc.

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Funding

National Science Foundation (#2052528) and National Heart, Lung, and Blood Institute (#R21HL159661-01) provided financial support in the form of research funding. The sponsor had no role in the design or conduct of this research.

Author information

Authors and Affiliations

  1. Biomedical Engineering, The University of Arizona, Tucson, AZ, USA

    Jiayan Huo & Janet Roveda

  2. Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Stuart F. Quan

  3. Asthma and Airway Disease Research Center, College of Medicine, The University of Arizona, Tucson, AZ, USA

    Stuart F. Quan

  4. Department of Electrical and Computer Engineering, The University of Arizona, 1230 E Speedway Blvd, Tucson, AZ, 85719, USA

    Janet Roveda & Ao Li

  5. BIO5 Institute, The University of Arizona, Tucson, AZ, USA

    Janet Roveda & Ao Li

Authors
  1. Jiayan Huo
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  2. Stuart F. Quan
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  4. Ao Li
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Corresponding author

Correspondence to Ao Li.

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For this type of study, formal consent is not required.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

Dr. Quan is a consultant from Bryte Bed, Whispersom, DR Capital and Best Doctors. Other authors have nothing to disclose.

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Huo, J., Quan, S.F., Roveda, J. et al. BASH-GN: a new machine learning–derived questionnaire for screening obstructive sleep apnea. Sleep Breath 27, 449–457 (2023). https://doi.org/10.1007/s11325-022-02629-8

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  • DOI: https://doi.org/10.1007/s11325-022-02629-8

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