Investigation on factors related to poor CPAP adherence using machine learning: a pilot study

To improve patients’ adherence to continuous positive airway pressure (CPAP) therapy, this study aimed to clarify whether machine learning-based data analysis can identify the factors related to poor CPAP adherence (i.e., CPAP usage that does not reach four hours per day for five days a week). We de...

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Bibliographic Details
Published in:Scientific reports 2022-11, Vol.12 (1), p.19563-19563, Article 19563
Main Authors: Eguchi, Kana, Yabuuchi, Tsutomu, Nambu, Masayuki, Takeyama, Hirofumi, Azuma, Shozo, Chin, Kazuo, Kuroda, Tomohiro
Format: Article
Language:English
Subjects:
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Air leakage
Airway management
Apnea
Continuous positive airway pressure
Continuous Positive Airway Pressure - methods
Data analysis
Hospitals
Humanities and Social Sciences
Humans
Learning algorithms
Machine Learning
Medicine
multidisciplinary
Patient Compliance
Patients
Pilot Projects
Prediction models
Regression analysis
Science
Science (multidisciplinary)
Sleep apnea
Sleep Apnea, Obstructive - therapy
Sleep disorders
Standard deviation
Standardization
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Summary:To improve patients’ adherence to continuous positive airway pressure (CPAP) therapy, this study aimed to clarify whether machine learning-based data analysis can identify the factors related to poor CPAP adherence (i.e., CPAP usage that does not reach four hours per day for five days a week). We developed a CPAP adherence prediction model using logistic regression and learn-to-rank machine learning with a pairwise approach. We then investigated adherence prediction performance targeting a 12-week period and the top ten factors correlating to poor CPAP adherence. The CPAP logs of 219 patients with obstructive sleep apnea (OSA) obtained from clinical treatment at Kyoto University Hospital were used. The highest adherence prediction accuracy obtained was an F1 score of 0.864. Out of the top ten factors obtained with the highest prediction accuracy, four were consistent with already-known clinical knowledge. The factors for better CPAP adherence indicate that air leakage should be avoided, mask pressure should be kept constant, and CPAP usage duration should be longer and kept constant. The results indicate that machine learning is an adequate method for investigating factors related to poor CPAP adherence.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-21932-8