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. 2022 Nov 15;12(1):19563.
doi: 10.1038/s41598-022-21932-8.

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

Kana Eguchi  1   2 , Tsutomu Yabuuchi  3 , Masayuki Nambu  4 , Hirofumi Takeyama  5   6 , Shozo Azuma  7 , Kazuo Chin  5   8   9 , Tomohiro Kuroda  10
Affiliations

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

Kana Eguchi et al. Sci Rep. .

Abstract

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.

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Conflict of interest statement

The Department of Real World Data R&D of Kyoto University is funded by endowments from Nippon Telegraph and Telephone (NTT) Corporation, Canon Medical Systems Corporation, and H.U. Group Holdings, Inc. to Kyoto University. The Preemptive Medicine & Lifestyle-Related Disease Research Center is funded by Resorttrust, Inc. The Department of Respiratory Care and Sleep Control Medicine of Kyoto University is funded by endowments from Philips-Respironics, ResMed, Fukuda Denshi, and Fukuda Lifetec-Keiji to Kyoto University. The Department of Sleep Medicine and Respiratory Care of Nihon University is funded by endowments from Philips-Respironics, ResMed, Fukuda Denshi, and Fukuda Lifetec-Tokyo to Nihon University. Aside from the above, the authors have no competing interests.

Figures

Figure 1
Figure 1
Flowchart of participant selection. Definition of abbreviations: CPAP = continuous positive airway pressure; EHR = electric health record.
Figure 2
Figure 2
Number of weeks until first poor CPAP adherence among 145 patients who exhibited poor CPAP adherence.
Figure 3
Figure 3
Receiver operating characteristic (ROC) curve indicating prediction performance of proposed model.
See this image and copyright information in PMC

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