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Pre- and in-therapy predictive score models of adult OSAS patients with poor adherence pattern on nCPAP therapy

Authors Li Y, Wang Y, Alan G, Chai Y, Luo J, Niu X, Hai B, Qin J

Received 18 February 2015

Accepted for publication 18 March 2015

Published 28 May 2015 Volume 2015:9 Pages 715—723

DOI https://doi.org/10.2147/PPA.S83105

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 4

Editor who approved publication: Dr Naifeng Liu

Yeying Wang,1,2 Alan F Geater,3 Yanling Chai,1 Jiahong Luo,2 Xiaoqun Niu,1 Bing Hai,1 Jingting Qin,1 Yongxia Li1

1Department of Respiratory Medicine, The 2nd Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, People’s Republic of China; 2Department of Epidemiology and Biostatistics, School of Public Health, Kunming Medical University, Kunming, Yunnan Province, People’s Republic of China; 3Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand

Objectives: To identify patterns of adherence to nasal continuous positive airway pressure (nCPAP) use in the first 3 months of therapy among newly diagnosed adult patients with obstructive sleep apnea/hypopnea syndrome (OSAS) and their predictors. To develop pretherapy and in-therapy scores to predict adherence pattern.
Methods: Newly diagnosed adult OSAS patients were consecutively recruited from March to August 2013. Baseline clinical information and measures such as Epworth Sleepiness Scale (ESS), Fatigue Severity Scale (FSS), Zung’s Self-Rating Depression Scale (SDS), and The Pittsburgh Sleep Quality Index (PSQI) at baseline and at the end of 3rd-week therapy were collected. Twelve weeks’ adherence data were collected from the nCPAP memory card, and K-means cluster analysis was used to explore adherence patterns. Predictive scores were developed from the coefficients of cumulative logit models of adherence patterns using variables available at baseline and after 3 weeks of therapy. Performance of the score was validated using 500 bootstrap resamples.
Results: Seventy six patients completed a 12-week follow-up. Three patterns were revealed. Patients were identified as developing an adherence pattern that was poor (n=14, mean ± SD, 2.3±0.9 hours per night), moderate (n=19, 5.3±0.6 hours per night), or good (n=43, 6.8±0.3 hours per night). Cumulative logit regression models (good → moderate → poor) revealed independent baseline predictors to be ESS (per unit increase) (OR [95% CI], 0.763 [0.651, 0.893]), SDS (1.461 [1.238, 1.724]), and PSQI (2.261 [1.427, 3.584]); and 3-week therapy predictors to be ESS (0.554 [0.331, 0.926]), PSQI (2.548 [1.454, 4.465]), and the changes (3rd week–baseline data) in ESS (0.459 [0.243, 0.868]), FSS (3.556 [1.788, 7.070]), and PSQI (2.937 [1.273, 6.773]). Two predictive score formulas for poor adherence were developed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curves for baseline and 3-week formulas were 0.989 and 0.999, respectively. Bootstrap analysis indicated positive predictive values of baseline and 3-week predictive scores in our patient population of 0.82 (95% CI [0.82, 0.83]) and 0.94 (95% CI [0.93, 0.94]), respectively.
Conclusion: A high level of prediction of poor adherence pattern is possible both before and at the first 3 weeks of therapy. The predictive scores should be further evaluated for external validity.

Keywords: OSAS, adherence, nCPAP, predictive model, K-means cluster analysis, bootstrap analysis

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