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COMPARATIVE STUDY
JOURNAL ARTICLE
MULTICENTER STUDY
RANDOMIZED CONTROLLED TRIAL
RESEARCH SUPPORT, NON-U.S. GOV'T
Predictors of CPAP compliance in different clinical settings: primary care versus sleep unit.
Sleep & Breathing 2018 March
BACKGROUND: Good adherence to continuous positive airway pressure (CPAP) treatment improves the patient's quality of life and decreases the risk of cardiovascular disease. Previous studies that have analyzed the adherence to CPAP were performed in a sleep unit (SU) setting. The involvement of primary care (PC) in the management of obstructive sleep apnea (OSA) patients receiving CPAP treatment could introduce factors related to the adherence to treatment.
OBJECTIVES: The objective was to compare the baseline predictors of CPAP compliance in SU and PC settings.
METHODS: OSA patients treated with CPAP were followed for 6 months in SU or PC setting. We included baseline clinical and anthropometrical variables, the Epworth Sleep Scale (ESS) score, the quality of life index, and the Charlson index. A logistic regression was performed for each group to determine the CPAP compliance predictors. Discrimination and calibration were performed using the area under the curve and Hosmer-Lemeshow tests.
RESULTS: We included 191 patients: 91 in the PC group and 100 in the SU group. In 74.9% of the patients, the compliance was ≥ 4 h per day, with 80% compliance in the SU setting and 69.2% compliance in the PC setting (p = 0.087). The predictors of CPAP compliance were different between SU and PC settings. Body mass index, ESS, and CPAP pressure were predictors in the SU setting, and ESS, gender, and waist circumference were predictors in the PC setting.
CONCLUSIONS: The predictors of adequate CPAP compliance vary between SU and PC settings. Detecting compliance predictors could help in the planning of early interventions to improve CPAP adherence.
OBJECTIVES: The objective was to compare the baseline predictors of CPAP compliance in SU and PC settings.
METHODS: OSA patients treated with CPAP were followed for 6 months in SU or PC setting. We included baseline clinical and anthropometrical variables, the Epworth Sleep Scale (ESS) score, the quality of life index, and the Charlson index. A logistic regression was performed for each group to determine the CPAP compliance predictors. Discrimination and calibration were performed using the area under the curve and Hosmer-Lemeshow tests.
RESULTS: We included 191 patients: 91 in the PC group and 100 in the SU group. In 74.9% of the patients, the compliance was ≥ 4 h per day, with 80% compliance in the SU setting and 69.2% compliance in the PC setting (p = 0.087). The predictors of CPAP compliance were different between SU and PC settings. Body mass index, ESS, and CPAP pressure were predictors in the SU setting, and ESS, gender, and waist circumference were predictors in the PC setting.
CONCLUSIONS: The predictors of adequate CPAP compliance vary between SU and PC settings. Detecting compliance predictors could help in the planning of early interventions to improve CPAP adherence.
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