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. 2022 Jan 28;90(2).
doi: 10.5603/ARM.a2022.0014.

Influence of age on adherence to auto-CPAP: experience from a sleep center in Portugal

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Free article

Influence of age on adherence to auto-CPAP: experience from a sleep center in Portugal

Andreia Soares Barroso et al. Adv Respir Med. .
Free article

Abstract

Introduction: Obstructive sleep apnea (OSA) is a disorder characterized by obstructive apneas, hypopneas, and/or arousals related to respiratory effort caused by repetitive collapse of the upper airway during sleep. Left untreated, or with poor adherence to treatment, is likely to lead to negative outcomes, especially cardiac or cerebrovascular diseases. Our objective was to investigate age as a potential factor that may interfere with adherence to treatment with automatic positive airway pressure (APAP).

Materials and methods: This is a cross-sectional study on 1151 patients with OSA and we analyzed the adherence data of all patients who had been on APAP for at least six months during the period from July 1, 2019 to December 31, 2020 at Centro de Medicina do Sono. Spearman correlation was used in the bivariate analysis and to determine the factors associated with APAP adherence, a logistic regression was performed.

Results: Of 1151 patients included, 780 patients were men (67.1%) and the majority was under 65 years (59.4%). APAP adherence was higher in older age groups (p<0.001) and in patients with a higher AHI (p < 0.001), no differences were observed with regards to gender. In the multivariable regression analysis, the main factors associated with adherence were age group over 65 years (OR = 2.435; 95% CI = 1.862-3.185), AHI 15-30/h (OR = 1.733; 95% CI = 1.242-2.416), and AHI > 30/h (OR = 3.406; 95% CI = 2.426-4.782).

Conclusions: Patients older than 65 years have better adherence to APAP than younger ones and with moderate but especially severe AHI have better adherence than those with the milder form of the disease.

Keywords: APAP adherence; cross-sectional study; obstructive sleep apnea; older patients; predict-model.

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