Physiological Traits and Adherence to Sleep Apnea Therapy in Individuals with Coronary Artery Disease

Abstract

Rationale: Untreated obstructive sleep apnea (OSA) is associated with adverse outcomes in patients with coronary artery disease (CAD). Continuous positive airway pressure (CPAP) is the most common treatment, but despite interventions addressing established adherence determinants, CPAP use remains poor. Objectives: To determine whether physiological traits that cause OSA are associated with long-term CPAP adherence in patients with CAD. Methods: Participants in the RICCADSA (Randomized Intervention with CPAP in CAD and OSA) trial with objective CPAP adherence (h/night) over 2 years and analyzable raw polysomnography data were included (N = 249). The physiological traits-loop gain, arousal threshold (ArTH), pharyngeal collapsibility (Vpassive), and pharyngeal muscle compensation (Vcomp)-were measured by using polysomnography. Linear mixed models were used to assess the relationship between the traits and adherence. We also compared actual CPAP adherence between those with physiologically predicted "poor" adherence (lowest quartile of predicted adherence) and those with physiologically predicted "good" adherence (all others). Measurements and Main Results: The median (interquartile range) CPAP use declined from 3.2 (1.0-5.8) h/night to 3.0 (0.0-5.6) h/night over 24 months (P < 0.001). In analyses adjusted for demographics, anthropometrics, OSA characteristics, and clinical comorbidities, a lower ArTH was associated with worse CPAP adherence (0.7 h/SD of the ArTH; P = 0.021). Both high and low Vcomp were associated with lower adherence (P = 0.008). Those with predicted poor adherence exhibited markedly lower CPAP use than those with predicted good adherence for up to 2 years of follow-up (group differences of 2.0-3.2 h/night; P < 0.003 for all). Conclusions: A low ArTH, as well as a very low and high Vcomp, are associated with worse long-term CPAP adherence in patients with CAD and OSA. Physiological traits-alongside established determinants-may help predict and improve CPAP adherence. Clinical trial registered with www.clinicaltrials.gov (NCT00519597).

Keywords: adherence; arousal threshold; coronary artery disease; obstructive sleep apnea; physiologic traits.

Figures

Figure 1.

Distribution of average continuous positive airway pressure (CPAP; h/night) use at the (A) 1-month follow-up and (B) 24-month follow-up.

Figure 2.

Relationship among a low arousal threshold (ArTH), the sleep duration at baseline, and the longitudinal continuous positive airway pressure (CPAP) adherence. A directed acyclic graph and mediation analysis model for the change in CPAP adherence caused by a change in the ArTH (see alsoTable 2) as mediated by the total sleep time at baseline (P = 0.010 for mediation) is shown. A lower total sleep time at baseline accounted for 23.5% (interquartile range, 2.3–100.0%) of the relationship between the ArTH and CPAP adherence (12 min of the 49-min decrease in CPAP use were attributed to each 1-SD decrease in the ArTH).

Figure 3.

Comparison of continuous positive airway pressure (CPAP) adherence in those with predicted “good” adherence and those with predicted “poor” adherence. (A) Comparison made by using the physiological adherence model between the median actual CPAP adherence (h/night) of those with predicted good adherence (green) and the median actual CPAP adherence (h/night) of those with predicted poor adherence (red). Solid circles represent results before leave-one-out cross-validation, and open squares represent results after leave-one-out cross-validation. Differences between groups are statistically different across each time point (P < 0.003). (B) Distributions and medians (bars) of actual CPAP adherence among those with predicted good adherence (green, n = 187) and those with predicted poor adherence (red, n = 62) at the 1-month follow-up (change in median, 2.0 h/night; P = 0.003) and 24-month follow-up (change in median, 3.2 h/night; P < 0.001).