Sleep disorders in chronic obstructive pulmonary disease: etiology, impact, and management.

ABSTRACT: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality and may frequently be complicated by sleep disorders. Insomnia and obstructive sleep apnea are commonly encountered in patients with COPD. Nocturnal hypoxemia is also prevalent in COPD may occur despite adequate awake oxygenation and can be especially severe in rapid eye movement sleep. Additionally, several factors--some of them unique to COPD--can contribute to sleep-related hypoventilation. Recognition of hypoventilation can be vital as supplemental oxygen therapy itself can acutely worsen hypoventilation and lead to disastrous consequences. Finally, accruing data establish an association between restless leg syndrome and COPD--an association that may be driven by hypoxemia and/or hypercapnia. Comorbid sleep disorders portend worse sleep quality, diminished quality of life, and multifarious other adverse consequences. The awareness and knowledge regarding sleep comorbidities in COPD has continued to evolve over past many years. There are still several lacunae, however, in our understanding of the etiologies, impact, and therapies of sleep disorders, specifically in patients with COPD. This review summarizes the latest concepts in prevalence, pathogenesis, diagnosis, and management of diverse sleep disorders in COPD.

Cost minimization using an artificial neural network sleep apnea prediction tool for sleep studies.

RATIONALE: More than a million polysomnograms (PSGs) are performed annually in the United States to diagnose obstructive sleep apnea (OSA). Third-party payers now advocate a home sleep test (HST), rather than an in-laboratory PSG, as the diagnostic study for OSA regardless of clinical probability, but the economic benefit of this approach is not known. OBJECTIVES: We determined the diagnostic performance of OSA prediction tools including the newly developed OSUNet, based on an artificial neural network, and performed a cost-minimization analysis when the prediction tools are used to identify patients who should undergo HST. METHODS: The OSUNet was trained to predict the presence of OSA in a derivation group of patients who underwent an in-laboratory PSG (n = 383). Validation group 1 consisted of in-laboratory PSG patients (n = 149). The network was trained further in 33 patients who underwent HST and then was validated in a separate group of 100 HST patients (validation group 2). Likelihood ratios (LRs) were compared with two previously published prediction tools. The total costs from the use of the three prediction tools and the third-party approach within a clinical algorithm were compared. MEASUREMENTS AND MAIN RESULTS: The OSUNet had a higher +LR in all groups compared with the STOP-BANG and the modified neck circumference (MNC) prediction tools. The +LRs for STOP-BANG, MNC, and OSUNet in validation group 1 were 1.1 (1.0-1.2), 1.3 (1.1-1.5), and 2.1 (1.4-3.1); and in validation group 2 they were 1.4 (1.1-1.7), 1.7 (1.3-2.2), and 3.4 (1.8-6.1), respectively. With an OSA prevalence less than 52%, the use of all three clinical prediction tools resulted in cost savings compared with the third-party approach. CONCLUSIONS: The routine requirement of an HST to diagnose OSA regardless of clinical probability is more costly compared with the use of OSA clinical prediction tools that identify patients who should undergo this procedure when OSA is expected to be present in less than half of the population. With OSA prevalence less than 40%, the OSUNet offers the greatest savings, which are substantial when the number of sleep studies done annually is considered.