The Effects of Insomnia and Sleep Loss on Cardiovascular Disease.

Sleep loss has negative impacts on quality of life, mood, cognitive function and heath. Insomnia or difficulty sleeping is also a prevalent issue, affecting up to 35% of the population at some point in their lives. Insomnia is linked to poor mood, increased use of health care resources, and decreased quality of life as well as possible links to cardiovascular risk factors and disease. Studies have shown an increase in cortisol levels, decreased immunity, and increased markers of sympathetic activity in sleep-deprived healthy subjects and those with chronic insomnia. The literature also shows that subjective complaints consistent with chronic insomnia and shortened sleep time, both independently and in combination, can be associated with the development of diabetes, hypertension, and cardiovascular disease. In this article, we will explore the relationship and strength of association between insufficient sleep and insomnia with these health conditions.

Association between anxiety, depression, and emotional distress and hypoglossal nerve stimulator adherence.

PURPOSE: To evaluate relationships between hypoglossal nerve stimulator (HNS) adherence and the presence of anxiety, depression, and emotional distress. METHODS: This is a cross-sectional study of subjects with moderate to severe obstructive sleep apnea (OSA), who had HNS implanted and activated at The Ohio State University Medical Center (OSUMC). Patient usage data from the previous 6 months was obtained from 33 patients. Adherence was defined as ≥28 h of use per week. Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) were administered, and the Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS) score was calculated for all subjects. RESULTS: Sixty-five percent were adherent with average usage of 46.5±11.7 h per week vs 7.7±7.5 h per week in the non-adherent group. The average GAD-7 were 3.90±3.98 in the adherent group vs. 8.27±6.69 in the non-adherent group (p=0.049). PHQ-9 score was 6.15±4.31 vs. 10.09±7.53 (p=0.118), and PHQ-ADS was 10.05±7.49 vs. 19.20±9.80 (p=0.035). There were no statistically significant differences in age, gender, pre-treatment AHI, and post-treatment AHI between the two groups, though there was a trend to higher age in the adherent group. CONCLUSIONS: This study demonstrated higher GAD-7 and PHQ-ADS scores in the non-adherent group compared to those who were adherent to HNS supporting that anxiety and emotional distress may contribute to HNS therapy adherence. To our knowledge, this is the first study evaluating the relationship between anxiety, depression, emotional distress, and HNS adherence. Screening patients with the GAD-7 and PHQ-9 prior to implantation may be helpful when evaluating patient adherence to therapy.

The Effects of Insomnia and Sleep Loss on Cardiovascular Disease.

Sleep loss has negative impacts on quality of life, mood, cognitive function, and heath. Insomnia is linked to poor mood, increased use of health care resources, decreased quality of life, and possibly cardiovascular risk factors and disease. Studies have shown increase in cortisol levels, decreased immunity, and increased markers of sympathetic activity in sleep-deprived healthy subjects and those with chronic insomnia. The literature shows subjective complaints consistent with chronic insomnia and shortened sleep can be associated with development of diabetes, hypertension, and cardiovascular disease. This article explores the relationship between insufficient sleep and insomnia with these health conditions.

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.