Same sleep disorder but different sleep patterns: Individual differences in sleep health and depressive symptomatology in Veterans with obstructive sleep apnea
Abstract: PURPOSE: Poor sleep health, a composite measure of key sleep characteristics, may relate to increased depressive symptoms among individuals treated for obstructive sleep apnea. The current investigation examined the association between sleep health and depressive symptomatology. METHODS: In a pilot sample of 13 symptomatic OSA military Veterans with adequate CPAP adherence (mean age = 54.8, 76.9% male, 100% White), empirically validated cutoffs were applied to actigraphy-derived sleep variables: duration, efficiency, timing, and regularity. RESULTS: Participants with zero optimal sleep scores had significantly higher depressive scores (M = 19.0, SD = 3.0) than participants with 1 or 2 (M = 9.8. SD = 4.3, p = .016) and 3 or more optimal sleep scores (M = 11.3, SD = 4.9, p = .038). CONCLUSIONS: These preliminary findings suggest that better sleep health was associated with lower depressive symptomatology. Future work should replicate these preliminary findings in a larger sample.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.