Posttraumatic sleep disturbances in veterans: A pilot randomized controlled trial of cognitive behavioral therapy for insomnia and imagery rehearsal therapy
Abstract: Posttraumatic stress disorder (PTSD) is associated with sleep disturbances including insomnia and nightmares. This study compared cognitive behavioral therapy for insomnia (CBT-I) with CBT-I combined with imagery rehearsal therapy (IRT) for nightmares to evaluate if the combined treatment led to greater reductions in trauma-related sleep disturbances in Australian veterans.Veterans with diagnosed PTSD, high insomnia symptom severity, and nightmares (N = 31) were randomized to eight group CBT-I sessions or eight group CBT-I + IRT sessions. Self-reported sleep, nightmare, and psychological measures (primary outcome: Pittsburgh Sleep Quality Index), and objective actigraphy data were collected; the effect of obstructive sleep apnea (OSA) risk on treatment outcomes was also examined. No treatment condition effects were detected for the combined treatment compared to CBT-I alone, and no moderating effect of OSA risk was detected. On average, participants from both groups improved on various self-report measures over time (baseline to 3 months posttreatment). Despite the improvements, mean scores for sleep-specific measures remained indicative of poor sleep quality. There were also no significant differences between the groups on the actigraphy indices. The findings indicate that there is potential to optimize both treatments for veterans with trauma-related sleep disturbances.
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.