Treatment of comorbid sleep disorders and posttraumatic stress disorder in US active duty military personnel: A pilot randomized clinical trial
Abstract: Insomnia and nightmares are common in patients with posttraumatic stress disorder (PTSD). They are associated with worse psychological and physical health and worse PTSD treatment outcomes. In addition, they are resistant to PTSD treatments, which do not typically address sleep disorders. Cognitive behavioral therapy for insomnia and nightmares (CBT-I&N) and cognitive processing therapy (CPT) for PTSD are first-line treatments, but limited evidence exists guiding the treatment of individuals with all three disorders. The current study randomized U.S. military personnel (N = 93) to one of three conditions: CBT-I&N delivered before CPT, CBT-I&N delivered after CPT, or CPT alone; all groups received 18 sessions. Across groups, participants demonstrated significantly improved PTSD symptoms. Because the study was terminated prematurely due to challenges with recruitment and retention, it was underpowered to answer the initially intended research questions. Nonetheless, statistical findings and relevant clinically meaningful changes were observed. Compared to participants who received CPT alone, those who received CBT-I&N and CPT, regardless of sequencing, demonstrated larger improvements in PTSD symptoms, d = −0.36; insomnia, d = −0.77; sleep efficiency, d = 0.62; and nightmares, d = −.53. Compared to participants who received CBT-I&N delivered before CPT, those who received CBT-I&N delivered after CPT demonstrated larger improvements in PTSD symptoms, d = 0.48, and sleep efficiency, d = −0.44. This pilot study suggests that treating comorbid insomnia, nightmares, and PTSD symptoms results in clinically meaningful advantages in improvement for all three concerns compared to treating PTSD alone.
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.