Randomized Effectiveness Trial of a Brief Course of Acupuncture for Posttraumatic Stress Disorder
Abstract: Initial posttraumatic stress disorder (PTSD) care is often delayed and many with PTSD go untreated. Acupuncture appears to be a safe, potentially nonstigmatizing treatment that reduces symptoms of anxiety, depression, and chronic pain, but little is known about its effect on PTSD. Fifty-five service members meeting research diagnostic criteria for PTSD were randomized to usual PTSD care (UPC) plus eight 60-minute sessions of acupuncture conducted twice weekly or to UPC alone. Outcomes were assessed at baseline and 4, 8, and 12 weeks postrandomization. The primary study outcomes were difference in PTSD symptom improvement on the PTSD Checklist (PCL) and the Clinician-administered PTSD Scale (CAPS) from baseline to 12-week follow-up between the 2 treatment groups. Secondary outcomes were depression, pain severity, and mental and physical health functioning. Mixed model regression and t test analyses were applied to the data. Mean improvement in PTSD severity was significantly greater among those receiving acupuncture than in those receiving UPC (PCLΔ=19.8±13.3 vs. 9.7±12.9, P<0.001; CAPSΔ=35.0±20.26 vs.10.9±20.8, P<0.0001). Acupuncture was also associated with significantly greater improvements in depression, pain, and physical and mental health functioning. Pre-post effect-sizes for these outcomes were large and robust. Acupuncture was effective for reducing PTSD symptoms. Limitations included small sample size and inability to parse specific treatment mechanisms. Larger multisite trials with longer follow-up, comparisons to standard PTSD treatments, and assessments of treatment acceptability are needed. Acupuncture is a novel therapeutic option that may help to improve population reach of PTSD treatment.
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.