The Posttraumatic Cognitions Inventory (PTCI): Psychometric evaluation in veteran men and women with trauma exposure
Abstract: The Posttraumatic Cognitions Inventory (PTCI) is a self-report measure of negative posttraumatic cognitions, which is an important construct in the development and maintenance of posttraumatic stress disorder (PTSD). Evidence for the most appropriate PTCI item and factor structure is mixed, and this measure has not been extensively studied in veterans. The present study examined the psychometric properties of the PTCI in two national samples of veteran men and women. Participants in Sample 1 (veterans from all service eras) and Sample 2 (recently separated veterans) completed the PTCI and additional measures of mental health symptoms. Confirmatory factor analyses indicated that a brief version of the PTCI (PTCI-9; 3-factor, nine-item) was a superior fit relative to other examined factor structures. Consistent with the original conceptualization of the measure, these factors were labeled: Negative cognitions about self, negative cognitions about the world, and self-blame. Scores on the PTCI-9 were differentially associated with the PTSD symptom clusters and with scores on self-report measures of external comorbidities. PTCI-9 scores were higher among individuals with trauma exposure and with a probable PTSD diagnosis. There was evidence of full (Sample 1) and partial (Sample 2) scalar invariance across men and women. Overall, the present study supports the use of the PTCI-9 as a measure of negative cognitions; however, scores may not be specific to PTSD and may represent a global negative thinking style. Even so, the PTCI-9 appears to be a suitable and abbreviated measure that could be used with veterans in research and clinical practice.
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.