Differences in Prosthetic Prescription Between Men and Women Veterans After Transtibial or Transfemoral Lower-Extremity Amputation: A Longitudinal Cohort Study (2005-2018)
Abstract: The objective was to evaluate whether prosthetic prescription differed by gender and the extent to which differences were mediated by measured factors. Study design was a retrospective longitudinal cohort study using data from Veterans Health Administration (VHA) administrative databases. The setting was VHA patients throughout the United States. For participants, the sample included 20,889 men and 324 women who had an incident transtibial or transfemoral amputation between 2005 and 2018. Interventions were not applicable. Main outcome measures were time to prosthetic prescription (up to 1 year). We used parametric survival analysis (an accelerated failure time model) to assess gender differences. We estimated mediation effects of amputation level, pain comorbidity burden, medical comorbidities, depression, and marital status on time to prescription. Results were in the 1 year after amputation, the proportion of women (54.3%) and men (55.7%) prescribed a prosthesis was similar. However, after we controlled for age, race, ethnicity, enrollment priority, VHA region, and service-connected disability, the time to prosthetic prescription was significantly faster among men compared with women (acceleration factor=0.73; 95% confidence interval, 0.61-0.87). The difference in time to prosthetic prescription between men and women was significantly mediated by amputation level (23%), pain comorbidity burden (–14%), and marital status (5%) but not medical comorbidities or depression. Although the proportion of patients with prosthetic prescription at 1-year postamputation was similar between men and women, women received prosthetic prescriptions more slowly than men, suggesting that more work is needed to understand barriers to timely prosthetic prescriptions among women, and how to intervene to reduce those barriers.
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.