Microprocessor Knee Versus Non-Microprocessor Knee for Backup Device in Lower Limb Prostheses: A Qualitative Study
Abstract: Current policy in the Canadian Armed Forces (CAF) and Veterans Affairs Canada (VAC) is to provide individuals who require a prosthesis for a knee disarticulation (KD) or transfemoral (TF)-level amputation a microprocessor knee (MPK) unit for daily use and a non-microprocessor knee unit (N-MPK) as a backup prosthesis. Given the known functional differences between these two types of prosthetic knee units, the purpose of this study was to gain an understanding of user device preference and the impact of switching between the MPK and N-MPK. Four currently serving CAF members and two Veterans with unilateral TF or KD amputation participated in semi-structured interviews. Qualitative content analysis identified key themes reflecting their experiences using prostheses. Seven major categories emerged that helped shape prosthesis preferences: functionality, physical aspects, mental aspects, activity, maintenance, safety, and health-related quality of life. The MPK was superior in all categories, resulting in considerably fewer falls and improved cognitive and physical performance. The four participants who had an N-MPK backup did not use this device and instead received a loaner MPK from their prosthetist when required. For individuals who do not have ready access to their prosthetist to obtain a loaner knee unit, consideration should be given for a backup prosthesis with the same MPK unit as their daily-use prosthesis, as participants identify significant issues when trying to function with an N-MPK unit. Individuals with ready access to a loaner knee unit through their prosthetist may not require a backup prosthesis.
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.