Health-related quality of life after major extremity trauma: qualitative research with military service members and clinicians to inform measurement of patient-reported outcomes

Abstract: Purpose: The purpose of this study was to understand health-related quality of life (HRQOL) factors for adults who experience major extremity trauma, including limb preservation and amputation, to guide the selection and creation of patient-reported outcome (PRO) measures. Methods: A thematic content qualitative analysis was used to study service members (SMs) with a history of major extremity trauma and clinical providers with expertise in limb trauma, limb loss, and limb preservation/reconstruction. Focus groups were conducted at three Military Treatment Facilities and one Department of Veterans Affairs Medical Center. Results: Fifty-six SMs and 34 clinicians participated. Thirty-six percent of focus group comments were coded under Physical Health, 31% Emotional Health, and 28% Social Participation. These results were largely consistent across clinicians and SMs, and clinical subgroups, with a few exceptions such as the relevance of fine motor tasks and prosthetic devices for SMs with upper extremity injury/limb loss, and orthotic devices for SMs with limb preservation/reconstruction. Conclusion: Many HRQOL topics identified are shared with existing general PRO measures—including pain, physical function, anxiety, depression, anger, positive affect and well-being, fatigue, social participation, and loneliness—as well as rehabilitation-focused PRO measures—such as resilience, grief/loss, stigma, self-esteem, mobility, fine motor functioning, self-care, and independence. This qualitative research can be used to inform domains of HRQOL in need of new PRO measures for this population, including satisfaction with orthosis/prosthesis, satisfaction with physical abilities/athleticism, body image, future outlook, and vocational impact.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    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.