Latent Profile Analysis of Canadian Military Veterans With Chronic Pain Identifies 5 Meaningful Classes Through Self-Report Measures

The purpose of this study was to identify meaningful response patterns in self-report survey data collected from Canadian military veterans with chronic pain and to create an algorithm intended to facilitate triage and prioritization of veterans to the most appropriate interventions. An online survey was presented to former members of the Canadian military who self-identified as having chronic pain. Variables collected were related to pain, physical and mental interference, prior traumatic experiences, and indicators from each of the 7 potential drivers of the pain experience. Maximum likelihood estimation-based latent profile analysis was used to identify clinically and statistically meaningful profiles using the 7-axis variables, and classification and regression tree (CRT) analysis was then conducted to identify the most parsimonious set of indicators that could be used to accurately classify respondents into the most relevant profile group. Data from N = 322 veterans were available for analysis. The results of maximum likelihood estimation-based latent profile analysis indicated a 5-profile structure was optimal for explaining the patterns of responses within the data. These were: Mood-Dominant (13%), Localized Physical (24%), Neurosensory-Dominant (33%), Central-Dominant with complex mood and neurosensory symptoms (16%), and Trauma- and mood-dominant (14%). From CRT analysis, an algorithm requiring only 3 self-report tools (central symptoms, mood screening, bodily coherence) achieved 83% classification accuracy across the 5 profiles. The new classification algorithm requiring 16 total items may be helpful for clinicians and veterans in pain to identify the most dominant drivers of their pain experience that may be useful for prioritizing intervention strategies, targets, and relevant health care disciplines. PERSPECTIVE: This article presents the results of latent profile (cluster) analysis of responses to standardized self-report questionnaires by Canadian military veterans with chronic pain. It identified 5 clusters that appear to represent different drivers of the pain experience. The results could be useful for triaging veterans to the most appropriate pain care providers.