Latent Class Analysis of Adult Trauma Patients Identifies Distinct Care Pathways: A Retrospective Cohort Study

BackgroundMajor trauma is a highly heterogeneous clinical condition, posing significant challenges for accurate diagnosis, timely transfer, and effective treatment. Identification of meaningful subgroups using Latent Class Analysis (LCA) can inform clinicians and policymakers, but this approach has seldom been applied to diverse trauma cohorts. We therefore aim to identify clinically meaningful subgroups of trauma patients and explore predictors of group membership. MethodologyWe merged data for n=4,403 patients from ambulance service electronic patient care reports with a national major trauma audit and applied LCA to identify subgroups. We then used multinomial regression to examine associations between class membership and prehospital physiological parameters. ResultsUsing LCA, we identified five distinct patient classes by integrating demographics, care pathways, and outcomes of ranging severity: (1) Severe Trauma - critical care, (2) Moderate limb trauma - surgical management, (3) Minor-moderate chest, limb and spinal trauma - non-operative, (4) Head trauma - conservative management and (5) Complex, chest and head trauma. The five-class solution showed the best separation based on fit statistics and clinical interpretation. Analysis of prehospital physiological data showed that the Glasgow Coma Score was significantly lower in (1) and (4) with means of 13.0 and 13.2 respectively. ConclusionsThis first application of LCA to major trauma patients demonstrates its potential for identifying distinct patient cohorts within a heterogeneous population. Recognising these trajectories enables targeted evaluation of prehospital and in-hospital factors associated with outcomes.