Dynamic biomarker profiling and phenotyping in burn sepsis: a retrospective cohort study using growth mixture modeling

Objective: To assess the dynamic prognostic value of multicategory biomarkers in burn sepsis and identify patient phenotypes based on their longitudinal trajectories. Methods: ratio; natural killer, NK cells), and inflammation (interleukin-6, IL-6; C-reactive protein, CRP; procalcitonin, PCT; platelet count, lactate) were measured at five time points (days 1, 3, 7, 14, and 21). We compared survivor/non-survivor trajectories using linear mixed-effects models, assessed baseline biomarker associations with 21-day mortality via Cox regression, and evaluated predictive performance with Harrell's C-index. Growth mixture modeling (GMM) identified phenotypes from integrated ALB, IL-6, and immunoglobulin G (IgG) trajectories. Results: = 0.004). However, no biomarker retained independent significance in multivariable analysis, likely due to multicollinearity among nutritional markers [variance inflation factor (VIF) up to 8.4]. Harrell's C-indices for baseline ALB, PA, and IgG were modest (0.604, 0.583, and 0.585, respectively). Conclusions: Longitudinal multicategory biomarker trajectories predict 21-day survival in burn sepsis. Trajectory-based phenotyping identifies patient subgroups with markedly different outcomes, offering superior prognostic stratification over static measurements. The integrated phenotype, reflecting the dynamic interplay of catabolism, immune paralysis, and inflammation, emerges as a robust prognostic marker, supporting personalized management approaches.