BACKGROUND Sepsis is a complex clinical syndrome with substantial heterogeneity. We sought to identify patterns of serum biomarkers of endothelial activation and dysfunction in individuals with sepsis and evaluate subgroup-specific differences in mortality.METHODS Adult patients with sepsis (n = 426) were consecutively recruited from 2 hospitals in Uganda. Clinical information was collected, and serum concentrations of 11 biomarkers involved in the endothelial response to infection were measured in samples from 315 patients. Latent variable models were fit to evaluate whether the endothelial response to sepsis consists of one unified biologic process or multiple processes and to identify subgroups of patients with distinct host-response profiles. Differences in survival at day 28 were evaluated using Kaplan-Meier survival curves.RESULTS We identified 3 patient subgroups characterized by unique host endothelial response profiles. Patients fitting profile 2 had significantly worse survival (log-rank P < 0.001). Four latent factors (factors 1–4) were identified, each potentially representing distinct biologic processes for the endothelial response to sepsis: factor 1 (CHI3L1, sTREM1, sFLT1), factor 2 (ANGPT1, PF4, VEGF), factor 3 (CXCL10, vWF, sICAM1), and factor 4 (ANGPT2, sTEK).CONCLUSION Patient profiles based on patterns of circulating biomarkers of endothelial responses may provide a clinically meaningful way to categorize patients into homogeneous subgroups and may identify patients with a high risk of mortality. Profile 2 may represent dysfunction of the endothelial response to infection.FUNDING Primary funding: Investigator-Initiated Award provided by Pfizer Inc. Additional support: Canadian Institutes of Health Research Foundation grant (FDN-148439) and the Canada Research Chair program.
Danielle V. Clark, Patrick Banura, Karen Bandeen-Roche, W. Conrad Liles, Kevin C. Kain, W. Michael Scheld, William J. Moss, Shevin T. Jacob
Standardized mean concentrations of biomarkers by class as determined by latent profile analysis