Temporal robustness of biomarker-based classification algorithms for sepsis

Abstract

PURPOSE: Heterogeneity of the host response in sepsis hampers development of effective treatments. Several immunobiologically distinct subphenotypes (or endotypes) have been identified using data-driven analyses of single-timepoint biomarker data, but their temporal stability remains uncertain due to dynamic biology and statistical limitations. METHODS: We analyzed data from 345 sepsis patients across two ICU cohorts. 30 immune biomarkers were measured every 8 h for up to 7 days. Latent profile analysis was used to identify classes upon admission and re-classify patients at later timepoints. Temporal robustness was assessed by (1) inter-class transition rates, and (2) intra-class cohesion (regardless of label) using the Rand Index (RI). RESULTS: At ICU admission, three immune profiles were identified: profile A (149 patients, 43%) reflected adaptive immune activation (elevated IL-4, IL-5, RANTES, and GM-CSF); profile B (60 patients, 17%) a hyperinflammatory state (high IL-6, IL-8, IL-1Ra, and low protein C); and profile C (136 patients, 39%) broadly attenuated inflammation. By 48 h, the prevalences of A and B declined to 31% and 13%, while C increased to 56%. Inter-class transitions occurred most in patients assigned to A (41% of all 8-hourly transitions), compared to 39% and 22% for B and C. Intra-class cohesion across intervals was poor (median RI 65%, IQR 62-64%), indicating that patients classified together at admission did not remain consistently together. CONCLUSION: Sepsis patients were frequently reclassified across immune profiles over short intervals, with approximately one-third of subgroup peers changing at each timepoint. This instability challenges the clinical utility of biomarker-derived endotypes.