Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure

Abstract

Abstract The absence of early outcome biomarkers for pediatric acute liver failure (PALF) hinders medical and liver transplant decisions. We sought to define dynamic interactions among circulating inflammatory mediators to gain insights into PALF outcome subgroups. Serum samples from 101 participants in the PALF study, collected over the first 7 d following enrollment, were assayed for 27 inflammatory mediators. Outcomes (spontaneous survivors [S, n = 61], nonsurvivors [NS, n = 12] and liver transplant patients [LTx, n = 28]) were assessed at 21 d post-enrollment. Dynamic interrelations among mediators were defined using data-driven algorithms. Dynamic Bayesian network inference identified a common network motif, with HMGB1 as a central node in all patient subgroups. The networks in S and LTx were similar, and differed from NS. Dynamic network analysis suggested similar dynamic connectivity in S and LTx, but a more highly interconnected network in NS that increased with time. A dynamic robustness index calculated to quantify how inflammatory network connectivity changes as a function of correlation stringency differentiated all three patient subgroups. Our results suggest that increasing inflammatory network connectivity is associated with nonsurvival in PALF and could ultimately lead to better patient outcome stratification.