title: Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure.
abstract: 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 sub-groups. Serum samples from 101 participants in the PALF study, collected over the first 7 days following enrollment, were assayed for 27 inflammatory mediators. Outcomes (Spontaneous survivors [S, n=61], Non-survivors [NS, n=12], and liver transplant patients [LTx, n=28]) were assessed at 21 days 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 sub-groups. 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 sub-groups. Our results suggest that increasing inflammatory network connectivity is associated with non-survival in PALF, and may ultimately lead to better patient outcome stratification.
authors.affiliation: Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.|Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219.
authors.forename: Ruben
authors.initials: R
authors.lastname: Zamora
authors.affiliation: Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.|Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219.
authors.forename: Yoram
authors.initials: Y
authors.lastname: Vodovotz
authors.affiliation: Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.
authors.forename: Qi
authors.initials: Q
authors.lastname: Mi
authors.affiliation: Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.
authors.forename: Derek
authors.initials: D
authors.lastname: Barclay
authors.affiliation: Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.
authors.forename: Jinling
authors.initials: J
authors.lastname: Yin
authors.affiliation: Seattle Children's Hospital, Seattle, WA 98105.
authors.forename: Simon
authors.initials: S
authors.lastname: Horslen
authors.affiliation: St Louis Children's Hospital, St. Louis, MO 63110.
authors.forename: David
authors.initials: D
authors.lastname: Rudnick
authors.affiliation: Children's Hospital of Philadelphia, Philadelphia, PA 19104.
authors.forename: Kathleen M
authors.initials: KM
authors.lastname: Loomes
authors.affiliation: Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA 15213.
authors.forename: Robert H
authors.initials: RH
authors.lastname: Squires
keywords: biomarker; computational analysis; inflammation; liver disease; networks