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.

Indeterminate pediatric acute liver failure is uniquely characterized by a CD103+CD8+ T‐cell infiltrate

“…Patients in this group, called indeterminate PALF (iPALF), have worse outcomes and are more likely to undergo liver transplantation (LT) than those with known diagnosis (dPALF) . To date, little is known about the pathophysiology of iPALF, but growing evidence supports the hypothesis that the liver injury is immune‐mediated, resulting from an imbalance between proinflammatory and anti‐inflammatory factors . iPALF patients frequently exhibit signs of immune activation and dysregulation including cytopenias, elevated soluble interleukin‐2 receptor levels, natural killer cell dysfunction, and development of aplastic anemia (AA) either shortly before or after their liver injury.…”

Liver Transplant Listing in Pediatric Acute Liver Failure: Practices and Participant Characteristics

“…Current models used to predict death in PALF are poor; therefore, listing for and proceeding to LT in a patient who may have survived with the native liver are likely unavoidable. In support of this scenario, the PALFSG previously identified distinct patterns of immune and inflammatory mediators in children with ALF that differentiated those who survived from those who died with their native liver, while those receiving LT had a unique pattern that was more like those seen in spontaneous survivors than in those who died . Furthermore, in adults it has long been recognized that there is an inherent risk of “unnecessary transplantation” in patients with ALF who were likely to recover spontaneously; and while prognostic models have been proposed, they remain imperfect .…”

Inflammation and disease: Modelling and modulation of the inflammatory response to alleviate critical illness

“…Dynamic Bayesian Network (DyBN) inference was used to identify the single most likely structure characterizing the dynamically changing inflammatory signalling network over all time points, including putative positive central nodes and negative feedback. This analysis suggested a potential “chemokine switch” architecture that might regulate the initial process of acute inflammation in humans [50]; proposed a novel role for the chemokine IP-10/CXCL-10 in spinal cord injury-induced systemic inflammation [51]; highlighted the structural similarity in the dynamic inflammatory responses of traumatic brain injury survivors and non-survivors which also offered a rational pathway from data, through data-driven modelling, to dynamic mechanistic models [*52]; and defined the dynamic inflammatory responses in subsets of patients with Paediatric Acute Liver Failure (PALF) [53, 54]. In the context of the inflammation-regulating bioreactor, DyBN suggested that sTNFR was a central node in experimental endotoxemia as well as Gram-negative sepsis in rats [**44].…”

“…In the context of the acute inflammatory response, DyNA can help suggest how these networks of inflammatory mediators change in complexity or connectively over certain time intervals. Examples of the utility of DyNA for critical illness include defining key structural differences in the inflammatory responses of mice undergoing trauma/haemorrhage vs. trauma alone [47]; the finding that human blunt trauma non-survivors [*11] as well as PALF non-survivors [54] exhibit dynamic networks that suggest ever-amplifying inflammation; the finding that the chemokine MCP-1/CCL2 is a central driver of stress induced hepatocyte inflammation in vitro [55]; and the demonstration of divergent inflammatory responses in various subsets of highly matched trauma patients [56, 57]. …”