Our goal is to improve short- and long-term outcomes for pediatric acute liver failure (PALF) through a better understanding of patient phenotypes, reassessment of risk classifications, and associating early events to outcome at one year. We will integrate two research efforts (Vodovotz-3U01 DK- 072146-05S1 and Roberts-1R21DK084201-01) currently collaborating with the PALF Study Group (NIH/NIDDK U01 DK072146-05) which are (1) modeling PALF as a complex biological system using physiological and inflammatory biomarkers and (2) developing models to represent the liver transplant (LT) decisions In PALF. To examine our hypotheses that clinical, biochemical, genomic, proteomic, metabolomic, immunologic, and cytokine analyses in PALF can be used to accurately define phenotypes that respond favorably to directed therapy (e.g., immunomodulation) as well as predict disease progression, including potential for spontaneous recovery or risk of death, all of which will provide a platform on which computer/informatics-based (e.g., in silico) studies can inform the design and conduct of clinical trials, and evaluate the impact of therapeutic decisions, including LT;we propose these Aims:
Aim 1 : To comprehensively characterize PALF phenotypes utilizing traditional clinical, biochemical, diagnostic, and management profiles supplemented by immune. Inflammatory and liver regeneration markers to identify factors that explain variations in outcomes for PALF phenotypes. Outcomes Include survival, LT, neurocognitive function, health-related quality of life (HRQOL), depression and post-traumatic stress disorder (PTSD) 6 months and 1 year after enrollment.
Aim 2 : To model the dynamics of PALF within and between distinct phenotypes using serially collected clinical, physiological, and biomarker data. Statistical modeling techniques will be augmented with models used to represent complex biological systems to more accurately reflect the dynamic nature of PALF. The data and models will be utilized to create a computer-based or """"""""in silico"""""""" analog of PALF to simulate interventional studies and to assess treatment, including LT decision processes and to estimate the impact of improved decision-making on organ allocation.
We will change the paradigm of research and patient management in PALF and will: 1) Improve mechanistic understanding of PALF;2) test the use of computational modeling in a this rare and complex medical condition 3) test in computer models (in silico) clinical trials of novel therapies, 4) identify cohorts within PALF phenotypes amenable to directed therapy and 5) improve LT decision making and inform organ allocation policy.
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