A Multi-Center Group to Study Acute Liver Failure in Children

我们的目标是通过更好地了解患者表型，重新评估风险分类，以及将早期事件与一年后的结果联系起来，改善儿科急性肝衰竭（PALF）的短期和长期结局。我们将整合目前与PALF研究组（NIH/NIDDK U01 DK072146-05）合作的两个研究工作（Vodovotz-3U01 DK- 072146-05S1和Roberts-1R21DK084201-01），这两个研究工作是(1)使用生理和炎症生物标志物将PALF建模为复杂的生物系统，(2)开发模型来代表PALF中的肝移植（LT）决策。为了检验我们的假设，即PALF的临床、生化、基因组、蛋白质组学、代谢组学、免疫学和细胞因子分析可用于准确定义对定向治疗（例如免疫调节）有良好反应的表型，以及预测疾病进展，包括自发恢复或死亡风险的可能性，所有这些都将提供一个平台，在这个平台上，基于计算机/信息学（例如，计算机）的研究可以为临床试验的设计和实施提供信息。评估治疗决策的影响，包括肝移植；我们提出以下目标：目标1：利用传统的临床、生化、诊断和管理资料，辅以免疫分析，全面表征PALF表型。炎症和肝脏再生标志物，以确定解释PALF表型结果变化的因素。结果包括入组后6个月和1年的生存率、LT、神经认知功能、健康相关生活质量（HRQOL）、抑郁和创伤后应激障碍（PTSD）。目的2：利用连续收集的临床、生理和生物标志物数据，对不同表型内部和之间的PALF动力学进行建模。统计建模技术将与用于表示复杂生物系统的模型相结合，以更准确地反映PALF的动态特性。这些数据和模型将用于创建基于计算机或“计算机”的PALF模拟，以模拟介入性研究和评估治疗，包括LT决策过程，并估计改进决策对器官分配的影响。

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.