Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure
Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure
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DOI：
10.2119/molmed.2016.00183
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发表时间：
2016-01-01
影响因子：
5.7
通讯作者：
Squires, Robert H.
中科院分区：
文献类型：
--
作者：
Zamora, Ruben;Vodovotz, Yoram;Squires, Robert H.
儿科急性肝衰竭（PALF）早期结果生物标志物的缺乏阻碍了医疗和肝移植决策。我们试图确定循环炎症介质之间的动态相互作用，以深入了解PALF结局亚组。PALF研究中的101名参与者在入组后的前7天内采集血清样本，分析27种炎症介质。在入组后第21天评估结局（自发存活者[S，n = 61]、非存活者[NS，n = 12]和肝移植患者[LTx，n = 28]）。使用数据驱动算法定义了调解员之间的动态相互关系。动态贝叶斯网络推理确定了一个共同的网络基序，HMGB1作为所有患者亚组的中心节点。S和LTx中的网络相似，但与NS不同。动态网络分析表明，类似的动态连接在S和LTx，但更高度互连的网络在NS中，随着时间的推移而增加。计算动态鲁棒性指数以量化炎症网络连接如何作为区分所有三个患者亚组的相关性严格性的函数而变化。我们的研究结果表明，增加炎症网络连接与PALF的非生存相关，并最终导致更好的患者结局分层。
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.