The pediatric sepsis biomarker risk model.
The pediatric sepsis biomarker risk model.
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DOI：
10.1186/cc11652
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发表时间：
2012-10-01
期刊：
影响因子：
--
通讯作者：
Lindsell CJ
中科院分区：
文献类型：
--
作者：
Wong HR;Salisbury S;Xiao Q;Cvijanovich NZ;Hall M;Allen GL;Thomas NJ;Freishtat RJ;Anas N;Meyer K;Checchia PA;Lin R;Shanley TP;Bigham MT;Sen A;Nowak J;Quasney M;Henricksen JW;Chopra A;Banschbach S;Beckman E;Harmon K;Lahni P;Lindsell CJ
临床感染性休克的内在异质性是一个重大挑战。对于临床试验、个体患者管理和质量改进工作，尚不清楚哪些患者最不可能存活，从而从替代治疗方法中获益。一个强大的风险分层工具将极大地帮助决策。本研究的目的是推导并测试一种基于多种生物标志物的风险模型，以预测儿童感染性休克的预后。从先前的全基因组表达谱中鉴定出12个候选血清蛋白分层生物标志物。为了获得风险分层工具，我们测量了220名未选择的感染性休克儿童的血清样本中的生物标志物，这些样本是在重症监护病房入院的前24小时内获得的。使用分类和回归树（CART）分析生成决策树，以基于生物标志物和临床变量预测28天全因死亡率。衍生树随后在135名感染性休克儿童的独立队列中进行了测试。所得决策树包括5个生物标志物。在衍生队列中，死亡率的敏感性为91% (95% CI 70 - 98)，特异性为86%(80 - 90)，阳性预测值为43%(29 - 58)，阴性预测值为99%（95 - 100）。当应用于测试队列时，敏感性为89%(64 - 98)，特异性为64%（55 - 73）。在一个更新的模型中，包括所有355名衍生和检验队列的受试者，死亡率的敏感性为93%(79 - 98)，特异性为74%(69 - 79)，阳性预测值为32%(24 - 41)，阴性预测值为99%（96 - 100）。在更新的模型中，假阳性受试者比真阴性受试者的疾病严重程度更高，以器官衰竭的持续时间、住院时间和无重症监护天数来衡量。儿童脓毒症生物标志物风险模型（PERSEVERE；儿科脓毒症生物标志物风险模型）可靠地识别出儿童脓毒症休克的死亡风险和更严重的疾病。PERSEVERE有潜力大大提高临床决策，调整临床试验中的风险，并作为感染性休克特异性质量指标。
The intrinsic heterogeneity of clinical septic shock is a major challenge. For clinical trials, individual patient management, and quality improvement efforts, it is unclear which patients are least likely to survive and thus benefit from alternative treatment approaches. A robust risk stratification tool would greatly aid decision-making. The objective of our study was to derive and test a multi-biomarker-based risk model to predict outcome in pediatric septic shock. Twelve candidate serum protein stratification biomarkers were identified from previous genome-wide expression profiling. To derive the risk stratification tool, biomarkers were measured in serum samples from 220 unselected children with septic shock, obtained during the first 24 hours of admission to the intensive care unit. Classification and Regression Tree (CART) analysis was used to generate a decision tree to predict 28-day all-cause mortality based on both biomarkers and clinical variables. The derived tree was subsequently tested in an independent cohort of 135 children with septic shock. The derived decision tree included five biomarkers. In the derivation cohort, sensitivity for mortality was 91% (95% CI 70 - 98), specificity was 86% (80 - 90), positive predictive value was 43% (29 - 58), and negative predictive value was 99% (95 - 100). When applied to the test cohort, sensitivity was 89% (64 - 98) and specificity was 64% (55 - 73). In an updated model including all 355 subjects in the combined derivation and test cohorts, sensitivity for mortality was 93% (79 - 98), specificity was 74% (69 - 79), positive predictive value was 32% (24 - 41), and negative predictive value was 99% (96 - 100). False positive subjects in the updated model had greater illness severity compared to the true negative subjects, as measured by persistence of organ failure, length of stay, and intensive care unit free days. The pediatric sepsis biomarker risk model (PERSEVERE; PEdiatRic SEpsis biomarkEr Risk modEl) reliably identifies children at risk of death and greater illness severity from pediatric septic shock. PERSEVERE has the potential to substantially enhance clinical decision making, to adjust for risk in clinical trials, and to serve as a septic shock-specific quality metric.
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DOI：
10.1164/rccm.200807-1085oc
发表时间：
2009-03-15
影响因子：
24.7
作者：
Freishtat, Robert J.;Natale, JoAnne;Hoffman, Eric P.
通讯作者：
Hoffman, Eric P.
影响因子：
5.5
作者：
Marshall, John C.
通讯作者：
Marshall, John C.
影响因子：
8.8
作者：
Solan PD;Dunsmore KE;Denenberg AG;Odoms K;Zingarelli B;Wong HR
通讯作者：
Wong HR
DOI：
10.1097/pcc.0b013e3181e28876
发表时间：
2011-03
期刊：
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
影响因子：
--
作者：
Kaplan JM;Wong HR
通讯作者：
Wong HR
影响因子：
9.3
作者：
Wong HR;Cvijanovich N;Lin R;Allen GL;Thomas NJ;Willson DF;Freishtat RJ;Anas N;Meyer K;Checchia PA;Monaco M;Odom K;Shanley TP
通讯作者：
Shanley TP