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Use of hy's law and a new composite algorithm to predict acute liver failure in patients with drug-induced liver injury

机译:使用Hy定律和新的复合算法预测药物性肝损伤患者的急性肝衰竭

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Background & Aims Hy's Law, which states that hepatocellular drug-induced liver injury (DILI) with jaundice indicates a serious reaction, is used widely to determine risk for acute liver failure (ALF). We aimed to optimize the definition of Hy's Law and to develop a model for predicting ALF in patients with DILI. Methods We collected data from 771 patients with DILI (805 episodes) from the Spanish DILI registry, from April 1994 through August 2012. We analyzed data collected at DILI recognition and at the time of peak levels of alanine aminotransferase (ALT) and total bilirubin (TBL). Results Of the 771 patients with DILI, 32 developed ALF. Hepatocellular injury, female sex, high levels of TBL, and a high ratio of aspartate aminotransferase (AST):ALT were independent risk factors for ALF. We compared 3 ways to use Hy's Law to predict which patients would develop ALF; all included TBL greater than 2-fold the upper limit of normal (×ULN) and either ALT level greater than 3 × ULN, a ratio (R) value (ALT × ULN/alkaline phosphatase × ULN) of 5 or greater, or a new ratio (nR) value (ALT or AST, whichever produced the highest ×ULN/ alkaline phosphatase × ULN value) of 5 or greater. At recognition of DILI, the R- and nR-based models identified patients who developed ALF with 67% and 63% specificity, respectively, whereas use of only ALT level identified them with 44% specificity. However, the level of ALT and the nR model each identified patients who developed ALF with 90% sensitivity, whereas the R criteria identified them with 83% sensitivity. An equal number of patients who did and did not develop ALF had alkaline phosphatase levels greater than 2 × ULN. An algorithm based on AST level greater than 17.3 × ULN, TBL greater than 6.6 × ULN, and AST:ALT greater than 1.5 identified patients who developed ALF with 82% specificity and 80% sensitivity. Conclusions When applied at DILI recognition, the nR criteria for Hy's Law provides the best balance of sensitivity and specificity whereas our new composite algorithm provides additional specificity in predicting the ultimate development of ALF.
机译:背景与目的Hy法则指出肝细胞药物性黄疸引起的肝损伤(DILI)表示严重的反应,被广泛用于确定急性肝衰竭(ALF)的风险。我们旨在优化Hy定律的定义,并开发出一种预测DILI患者ALF的模型。方法从1994年4月至2012年8月,我们从西班牙DILI注册中心收集了771例DILI患者(805例)的数据。我们分析了在DILI识别时以及丙氨酸氨基转移酶(ALT)和总胆红素( TBL)。结果771例DILI患者中,有32例发展为ALF。肝细胞损伤,女性,高水平的TBL和高比例的天冬氨酸转氨酶(AST):ALT是ALF的独立危险因素。我们比较了三种使用海斯定律预测哪些患者会发展为ALF的方法。所有这些都包括TBL大于正常上限(×ULN)的2倍,或者ALT水平大于3×ULN,比率(R)值(ALT×ULN /碱性磷酸酶×ULN)为5或更大,或者新比率(nR)值(ALT或AST,以最大的×ULN /碱性磷酸酶×ULN值为准)等于或大于5。在识别DILI时,基于R和nR的模型分别识别出发展为ALF的患者,其特异性分别为67%和63%,而仅使用ALT水平将其识别为具有44%的特异性。但是,ALT水平和nR模型均以90%的敏感性鉴定出发展为ALF的患者,而R标准以83%的敏感性鉴定为患有ALF的患者。患有和未患有ALF的患者中,有相等数量的患者碱性磷酸酶水平高于2×ULN。一种基于AST水平大于17.3×ULN,TBL大于6.6×ULN和AST:ALT大于1.5的算法可识别出具有82%特异性和80%敏感性的ALF患者。结论当应用于DILI识别时,Hy定律的nR准则在灵敏度和特异性之间达到了最佳平衡,而我们的新复合算法在预测ALF的最终发展方面提供了更高的特异性。

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