Predicting cirrhosis in patients with hepatitis C based on standard laboratory tests: Results of the HALT-C cohort
Predicting cirrhosis in patients with hepatitis C based on standard laboratory tests: Results of the HALT-C cohort
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DOI：
10.1002/hep.20772
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发表时间：
2005-08-01
期刊：
影响因子：
13.5
通讯作者：
Morishima, C
中科院分区：
文献类型：
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作者：
Lok, ASF;Ghany, MG;Morishima, C
了解肝硬化的存在对于慢性丙型肝炎 (CHC) 患者的治疗非常重要。大多数预测肝硬化的模型均来自少数患者，并包含不易获得的主观变量或实验室测试。本研究的目的是根据标准实验室测试开发 CHC 患者肝硬化的预测模型。分析了 1,141 名 CHC 患者（其中 429 名肝硬化患者）的数据。所有活检均由病理学家小组读取（对临床特征不知情），并通过共识确定纤维化阶段。该队列分为训练集 (n = 783) 和验证集 (n = 358)。将单变量分析中肝硬化患者与非肝硬化患者之间存在显着差异的变量输入逻辑回归模型，并比较每个模型的性能。最终模型的接受者操作特征曲线下面积（包括训练组和验证组中的血小板计数、AST/ALT 比率和 INR）分别为 0.78 和 0.81。排除肝硬化的截止值小于 0.2 只会对 7.8% 的肝硬化患者进行错误分类，而确认肝硬化的截止值大于 0.5 则会对 14.8% 的非肝硬化患者进行错误分类。该模型在碎片和非碎片活检以及不同长度的活检中表现同样良好。使用该模型可能无需对 50% 的 CHC 患者进行肝活检。总之，基于标准实验室测试结果的模型可用于高度准确地预测 50% 的 CHC 患者的组织学肝硬化。
Knowledge of the presence of cirrhosis is important for the management of patients with chronic hepatitis C (CHC). Most models for predicting cirrhosis were derived from small numbers of patients and included subjective variables or laboratory tests that are not readily available. The aim of this study was to develop a predictive model of cirrhosis in patients with CHC based on standard laboratory tests. Data from 1,141 CHC patients including 429 with cirrhosis were analyzed. All biopsies were read by a panel of pathologists (blinded to clinical features), and fibrosis stage was determined by consensus. The cohort was divided into a training set (n = 783) and a validation set (n = 358). Variables that were significantly different between patients with and without cirrhosis in univariate analysis were entered into logistic regression models, and the performance of each model was compared. The area under the receiver-operating characteristic curve of the final model comprising platelet count, AST/ALT ratio, and INR in the training and validation sets was 0.78 and 0.81, respectively. A cutoff of less than 0.2 to exclude cirrhosis would misclassify only 7.8 % of patients with cirrhosis, while a cutoff of greater than 0.5 to confirm cirrhosis would misclassify 14.8 % of patients without cirrhosis. The model performed equally well in fragmented and nonfragmented biopsies and in biopsies of varying lengths. Use of this model might obviate the requirement for a liver biopsy in 50 % of patients with CHC. In conclusion, a model based on standard laboratory test results can be used to predict histological cirrhosis with a high degree of accuracy in 50 % of patients with CHC.