Konerman Monica A, Lu Dongxia, Zhang Yiwei, Thomson Mary, Zhu Ji, Verma Aashesh, Liu Boang, Talaat Nizar, Balis Ulysses, Higgins Peter D R, Lok Anna S F, Waljee Akbar K
University of Michigan Health System, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
University of Michigan Health System, Department of Internal Medicine, Ann Arbor, Michigan, United States of America.
PLoS One. 2017 Nov 6;12(11):e0187344. doi: 10.1371/journal.pone.0187344. eCollection 2017.
Assessing risk of adverse outcomes among patients with chronic liver disease has been challenging due to non-linear disease progression. We previously developed accurate prediction models for fibrosis progression and clinical outcomes among patients with advanced chronic hepatitis C (CHC). The primary aim of this study was to validate fibrosis progression and clinical outcomes models among a heterogeneous patient cohort.
Adults with CHC with ≥3 years follow-up and without hepatic decompensation, hepatocellular carcinoma (HCC), liver transplant (LT), HBV or HIV co-infection at presentation were analyzed (N = 1007). Outcomes included: 1) fibrosis progression 2) hepatic decompensation 3) HCC and 4) LT-free survival. Predictors included longitudinal clinical and laboratory data. Machine learning methods were used to predict outcomes in 1 and 3 years.
The external cohort had a median age of 49.4 years (IQR 44.3-54.3); 61% were male, 80% white, and 79% had genotype 1. At presentation, 73% were treatment naïve and 31% had cirrhosis. Fibrosis progression occurred in 34% over a median of 4.9 years (IQR 3.2-7.6). Clinical outcomes occurred in 22% over a median of 4.4 years (IQR 3.2-7.6). Model performance for fibrosis progression was limited due to small sample size. The area under the receiver operating characteristic curve (AUROC) for 1 and 3-year risk of clinical outcomes was 0.78 (95% CI 0.73-0.83) and 0.76 (95% CI 0.69-0.81).
Accurate assessments for risk of clinical outcomes can be obtained using routinely collected data across a heterogeneous cohort of patients with CHC. These methods can be applied to predict risk of progression in other chronic liver diseases.
由于慢性肝病的疾病进展呈非线性，评估慢性肝病患者不良结局的风险一直具有挑战性。我们之前为晚期慢性丙型肝炎（CHC）患者开发了纤维化进展和临床结局的准确预测模型。本研究的主要目的是在一个异质性患者队列中验证纤维化进展和临床结局模型。
分析了成年CHC患者，这些患者有≥3年的随访时间，且在就诊时无肝失代偿、肝细胞癌（HCC）、肝移植（LT）、HBV或HIV合并感染（N = 1007）。结局包括：1）纤维化进展；2）肝失代偿；3）HCC；4）无LT生存。预测因素包括纵向临床和实验室数据。使用机器学习方法预测1年和3年的结局。
外部队列的中位年龄为49.4岁（四分位间距44.3 - 54.3）；61%为男性，80%为白人，79%为基因1型。就诊时，73%为初治患者，31%有肝硬化。在中位4.9年（四分位间距3.2 - 7.6）的时间里，34%的患者出现纤维化进展。在中位4.4年（四分位间距3.2 - 7.6）的时间里，22%的患者出现临床结局。由于样本量小，纤维化进展的模型性能有限。临床结局1年和3年风险的受试者工作特征曲线下面积（AUROC）分别为0.78（95%CI 0.73 - 0.83）和0.76（95%CI 0.69 - 0.81）。
通过对异质性CHC患者队列进行常规收集的数据，可以获得临床结局风险的准确评估。这些方法可用于预测其他慢性肝病的进展风险。
