1From the University of Michigan Health System, Department of Internal Medicine, Division of Gastroenterology, Ann Arbor, Michigan, USA.
Existing predictive models of risk of disease progression in chronic hepatitis C (CHC) have limited accuracy. The aim of this study was to improve upon existing models by applying novel statistical methods that incorporate longitudinal data. Patients in the Hepatitis C Antiviral Long-term Treatment Against Cirrhosis (HALT-C) trial were analyzed. Outcomes of interest were: 1) fibrosis progression (increase of ≥2 Ishak stages) and 2) liver-related clinical outcomes (liver-related death, hepatic decompensation, hepatocellular carcinoma, liver transplant, or increase in Child-Turcotte-Pugh score to ≥7). Predictors included longitudinal clinical, laboratory, and histologic data. Models were constructed using logistic regression (LR), and two machine learning (ML) methods [random forest (RF) and boosting] to predict an outcome in the next 12 months. The control arm was used as the training dataset (n= 349 clinical; n=184 fibrosis) and the interferon arm for internal validation. The area under the receiver operating characteristic curve (AUROC) for longitudinal models of fibrosis progression was: 0.78 (95%CI 0.74-0.83) using LR, 0.79 (95%CI 0.77-0.81) using RF, and 0.79 (95%CI 0.77-0.82) using boosting. The AUROC for longitudinal models of clinical progression was: 0.79 (95%CI 0.77-0.82) using LR, 0.86 (95%CI 0.85-0.87) using RF, and 0.84 (95%CI 0.82-0.86) using boosting. Longitudinal models outperformed baseline models for both outcomes (p<0.0001). Longitudinal ML models had negative predictive values of 94% for both outcomes. Conclusions: Prediction models that incorporate longitudinal data can capture the non-linear disease progression in CHC and thus outperform baseline models. ML methods can capture complex relationships between predictors and outcomes, yielding more accurate predictions. Our models can help target costly therapies to patients with most urgent need, guide intensity of clinical monitoring required, and provide prognostic information to patients.