A decision-making model for prediction of a stable disease course in chronic hepatitis B patients

Abstract

Abstract Patients with chronic hepatitis B (CHB) are regularly monitored for HBV DNA and liver enzymes in order to assess disease progression and the need for antiviral therapy. Identifying patients with a stable course of disease can potentially prolong the intervals between visits, withhold unnecessary tests and save money. Accordingly, we aimed to find predictors for a stable disease course in patients with CHB. 579 patients with CHB, who were followed in a tertiary referral center between January 2004–December 2018, were retrospectively analyzed. Patients with low and steady viral load titer (< 2000 IU/ml) and normal ALT levels (< 40 IU/ml) in 6 consecutive clinic encounters were considered to have a stable course of CHB. A stepwise multivariate logistic regression analysis and a decision tree model were used to identify predictors of a stable disease course. Following exclusion of ineligible patients, a total of 220 patients were included in the final analysis. 64/220 patients had a stable disease course. Patients with a stable disease were older (62.99 ± 12.36 Vs. 54.07 ± 13.64, p < 0.001) with a higher percentage of women (53% vs. 38%) and had lower baseline levels of AST, ALT and viral load (VL). In a multivariate analysis, age (OR 0.94, 95% CI 0.91–0.98), baseline ALT (OR 1.06, 95% CI 1.01–1.1) and VL (OR 1.05 95% CI 1.02–1.08), were significantly associated with a stable disease. In a decision tree model, patients 46–67 years old, with baseline VL < 149 IU/mL and ALT < 40 IU/mL had the best probability (91%) for a stable disease course over 4.4 ± 2.2 years. We conclude that integrating patients’ age with baseline VL and ALT can predict a stable disease course in patients with CHB off treatment.