Machine-learning methodologies to predict disease progression in chronic hepatitis B in Africa.

Desalegn H, Yang X, Yen YS, Berhe N, Kenney B, Siwo GH, Tang W, Zhu J, Waljee AK, Johannessen A. Machine-learning methodologies to predict disease progression in chronic hepatitis B in Africa. Hepatology communications. 2024 Dec 1; 8(12):DOI: 10.1097/HC9.0000000000000584.

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Abstract:

BACKGROUND: Little is known about the determinants of disease progression among African patients with chronic HBV infection. METHODS: We used machine-learning models with longitudinal data to establish predictive algorithms in a well-characterized cohort of Ethiopian HBV-infected patients without baseline liver fibrosis. Disease progression was defined as an increase in liver stiffness to > 7.9 kPa or initiation of treatment based on meeting the eligibility criteria. RESULTS: Twenty-four of 551 patients (4.4%) experienced disease progression after a median follow-up time of 69 months. A random forest model based on a combination of available laboratory tests (standard hematology and biochemistry) demonstrated the best predictive properties with the AUROC ranging from 0.82 to 0.88. CONCLUSION: We conclude that combined metrics based on simple and available laboratory tests had good predictive properties and should be explored further in larger HBV cohorts.