Hepatology communications · 2024
Machine-learning methodologies to predict disease progression in chronic hepatitis B in Africa.
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Desalegn Hailemichael, Yang Xianchen, Yen Yi-Syuan, Berhe Nega, Kenney Brooke, Siwo Geoffrey H, Tang Weijing, Zhu Ji, Waljee Akbar K, Johannessen Asgeir
Аннотация
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.
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.