预测非洲慢性乙型肝炎疾病进展的机器学习方法。
Machine-learning methodologies to predict disease progression in chronic hepatitis B in Africa.
作者信息
Desalegn Hailemichael, Yang Xianchen, Yen Yi-Syuan, Berhe Nega, Kenney Brooke, Siwo Geoffrey H, Tang Weijing, Zhu Ji, Waljee Akbar K, Johannessen Asgeir
机构信息
Medical Department, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia.
Department of Infectious Diseases, Vestfold Hospital Trust, Tønsberg, Norway.
出版信息
Hepatol Commun. 2024 Nov 15;8(12). doi: 10.1097/HC9.0000000000000584. eCollection 2024 Dec 1.
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.
背景
关于非洲慢性乙型肝炎病毒（HBV）感染患者疾病进展的决定因素知之甚少。
方法
我们使用具有纵向数据的机器学习模型，在一组特征明确的无基线肝纤维化的埃塞俄比亚HBV感染患者队列中建立预测算法。疾病进展定义为肝硬度增加至>7.9 kPa或根据符合资格标准开始治疗。
结果
551例患者中有24例（4.4%）在中位随访时间69个月后出现疾病进展。基于现有实验室检查（标准血液学和生物化学检查）组合的随机森林模型显示出最佳预测性能，曲线下面积（AUROC）范围为0.82至0.88。
结论
我们得出结论，基于简单且可用的实验室检查的综合指标具有良好的预测性能，应在更大的HBV队列中进一步探索。
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