Annals of hepatology · 2024
An exploratory machine learning model for predicting advanced liver fibrosis in autoimmune hepatitis patients: A preliminary study.
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Wei Qinglin, Li Wen, He Shubei, Wu Hongbo, Xie Qiaoling, Peng Ying, Zhang Xingyue
Аннотация
INTRODUCTION AND OBJECTIVES: Advanced fibrosis is a crucial stage in the progression of autoimmune hepatitis (AIH), where fibrosis can either regress or advance. This study aims to leverage machine learning (ML) models for the assessment of advanced liver fibrosis in AIH patients using routine clinical features.
PATIENTS AND METHODS: A total of 233 patients diagnosed with AIH and underwent liver biopsy were included in the discovery cohort. The dataset was randomly split into training and testing sets. Patients were categorized into groups with no/minimal/moderate fibrosis and advanced fibrosis. Six ML models were employed to identify the optimal model. Subsequently, the predictive capability of the best ML model was validated in an additional cohort (n = 33) and compared with conventional noninvasive fibrosis scores.
RESULTS: Three key clinical features, including prothrombin time (PT), albumin (ALB), and ultrasound spleen thickness (UTST), were analyzed by least absolute shrinkage and selection operator (LASSO) regression. In the training set, the random forest (RF) model showed the highest diagnostic performance in predicting advanced fibrosis stage (AUC=0.951). In the testing cohort and validation cohort, the RF model maintained high accuracy (AUC = 0.863 and AUC = 0.843). Additionally, the random forest model outperformed the conventional noninvasive fibrosis scores.
CONCLUSIONS: ML models, particularly the RF model, can help improve the discrimination of advanced liver fibrosis in patients with AIH.
PATIENTS AND METHODS: A total of 233 patients diagnosed with AIH and underwent liver biopsy were included in the discovery cohort. The dataset was randomly split into training and testing sets. Patients were categorized into groups with no/minimal/moderate fibrosis and advanced fibrosis. Six ML models were employed to identify the optimal model. Subsequently, the predictive capability of the best ML model was validated in an additional cohort (n = 33) and compared with conventional noninvasive fibrosis scores.
RESULTS: Three key clinical features, including prothrombin time (PT), albumin (ALB), and ultrasound spleen thickness (UTST), were analyzed by least absolute shrinkage and selection operator (LASSO) regression. In the training set, the random forest (RF) model showed the highest diagnostic performance in predicting advanced fibrosis stage (AUC=0.951). In the testing cohort and validation cohort, the RF model maintained high accuracy (AUC = 0.863 and AUC = 0.843). Additionally, the random forest model outperformed the conventional noninvasive fibrosis scores.
CONCLUSIONS: ML models, particularly the RF model, can help improve the discrimination of advanced liver fibrosis in patients with AIH.