Hepatology research : the official journal of the Japan Society of Hepatology · 2023
Development and validation of machine learning model for predicting treatment responders in patients with primary biliary cholangitis.
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Kimura Naruhiro, Takahashi Kazuya, Setsu Toru, Horibata Yusuke, Kaneko Yusuke, Miyazaki Haruka, Ogawa Kohei, Kawata Yuzo, Sakai Norihiro, Watanabe Yusuke, Abe Hiroyuki, Kamimura Hiroteru, Sakamaki Akira, Yokoo Takeshi, Kamimura Kenya, Tsuchiya Atsunori, Terai Shuji
Аннотация
AIMS: Ursodeoxycholic acid is the first-line treatment for primary biliary cholangitis, and treatment response is one of the factors predicting the outcome. To prescribe alternative therapies, clinicians might need additional information before deciphering the treatment response to ursodeoxycholic acid, contributing to a better patient prognosis. In this study, we developed and validated machine learning (ML) algorithms to predict treatment responses using pretreatment data.
METHODS: This multicenter cohort study included collecting datasets from two data samples. Data 1 included 245 patients from 18 hospitals for ML development, and was divided into (i) training and (ii) development sets. Data 2 (iii: test set) included 51 patients from our hospital for validation. An extreme gradient boosted tree predicted the treatment response in the ML model. The area under the curve was used to evaluate the efficacy of the algorithm.
RESULTS: Data 1 showed that patients complying with the Paris II treatment response had significantly lower serum alkaline phosphatase and total bilirubin levels than those who did not respond. Three factors, total bilirubin, total protein, and alanine aminotransferase levels were selected as essential variables for prediction. Data 2 showed that patients complying with the Paris II criteria had significantly high prothrombin time and low total bilirubin levels. The area under the curve of extreme gradient boosted tree was good for (ii) (0.811) and (iii) (0.856).
CONCLUSIONS: We demonstrated the efficacy of ML in predicting the treatment response for patients with primary biliary cholangitis. Early identification of cases requiring additional treatment with our novel ML model may improve prognosis.
METHODS: This multicenter cohort study included collecting datasets from two data samples. Data 1 included 245 patients from 18 hospitals for ML development, and was divided into (i) training and (ii) development sets. Data 2 (iii: test set) included 51 patients from our hospital for validation. An extreme gradient boosted tree predicted the treatment response in the ML model. The area under the curve was used to evaluate the efficacy of the algorithm.
RESULTS: Data 1 showed that patients complying with the Paris II treatment response had significantly lower serum alkaline phosphatase and total bilirubin levels than those who did not respond. Three factors, total bilirubin, total protein, and alanine aminotransferase levels were selected as essential variables for prediction. Data 2 showed that patients complying with the Paris II criteria had significantly high prothrombin time and low total bilirubin levels. The area under the curve of extreme gradient boosted tree was good for (ii) (0.811) and (iii) (0.856).
CONCLUSIONS: We demonstrated the efficacy of ML in predicting the treatment response for patients with primary biliary cholangitis. Early identification of cases requiring additional treatment with our novel ML model may improve prognosis.