Ze Yu , Fang Kou , Ya Gao , Fei Gao , Chun-ming Lyu , Hai Wei
{"title":"基于真实世界研究的类风湿关节炎患者正清风痛宁所致肝功能异常的机器学习模型","authors":"Ze Yu , Fang Kou , Ya Gao , Fei Gao , Chun-ming Lyu , Hai Wei","doi":"10.1016/j.joim.2024.12.001","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’ quality of life. Zhengqing Fengtongning (ZF) is a traditional Chinese medicine preparation used to treat RA. ZF may cause liver injury. In this study, we aimed to develop a prediction model for abnormal liver function caused by ZF.</div></div><div><h3>Methods</h3><div>This retrospective study collected data from multiple centers from January 2018 to April 2023. Abnormal liver function was set as the target variable according to the alanine transaminase (ALT) level. Features were screened through univariate analysis and sequential forward selection for modeling. Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.</div></div><div><h3>Results</h3><div>This study included 1,913 eligible patients. The LightGBM model exhibited the best performance (accuracy = 0.96) out of the 10 learning models. The predictive metrics of the LightGBM model were as follows: precision = 0.99, recall rate = 0.97, F1_score = 0.98, area under the curve (AUC) = 0.98, sensitivity = 0.97 and specificity = 0.85 for predicting ALT < 40 U/L; precision = 0.60, recall rate = 0.83, F1_score = 0.70, AUC = 0.98, sensitivity = 0.83 and specificity = 0.97 for predicting 40 ≤ ALT < 80 U/L; and precision = 0.83, recall rate = 0.63, F1_score = 0.71, AUC = 0.97, sensitivity = 0.63 and specificity = 1.00 for predicting ALT ≥ 80 U/L. ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels, the combination of TNF-α inhibitors, JAK inhibitors, methotrexate + nonsteroidal anti-inflammatory drugs, leflunomide, smoking, older age, and females in middle-age (45–65 years old).</div></div><div><h3>Conclusion</h3><div>This study developed a model for predicting ZF-induced abnormal liver function, which may help improve the safety of integrated administration of ZF and Western medicine.</div><div>Please cite this article as: Yu Z, Kou F, Gao Y, Lyu CM, Gao F, Wei H. A machine learning model for predicting abnormal liver function induced by a Chinese herbal medicine preparation (Zhengqing Fengtongning) in patients with rheumatoid arthritis based on real-world study. <em>J Integr Med</em>. 2025; 23(1): 25–35.</div></div>","PeriodicalId":48599,"journal":{"name":"Journal of Integrative Medicine-Jim","volume":"23 1","pages":"Pages 25-35"},"PeriodicalIF":4.2000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A machine learning model for predicting abnormal liver function induced by a Chinese herbal medicine preparation (Zhengqing Fengtongning) in patients with rheumatoid arthritis based on real-world study\",\"authors\":\"Ze Yu , Fang Kou , Ya Gao , Fei Gao , Chun-ming Lyu , Hai Wei\",\"doi\":\"10.1016/j.joim.2024.12.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’ quality of life. Zhengqing Fengtongning (ZF) is a traditional Chinese medicine preparation used to treat RA. ZF may cause liver injury. In this study, we aimed to develop a prediction model for abnormal liver function caused by ZF.</div></div><div><h3>Methods</h3><div>This retrospective study collected data from multiple centers from January 2018 to April 2023. Abnormal liver function was set as the target variable according to the alanine transaminase (ALT) level. Features were screened through univariate analysis and sequential forward selection for modeling. Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.</div></div><div><h3>Results</h3><div>This study included 1,913 eligible patients. The LightGBM model exhibited the best performance (accuracy = 0.96) out of the 10 learning models. The predictive metrics of the LightGBM model were as follows: precision = 0.99, recall rate = 0.97, F1_score = 0.98, area under the curve (AUC) = 0.98, sensitivity = 0.97 and specificity = 0.85 for predicting ALT < 40 U/L; precision = 0.60, recall rate = 0.83, F1_score = 0.70, AUC = 0.98, sensitivity = 0.83 and specificity = 0.97 for predicting 40 ≤ ALT < 80 U/L; and precision = 0.83, recall rate = 0.63, F1_score = 0.71, AUC = 0.97, sensitivity = 0.63 and specificity = 1.00 for predicting ALT ≥ 80 U/L. 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A machine learning model for predicting abnormal liver function induced by a Chinese herbal medicine preparation (Zhengqing Fengtongning) in patients with rheumatoid arthritis based on real-world study. <em>J Integr Med</em>. 2025; 23(1): 25–35.</div></div>\",\"PeriodicalId\":48599,\"journal\":{\"name\":\"Journal of Integrative Medicine-Jim\",\"volume\":\"23 1\",\"pages\":\"Pages 25-35\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Integrative Medicine-Jim\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2095496424004126\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INTEGRATIVE & COMPLEMENTARY MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Integrative Medicine-Jim","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2095496424004126","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INTEGRATIVE & COMPLEMENTARY MEDICINE","Score":null,"Total":0}
A machine learning model for predicting abnormal liver function induced by a Chinese herbal medicine preparation (Zhengqing Fengtongning) in patients with rheumatoid arthritis based on real-world study
Objective
Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’ quality of life. Zhengqing Fengtongning (ZF) is a traditional Chinese medicine preparation used to treat RA. ZF may cause liver injury. In this study, we aimed to develop a prediction model for abnormal liver function caused by ZF.
Methods
This retrospective study collected data from multiple centers from January 2018 to April 2023. Abnormal liver function was set as the target variable according to the alanine transaminase (ALT) level. Features were screened through univariate analysis and sequential forward selection for modeling. Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.
Results
This study included 1,913 eligible patients. The LightGBM model exhibited the best performance (accuracy = 0.96) out of the 10 learning models. The predictive metrics of the LightGBM model were as follows: precision = 0.99, recall rate = 0.97, F1_score = 0.98, area under the curve (AUC) = 0.98, sensitivity = 0.97 and specificity = 0.85 for predicting ALT < 40 U/L; precision = 0.60, recall rate = 0.83, F1_score = 0.70, AUC = 0.98, sensitivity = 0.83 and specificity = 0.97 for predicting 40 ≤ ALT < 80 U/L; and precision = 0.83, recall rate = 0.63, F1_score = 0.71, AUC = 0.97, sensitivity = 0.63 and specificity = 1.00 for predicting ALT ≥ 80 U/L. ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels, the combination of TNF-α inhibitors, JAK inhibitors, methotrexate + nonsteroidal anti-inflammatory drugs, leflunomide, smoking, older age, and females in middle-age (45–65 years old).
Conclusion
This study developed a model for predicting ZF-induced abnormal liver function, which may help improve the safety of integrated administration of ZF and Western medicine.
Please cite this article as: Yu Z, Kou F, Gao Y, Lyu CM, Gao F, Wei H. A machine learning model for predicting abnormal liver function induced by a Chinese herbal medicine preparation (Zhengqing Fengtongning) in patients with rheumatoid arthritis based on real-world study. J Integr Med. 2025; 23(1): 25–35.
期刊介绍:
The predecessor of JIM is the Journal of Chinese Integrative Medicine (Zhong Xi Yi Jie He Xue Bao). With this new, English-language publication, we are committed to make JIM an international platform for publishing high-quality papers on complementary and alternative medicine (CAM) and an open forum in which the different professions and international scholarly communities can exchange views, share research and their clinical experience, discuss CAM education, and confer about issues and problems in our various disciplines and in CAM as a whole in order to promote integrative medicine.
JIM is indexed/abstracted in: MEDLINE/PubMed, ScienceDirect, Emerging Sources Citation Index (ESCI), Scopus, Embase, Chemical Abstracts (CA), CAB Abstracts, EBSCO, WPRIM, JST China, Chinese Science Citation Database (CSCD), and China National Knowledge Infrastructure (CNKI).
JIM Editorial Office uses ThomsonReuters ScholarOne Manuscripts as submitting and review system (submission link: http://mc03.manuscriptcentral.com/jcim-en).
JIM is published bimonthly. Manuscripts submitted to JIM should be written in English. Article types include but are not limited to randomized controlled and pragmatic trials, translational and patient-centered effectiveness outcome studies, case series and reports, clinical trial protocols, preclinical and basic science studies, systematic reviews and meta-analyses, papers on methodology and CAM history or education, conference proceedings, editorials, commentaries, short communications, book reviews, and letters to the editor.
Our purpose is to publish a prestigious international journal for studies in integrative medicine. To achieve this aim, we seek to publish high-quality papers on any aspects of integrative medicine, such as acupuncture and traditional Chinese medicine, Ayurveda medicine, herbal medicine, homeopathy, nutrition, chiropractic, mind-body medicine, taichi, qigong, meditation, and any other modalities of CAM; our commitment to international scope ensures that research and progress from all regions of the world are widely covered. These ensure that articles published in JIM have the maximum exposure to the international scholarly community.
JIM can help its authors let their papers reach the widest possible range of readers, and let all those who share an interest in their research field be concerned with their study.