广东医学2023，Vol.44Issue(11)：1321-1327,7.DOI:10.13820/j.cnki.gdyx.20231432
基于机器学习构建强直性脊柱炎患者使用生物制剂不依从性临床预测模型
Construction of a clinical prediction model for non-adherence to biologic therapy in ankylosing spondylitis pa-tients based on machine learning
摘要
Abstract
Objective To develop a clinical prediction model for non-adherence to biologic therapy in ankylo-sing spondylitis(AS)patients.Methods A total of 201 confirmed AS patients treated in outpatient and inpatient de-partments from January 2020 to October 2022 were included in this study(220 cases were collected,and 19 were exclu-ded).Non-adherence was determined based on the proportion of treatment-covered days six months later.Feature vari-ables were selected using LASSO regression and support vector machines,and the intersection was taken.A multivariate logistic regression analysis was conducted to construct a clinical prediction model for non-adherence.The predictive abil-ity and clinical utility of the model were evaluated using the C-index,receiver operating characteristic(ROC)curve,calibration plot,and clinical decision curve.Adaboost and Lightgbm algorithms were used to validate the constructed bina-ry classification model,and ROC and PR curves were plotted to assess the model's prediction ability.An internal valida-tion set was constructed through internal sampling,and validation was performed using the C-index,calibration curve,and ROC curve.Results The study showed that the non-adherence rate to biologic therapy in AS was 46.8％.Machine learning results yielded six feature variables,including education level,monthly income,anxiety level,drug use frequen-cy,disease activity,and age,as factors for constructing the prediction model.The model had a C-index of 0.739,and the area under the ROC curve was 0.715.Decision curve analysis showed that the model could benefit approximately 90％of patients.Adaboost algorithm showed an area under the ROC curve of 0.643 and an area under the PR curve of 0.634,while the Lightgbm algorithm showed an area under the ROC curve of 0.633 and an area under the PR curve of 0.676.In-ternal validation results showed a C-index of 0.755 and an area under the ROC curve of 0.733.Conclusion The clini-cal prediction model for non-adherence to biologic therapy,based on six feature variables,demonstrates high predictive ability and practicality.It helps identify AS patients with poor adherence early on.关键词
强直性脊柱炎/临床预测模型/机器学习/不依从性/生物制剂Key words
ankylosing spondylitis/clinical prediction model/machine learning/nonadherence/biological agents分类
医药卫生引用本文复制引用
蔡旭,肖剑伟,郭粉莲,程钒钒,胡鑫玉,许楚花,陈泽建..基于机器学习构建强直性脊柱炎患者使用生物制剂不依从性临床预测模型[J].广东医学,2023,44(11):1321-1327,7.基金项目
广东省中医药管理局中医药科研项目(20221342) （20221342）
深圳市福田区卫生公益性科研项目(FTWS2021026,FT-WS2021063,FTWS2021064) （FTWS2021026,FT-WS2021063,FTWS2021064）
