中国中西医结合杂志2026，Vol.46Issue(3)：268-275,8.DOI:10.7661/j.cjim.20251224.289
心肾代谢综合征冠状动脉狭窄中西医结合智能评估模型
Integrated Chinese and Western Medicine Intelligent Assessment Model for Coronary Stenosis in Cardiovascular-Kidney-Metabolic Syndrome
摘要
Abstract
Objective To develop an optimized assessment model for coronary artery stenosis in patients with cardiovascular-kidney-metabolic(CKM)syndrome using the Tabular Prior-Data Fitted Network(TabPFN),thereby providing support for clinical decision-making.Methods Based on a cross-sectional study design,229 hospitalized patients were enrolled from two campuses of Jiangsu Province Hospital of Chinese Medicine,including 178 cases from the Main Campus and 51 cases from the Zidong Branch.Data from the Main Campus were randomly assigned to a training set(123 cases)and an internal validation set(55 cases)at a 7∶3 ratio,while data from the Zidong Branch served as the external validation set(51 cases).Risk factors were screened through integrated regression analyses,and models were constructed using the TabPFN and eight conventional machine learning techniques.Performance metrics,calibration curves,decision curves,and learning curves were compared in a multidimensional manner to conduct model selection,validation,and generalizability assessment.Finally,a local Shiny calculator was developed.Results Through stepwise regression and ensemble least absolute shrinkage and selection operator(LASSO)regression,a total of 8 risk factors were identified,including Chinese Medicine pathogenic elements(stagnation,phlegm,stasis,deficiency),coronary artery computed tomography angiography(CTA)findings,duration of diabetes,lymphocyte count,and low-density lipoprotein cholesterol(LDL-C).After multi-dimensional comparisons,TabPFN demonstrated superior comprehensive performance in small-sample settings compared to traditional models,and was selected as the optimal model.The model achieved area under the receiver operating characteristic curve(AUC)of 0.994(95％CI:0.984～1.000)in the training set,0.933(95％CI:0.862～1.000)in internal validation,and 0.902(95％CI:0.820～0.984)in external validation.Calibration curves indicated high predictive consistency,decision curve analysis confirmed clinical utility across all threshold probabilities,and the learning curve suggested strong feature learning and generalization capabilities.Compared with assessment based on CTA alone,the optimal model reduced misdiagnosis rate by 28.0％and missed diagnoses rate by 14.5％.Permutation feature importance analysis ranked the predictors in descending order of importance as follows:stasis,CTAfindings,lymphocyte count,LDL-C,stagnation,deficiency,duration of diabetes,phlegm.Conclusions TabPFN exhibits promising potential for small-sample medical data analysis.The developed Chinese-Western medicine multimodal assessment model demonstrates robust efficacy,offering an optimized assessment tool for coronary stenosis in CKM patients.关键词
表格先验数据拟合网络/多模态数据/心肾代谢综合征/冠状动脉狭窄/中西医结合/评估模型/本地Shiny计算器/人工智能Key words
TabPFN/multimodal data/cardiovascular-kidney-metabolic syndrome/coronary artery stenosis/integrative medicine/assessment model/local shiny calculator/artificial intelligence引用本文复制引用
朱时典,刘滟琳,刘彦孜,卜文玉,刘福明..心肾代谢综合征冠状动脉狭窄中西医结合智能评估模型[J].中国中西医结合杂志,2026,46(3):268-275,8.基金项目
江苏省卫生健康委员会重点项目(No.ZD2022001) （No.ZD2022001）
江苏省重点研发计划-社会发展面上项目(No.BE2020683) （No.BE2020683）
江苏省"六大人才高峰"创新人才团队项目(No.TD-SWYY-069) （No.TD-SWYY-069）
