Development of an optimized risk evaluation system for cardiovascular-kidney-metabolic syndrome-associated coronary heart disease based on tabular prior-data fitted network

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Background The innovative concept of cardiovascular-kidney-metabolic (CKM) syndrome and tabular prior-data fitted network (TabPFN) offers opportunities for optimizing coronary heart disease (CHD) risk evaluation. This study compared TabPFN with traditional machine learning (ML) methods in medical small-sample data, aiming to construct and validate a risk model for coronary stenosis in CKM–CHD patients. Methods The research strictly adheres to transparent reporting of a multivariable prediction model for individual prognosis or diagnosis + artificial intelligence (TRIPOD + AI). A total of 296 inpatients from the Main Campus of Jiangsu Province Hospital of Chinese Medicine (June 2023–August 2024) and Zidong Branch (June 2024–December 2024) were screened. The data of the Main Campus were randomly divided into a training set ( n = 160) and an internal validation set ( n = 54) according to a ratio of 3:1, and the data of Zidong Branch were used as an external validation set ( n = 82). Integrated least absolute shrinkage and selection operator regression was used to screen risk factors. TabPFN and eight traditional ML algorithms were applied to build models, which were evaluated by common indicators, calibration curves, decision curves, and learning curves. Finally, a local Shiny calculator was built. Results Five risk factors were identified: coronary computed tomography angiography, Type 2 diabetes mellitus, triglyceride-glucose index, body mass index, and absolute lymphocyte count. TabPFN outperformed traditional models in small samples, with area under the receiver operating characteristic curve (AUC) values of 0.922 (95% confidence interval [CI]: 0.886–0.958) in the training set, 0.857 (95% CI: 0.733–0.981) in the internal validation set, and 0.815 (95% CI: 0.711–0.918) in the external validation set. The best model reduced the false-negative rate of CCTA by 4.9% (95% CI: 1.9%−8.1%), and a user-friendly Shiny calculator was deployed. Conclusion TabPFN shows promise in medical small-sample analysis, and the optimized CKM–CHD risk model offers a certain degree of support for clinical decision-making. However, future larger-sample, multicenter prospective studies are still needed to further optimize the model.

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