ArticleDigital health
Development of an optimized risk evaluation system for cardiovascular-kidney-metabolic syndrome-associated coronary heart disease based on tabular prior-data fitted network.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- The Associations of Anthropometric Indices With Stages and Mortality in Cardiovascular-Kidney-Metabolic Syndrome: Insights From NHANES.Reviews in cardiovascular medicine · 2026Article
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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 ( 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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