ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Coronary Artery Disease
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1611709
This article is part of the Research TopicCoronary Artery Disease in Young Adults: Epidemiology, Clinical Insights and ManagementView all articles
Construction of diagnostic and prognostic models for premature coronary artery disease based on multiple machine algorithms
Provisionally accepted
- 1Liuzhou People's Hospital, Liuzhou, China
- 2Guangxi Medical University, Nanning, Guangxi Zhuang Region, China
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Objective: To evaluate the diagnostic and prognostic predictive value of the pan-immune-inflammation value (PIV) and triglyceride-glucose (TyG) index in premature coronary artery disease (PCAD). Methods: This study analyzed data from 26,883 patients admitted with chest pain at Liuzhou People's Hospital (January 2014 to December 2020), with 5,653 patients included after screening. Multiple machine learning algorithms, including Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Lasso regression, Random Forest (RF), and logistic regression, were applied to identify PCAD-related variables, which were integrated into a decision tree model. Propensity score matching (PSM) ensured cohort comparability. The Mime1 package facilitated ensemble feature selection and visualization, while optimal PIV and TyG cutoff values were determined via Receiver Operating Characteristic (ROC) analysis for 36-month survival subgroup analysis. Results: Logistic regression identified PIV (odds ratio [OR] 2.651, 95% CI [to be specified], P < 0.001) and TyG (OR 1.003, 95% CI [to be specified], P < 0.001) as PCAD risk factors. The decision tree model, incorporating PIV, TyG, and white blood cell count (WBC), achieved an accuracy of 0.88 and an area under the ROC curve (AUC) of 0.86 for PCAD diagnosis. Survival analysis over 36 months revealed that low PIV and TyG levels were associated with reduced all-cause mortality, whereas elevated levels correlated with poorer prognosis (P < 0.001), with TyG showing a pronounced effect. Conclusion: The combined evaluation of PIV, TyG, and WBC offers robust diagnostic and prognostic value for PCAD, with elevated PIV and TyG levels indicating a poor prognosis, underscoring their potential as clinical biomarkers.
Keywords: Premature coronary artery disease, Pan-Immune-Inflammation Value, Triglyceride-glucose index, machine learning, Diagnostic model, Prognostic model, survival analysis
Received: 14 Apr 2025; Accepted: 20 Oct 2025.
Copyright: © 2025 He, Li, Qin, Bi, Li, Liu and Miao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yanli Liu, gxlyl@126.com
Liu Miao, dr.miaoliu@qq.com
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