ORIGINAL RESEARCH article
Front. Physiol.
Sec. Clinical and Translational Physiology
Machine learning-selected inflammation biomarkers for stable coronary artery disease with intermediate coronary lesions: potential for long-term prognosis in a multicenter cohort study
Provisionally accepted
- 1Department of Orthopedics, the First Affiliated Hospital of Bengbu Medical University, Bengbu, China
- 2Wuhan University Renmin Hospital, Wuhan, China
- 3Anhui Medical University, Hefei, China
- 4Xiangyang Central Hospital, Xiangyang, China
- 5The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
- 6The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
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Background: Stable coronary artery disease (SCAD) generally exhibits prolonged periods of stability. However, this condition can unpredictably progress into an unstable state, representing a complex pathological process involving multiple contributing factors. Thus, we aimed to utilize machine-learning techniques to identify predictive features from electronic health record (EHR) data for forecasting the long-term prognosis of patients with SCAD and intermediate coronary lesions. Methods: Patients were divided into a training cohort (n=403) and an external validation cohort (n=247) according to their hospital of origin during the period from January 2018 to December 2020. Predictive features were determined using LASSO regression analysis and boruta algorithm, followed by multivariate Cox regression analysis for model construction. Results: The developed predictive model comprised four clinical variables: platelet-to-lymphocyte ratio, diabetes mellitus, lipoprotein(a), and mean platelet width. The area under the curve for predicting major adverse cardiovascular events (MACEs) within 2-, 3- and 4-year in the development cohort was 0.692 (95%CI :0.59-0.793), 0.709 (95%CI :0.625-0.792) and 0.743 (95%CI:0.672-0.813), respectively, while that in the external validation cohort was 0.658 (95%CI 0.542-0.773), 0.681 (95%CI :0.579-0.782) and 0.723 (95%CI: 0.635-0.811), respectively. Additionally, the developed predictive model was calibrated by analyzing the correlation between expected and observed MACEs in the development and external validation cohorts. Lastly, the clinical value of the developed predictive model was confirmed via decision curve analysis. Conclusion: Our validated nomogram was based on inflammation biomarkers and EHR data, demonstrating moderate discriminative ability to detect individuals at high risk of poor outcome among patients with SCAD and angiographically intermediate coronary stenosis.
Keywords: Inflammation biomarkers, intermediate coronary lesions, Machine l earning, Prediction nomogram, Stable coronary artery disease
Received: 18 Aug 2025; Accepted: 30 Jan 2026.
Copyright: © 2026 Xu, Duan, Liu, Li, Zuo, Zhu and Wang. 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: Jun Wang
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