Why the study?
Can a machine learning-based predictive model using inflammation biomarkers forecast long-term MACEs in patients with SCAD and intermediate coronary lesions?
Cohort (n=650)
Yes
Can a machine learning-based predictive model using inflammation biomarkers forecast long-term MACEs in patients with SCAD and intermediate coronary lesions?
Answers are grounded in this paper and cite their sources.
A machine learning-derived nomogram incorporating four clinical variables demonstrated moderate discriminative ability for predicting long-term MACEs in patients with SCAD and intermediate coronary lesions.
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.
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Xu et al. (2026) conducted a cohort in Stable coronary artery disease with intermediate coronary lesions (n=650). Machine learning-derived predictive model was evaluated on Major adverse cardiovascular events (MACEs) within 4 years (AUC 0.723, 95% CI 0.635-0.811).
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