ArticleFrontiers in cardiovascular medicine2025
Lipoproteins predicting coronary lesion complexity in premature coronary artery disease: a supervised machine learning approach.
Article in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.Journal of cardiovascular imaging · 2026Review
- Lipoprotein(a), Coronary Complexity, and Stent-Related Outcomes: Meta-Analytic Insights for the Interventional Cardiologist.Journal of clinical medicine · 2026Review
- Laboratory-derived inflammatory ratios as a novel diagnostic model for premature coronary artery disease.Frontiers in endocrinology · 2025Article
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Authors and funding
11 authors.
Funding
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Abstract
Introduction: We aimed to assess the usefulness of lipoprotein(a) [Lp(a)] and LDL-C levels as potential predictors of coronary lesions' complexity in patients with premature coronary artery disease (pCAD). Methods: This study enrolled 162 consecutive patients with pCAD undergoing coronary angiography. The SYNTAX score (SS) was used to assess coronary lesions' complexity. Linear discriminant analysis (LDA) was employed to construct a multivariate classification model enabling the prediction of coronary lesions' complexity in SS. Results: The Lp(a) levels among patients with SS ≥ 23 and with SS 1-22 were significantly higher than those with SS = 0 ( Conclusions: Lp(a) levels in pCAD patients are associated with the advancement of coronary artery lesions in SS patients. An Lp(a) level of 63.5 mg/dl can be the cut-off point for the identification of subjects with SS ≥ 23. LDA-based modelling using Lp(a), LDL-C, age and gender may be an applicable tool for the preliminary identification of patients at risk of more complex coronary artery lesions.
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