Lipoproteins Predicting Coronary Lesion Complexity in Premature Coronary Artery Disease: A Supervised Machine Learning Approach

Marta Marcinkowska Agnieszka Kuchta Tomasz Figatowski Piotr Kasprzyk Radosław Targoński Wojciech Sobiczewski Miłosz Jaguszewski Marcin Fijalkowski Marcin Gruchała Agnieszka Mickiewicz ^*

• Medical University of Gdansk, Gdańsk, Poland

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). 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. The Lp(a) levels among patients with SS ≥ 23 and with SS 1–22 were significantly higher than those with SS = 0 (p = 0.021 and p = 0.027, respectively). The cut-off point for the Lp(a) level of 63.5 mg/dl discriminated subjects with SS ≥ 23 from those with SS ≤ 22 (sensitivity 0.546, specificity 0.780; AUC 0.620; p = 0.027). An LDA-based model involving the Lp(a) level, age, sex and LDL-C provided improved discrimination performance (sensitivity 0.727, specificity 0.733, AUC 0.800; p = 0.0001). 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.