# Novel lipid parameters for predicting and interpreting the severity of coronary artery lesions in premature coronary artery disease

## TL;DR

This study identifies new lipid markers that predict the severity of coronary artery disease in younger patients and creates a predictive model for clinical use.

## Contribution

The study introduces novel lipid parameters and develops a nomogram model for predicting coronary lesion severity in premature coronary artery disease.

## Key findings

- Lp(a), non-HDL-C, RC, FFA, and BAR are positively correlated with coronary lesion severity.
- A nomogram model using these lipid parameters and patient sex shows strong predictive accuracy (AUC 0.815–0.839).
- The model demonstrates good discrimination, calibration, and clinical utility for identifying severe stenosis in pCAD patients.

## Abstract

To evaluate the predictive value of novel lipid parameters for coronary lesion severity in pCAD and to develop a nomogram-based prediction model.

Patients newly diagnosed with pCAD at Qingdao Municipal Hospital (2021–2024) were enrolled and randomly assigned to training and validation cohorts in a 7:3 ratio. Coronary lesion severity was assessed using the Gensini score (GS), with patients stratified into mild or significant stenosis groups. Spearman correlation analysis was performed between GS and lipid parameters. Key predictors were selected using LASSO regression, and independent risk factors were identified by multivariable logistic regression to construct the nomogram model. The model's discrimination, calibration, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

Lp(a), non-HDL-C, RC, FFA, and BAR were positively correlated with GS (r = 0.34, 0.34, 0.18, 0.19, 0.18; all P < 0.01). Lp(a) (OR = 1.03, P < 0.05), male sex (OR = 2.22, P < 0.05), FFA (OR = 2.40, P < 0.05), and non-HDL-C (OR = 2.07, P < 0.05) were independent risk factors for significant coronary artery stenosis. The nomogram model developed based on these variables demonstrated excellent predictive performance, with AUC values of 0.815 and 0.839 in the training and validation cohorts, respectively (P < 0.001).

The proposed nomogram provides an effective tool for identifying pCAD patients with severe coronary artery stenosis, demonstrating robust predictive accuracy and potential clinical utility.