Nomogram model for predicting multivessel disease in coronary artery disease using cardiac function parameters and clinical features.

Background: Multivessel disease (MVD) represents a severe phenotype of coronary artery disease and is associated with poor prognosis. Early, non-invasive identification of MVD remains a clinical challenge. This study aimed to develop and validate a nomogram integrating cardiac function parameters and clinical characteristics for individualized prediction of MVD risk. Methods: Clinical data were retrospectively collected from 353 patients with angiographically confirmed coronary artery disease at Guangzhou Red Cross Hospital between January 2023 and December 2024. Patients were randomly assigned to a training set (70%) and an internal validation set (30%). Least absolute shrinkage and selection operator (LASSO) regression was used to screen potential risk factors, followed by multivariate logistic regression to construct the predictive model and generate the nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results: Seven predictors were ultimately included in the nomogram: age, gender, prior stent implantation, total cholesterol, left ventricular ejection fraction (LVEF), high-density lipoprotein, and albumin. The model exhibited good discrimination, with an AUC of 0.84 (95% CI [0.79-0.90]) in the training set and 0.79 (95% CI [0.68-0.89]) in the validation set. Accuracy was 0.81 in both datasets. Calibration curves and decision curve analysis demonstrated good predictive accuracy and clinical utility of the nomogram. Furthermore, the nomogram scores successfully stratified patients into high-risk (≥191) and low-risk (<191) groups, with significantly different score distributions between the MVD and non-MVD groups (