Development and Validation of a Clinical Nomogram for Predicting Angiographic Progression of Coronary Heart Disease

The heterogeneous nature of coronary artery disease (CAD) progression significantly contributes to cardiovascular morbidity and mortality. Accurately identifying patients at high risk for progression is paramount for effective secondary prevention. This study aimed to develop and internally validate a novel clinical nomogram to precisely predict the probability of angiographic progression in patients with established CAD.

This retrospective case-referent study enrolled 333 patients with established CAD who underwent at least two coronary angiograms at Beijing Friendship Hospital between January 2018 and December 2023. The cohort comprised 222 patients with angiographic progression and 111 referents without progression, matched at a 2:1 ratio to minimize potential bias. A comprehensive set of 146 pre-selected potential predictors was systematically collected. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for robust initial variable selection, followed by univariable and multivariable logistic regression to identify independent risk factors, which were subsequently integrated into the nomogram. Model performance was rigorously evaluated through assessment of discrimination (area under the receiver operating characteristic curve, AUC), calibration (calibration curve and Hosmer-Lemeshow test), and clinical utility (decision curve analysis, DCA). Internal validation was conducted using a non-parametric bootstrap resampling technique to mitigate optimism and provide robust performance estimates.

Seven independent predictors of angiographic progression were identified: a shorter interval between angiograms (odds ratio OR 0.99, 95% CI 0.99-1.00, p=0.015), a higher preoperative Gensini score (OR 1.08, 95% CI 1.05-1.10, p<0.001), male gender (vs. female, OR 3.31, 95% CI 1.69-6.45, p<0.001), the absence of a prior history of stroke (vs. presence of stroke, OR 0.16, 95% CI 0.03-0.79, p=0.025), elevated low density lipoprotein cholesterol (LDL C) levels (OR 2.32, 95% CI 1.46-3.71, p<0.001), higher glycated hemoglobin (HbA1c) levels (OR 1.52, 95% CI 1.12-2.05, p=0.006), and increased antithrombin III levels (OR 1.04, 95% CI 1.01-1.07, p=0.008). The developed nomogram demonstrated promising discrimination (apparent AUC 0.88, 95% confidence interval CI 0.84-0.92) and superior calibration (Hosmer-Lemeshow test p=0.990) within the internal validation cohort. Decision curve analysis indicated that the nomogram offered a statistically and clinically significant net benefit across a range of threshold probabilities (5-80%).

We successfully developed and internally validated a novel nomogram integrating seven readily available clinical and laboratory variables to accurately predict angiographic progression in CAD. This tool provides an individualized, quantitative risk assessment with promising predictive performance for risk stratification. Nonetheless, its ultimate clinical utility and broader generalizability mandate rigorous external validation in diverse patient cohorts.