Predictive nomogram for abdominal aortic calcification risk: a multi-algorithm approach integrating demographic and clinical variables

Abdominal aortic calcification (AAC), a significant predictor of cardiovascular events and mortality, lacks a simple predictive tool for early risk stratification. This study aimed to develop and validate a clinical nomogram for predicting AAC risk using accessible demographic, comorbidity, and lifestyle factors. Utilizing data from the 2013–2014 National Health and Nutrition Examination Survey (NHANES) cohort, AAC was assessed via dual-energy X-ray absorptiometry (DXA) using the Kauppila scoring system (score > 0 defined AAC). Participants were stratified into training ( n = 2,198) and validation ( n = 942) cohorts. The nomogram was developed through multi-algorithm consensus, incorporating predictors selected by three robust methodologies: Least Absolute Shrinkage and Selection Operator (LASSO) regression, the best subsets regression (BSR) method and the Boruta algorithm. Model performance was evaluated through ROC, calibration, and decision curve analysis (DCA) curves. Five predictors—age, BMI, cardiovascular disease (CVD), hypertension, and smoking status—were identified. The nomogram demonstrated robust discrimination, with AUCs of 0.747 (95% CI: 0.724–0.770) and 0.700 (95% CI: 0.660–0.735) in training and validation cohorts, respectively. Calibration curves revealed strong agreement between predicted and observed probabilities. DCA curves confirmed clinical utility across risk thresholds. This validated nomogram provides a practical tool for AAC risk stratification using readily obtainable clinical parameters, enabling targeted interventions to mitigate calcification progression. Its integration into routine cardiovascular assessments may enhance preventive strategies in resource-limited settings.