Development and validation of a risk stratification model for cardiovascular disease in patients with radiographic axial spondyloarthritis (r-axSpA): a retrospective study

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Background: Patients with radiographic axial spondyloarthritis (r-axSpA) face an elevated risk of cardiovascular disease (CVD), yet practical prediction tools remain limited. This study aimed to develop a parsimonious risk stratification model for CVD risk in r-axSpA patients using routine clinical variables. Methods: This single-center retrospective cross-sectional study enrolled 259 r-axSpA patients (65 with CVD). Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to identify independent predictors. The model's discriminative ability, calibration, and clinical utility were assessed using area under the curve (AUC), calibration plots, and decision curve analysis (DCA). Results: Four independent predictors were identified: age, hypertension, diabetes mellitus, and alkaline phosphatase (ALP). The model demonstrated excellent discriminative ability in the training set (AUC = 0.888, 95% CI: 0.833-0.942; sensitivity 92.2%; specificity 74.6%) and acceptable performance in the validation set (AUC = 0.741). Calibration was satisfactory (Brier score: 0.115), and DCA confirmed positive net benefit. Subgroup analysis showed robust performance across both sexes. Conclusions: This study developed a simple yet robust risk stratification tool for CVD in r-axSpA patients using four readily available variables. The model showed good discriminative ability and clinical utility, with ALP emerging as a novel predictor. External validation in prospective cohorts is warranted before clinical implementation.

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