Development and validation of a nomogram for predicting early rupture and rebleeding risk after intracranial aneurysm embolization

Objective To construct and validate a nomogram for predicting early rupture rebleeding risk after intracranial aneurysm embolization, providing a precise clinical assessment tool. Methods Clinical data from 274 patients (March 2022–February 2025) were retrospectively analyzed, divided into a training set ( n = 192) and validation set ( n = 82) (7:3 ratio). Univariate/multivariate logistic regression identified independent risk factors. The nomogram’s performance was evaluated via ROC curves, calibration curves, and DCA. Results Multivariate logistic regression analysis revealed that larger aneurysm diameter, wider neck, higher preoperative Hunt-Hess grade, incomplete embolization, and poor postoperative blood pressure control were independent risk factors for early rupture rebleeding after embolization (all p < 0.05). The constructed nomogram demonstrated good calibration and discriminative ability in both the training and validation sets, with C-index values of 0.873 and 0.738, respectively. The areas under the ROC curves (AUC) were 0.870 (95%CI: 0.790–0.951) and 0.739 (95%CI: 0.456–1.000) with corresponding sensitivities and specificities of 0.812, 0.840 and 0.667, 0.902, respectively. Decision curve analysis indicated significant clinical utility within specific threshold probability ranges. Conclusion The multifactor nomogram exhibits strong predictive performance, facilitating early identification of high-risk patients and personalized treatment.