Background The occurrence of bleeding events may seriously affect the prognosis of patients with Stent-Assisted Coil (SAC) aneurysms. A nomogram can provide a personalized, more accurate risk estimate based on predictors. We, therefore, developed a nomogram to predict the probability of bleeding events in patients with stent-assisted aneurysm embolization. Methods We performed a single-center retrospective analysis of data collected from patients undergoing stent-assisted aneurysm embolization between January 2018 and December 2021. Forward stepwise logistic regression was performed to identify independent predictors of adverse events of bleeding after stent-assisted embolization and to establish nomograms. Discrimination and calibration of this model using the area under the ROC curve (AUC-ROC) and the calibration plot. The model is internally validated by using resampling (1000 replicates). Results A total of 131 patients were collected, and a total of 118 patients met the study criteria. The predictors included in the nomogram were Body Mass Index(BMI), AAi, and MA-ADP. The model showed good resolving power with a ROC area of ​​0.893 (95% CI: 0.834 ~ 0.952) for this model with good calibration. Conclusion The nomogram can be used to individualize, visualize and accurately predict the risk probability of bleeding events after stent-assisted embolization of aneurysms.

BackgroundThe occurrence of bleeding events may seriously affect the prognosis of patients with Stent-Assisted Coil (SAC) aneurysms. A nomogram can provide a personalized, more accurate risk estimate based on predictors. We, therefore, developed a nomogram to predict the probability of bleeding events in patients with stent-assisted aneurysm embolization. MethodsWe performed a single-center retrospective analysis of data collected from patients undergoing stentassisted aneurysm embolization between January 2018 and December 2021. Forward stepwise logistic regression was performed to identify independent predictors of adverse events of bleeding after stentassisted embolization and to establish nomograms. Discrimination and calibration of this model using the area under the ROC curve (AUC-ROC) and the calibration plot. The model is internally validated by using resampling (1000 replicates). ResultsA total of 131 patients were collected, and a total of 118 patients met the study criteria. The predictors included in the nomogram were Body Mass Index(BMI), AAi, and MA-ADP. The model showed good resolving power with a ROC area of 0.893 (95% CI: 0.834 ~ 0.952) for this model with good calibration. ConclusionThe nomogram can be used to individualize, visualize and accurately predict the risk probability of bleeding events after stent-assisted embolization of aneurysms.