Development and External Validation of a Dynamic Nomogram With Potential for Risk Assessment of Ruptured Multiple Intracranial Aneurysms - Chen, TingTing; Xiong, WeiGen; Zhao, ZhiHong; Shan, YaJie; Li, XueMei; Guo, LeHeng; Xiang, Lan; Chu, Dong; Fan, HongWei; Li, YingBin; Zou, JianJun (Frontiers in Neurology, 2022)
Background and Purpose: About 20.1% of intracranial aneurysms (IAs) carriers are multiple intracranial aneurysms (MIAs) patients with higher rupture risk and worse prognosis. A prediction model may bring some potential benefits. This study attempted to develop and external validate a dynamic nomogram to assess the rupture risk of each IA among MIAs patients. Method: We retrospectively analyzed the data of 262 patients with 611 IAs admitted to the Hunan Provincial People's Hospital between November 2015 and November 2021. Multivariable logistic regression (MLR) was applied to select the risk factors and derive a nomogram model for the assessment of IA rupture risk in MIA patients. To externally validate the nomogram, data of 35 patients with 78 IAs were collected from another independent center between December 2009 and May 2021. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical utility. Result: Size, location, irregular shape, diabetes history and neck width were independently associated with IA rupture. The nomogram showed a good discriminative ability for ruptured and unruptured IAs in the derivation cohort (AUC = 0.810; 95% CI, 0.774–0.847) and was successfully generalized in the external validation cohort (AUC = 0.744; 95% CI, 0.627–0.862). The nomogram was calibrated well and the decision curve analysis showed that it would generate more net benefit in identifying IA rupture than the “treat all” or “treat none” strategies at the threshold probabilities ranging from 10% to 60% both in the derivation and external validation set. The web-based dynamic nomogram calculator was accessible on https://wfs666.shinyapps.io/onlinecalculator/. Conclusion: External validation shown that the model was potential to assist clinical identification of dangerous aneurysms after longitudinal data evaluation. Size, neck width and location are the primary risk factors for ruptured IAs.