A web-based dynamic nomogram for rupture risk of posterior communicating artery aneurysms utilizing clinical, morphological, and hemodynamic characteristics

Heng Wei; Wenrui Han; Qi Tian; Kun Yao; Peibang He; Jianfeng Wang; Yujia Guo; Qianxue Chen; Mingchang Li

doi:10.3389/fneur.2022.985573

A web-based dynamic nomogram for rupture risk of posterior communicating artery aneurysms utilizing clinical, morphological, and hemodynamic characteristics

Front Neurol. 2022 Sep 14:13:985573. doi: 10.3389/fneur.2022.985573. eCollection 2022.

Authors

Heng Wei¹, Wenrui Han¹, Qi Tian¹, Kun Yao², Peibang He¹, Jianfeng Wang¹, Yujia Guo¹, Qianxue Chen¹, Mingchang Li¹

Affiliations

¹ Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China.
² Department of Neurosurgery, Jingzhou Central Hospital, Jingzhou, China.

Abstract

Background: Predicting rupture risk is important for aneurysm management. This research aimed to develop and validate a nomogram model to forecast the rupture risk of posterior communicating artery (PcomA) aneurysms.

Methods: Clinical, morphological, and hemodynamic parameters of 107 unruptured PcomA aneurysms and 225 ruptured PcomA aneurysms were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) analysis was applied to identify the optimal rupture risk factors, and a web-based dynamic nomogram was developed accordingly. The nomogram model was internally validated and externally validated independently. The receiver operating characteristic (ROC) curve was used to assess the discrimination of nomogram, and simultaneously the Hosmer-Lemeshow test and calibration plots were used to assess the calibration. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical utility of nomogram additionally.

Results: Four optimal rupture predictors of PcomA aneurysms were selected by LASSO and identified by multivariate logistic analysis, including hypertension, aspect ratio (AR), oscillatory shear index (OSI), and wall shear stress (WSS). A web-based dynamic nomogram was then developed. The area under the curve (AUC) in the training and external validation cohorts was 0.872 and 0.867, respectively. The Hosmer-Lemeshow p > 0.05 and calibration curves showed an appropriate fit. The results of DCA and CIC indicated that the net benefit rate of the nomogram model is higher than other models.

Conclusion: Hypertension, high AR, high OSI, and low WSS were the most relevant risk factors for rupture of PcomA aneurysms. A web-based dynamic nomogram thus established demonstrated adequate discrimination and calibration after internal and external validation. We hope that this tool will provide guidance for the management of PcomA aneurysms.

Keywords: LASSO regression; dynamic nomogram; external validation; posterior communicating artery aneurysms; rupture risk.