Nomogram model for predicting oculomotor nerve palsy in patients with intracranial aneurysm

Abstract

AIM: To explore the risk factors of oculomotor nerve palsy (ONP) in patients with intracranial aneurysm (IA) and develop a nomogram model for predicting ONP of IA patients. METHODS: A total of 329 IA patients were included. Logistic regression analysis was applied to identify independent factors, which were then integrated into the nomogram model. The performance of the nomogram model was evaluated by calibration curve, receiver operating curve (ROC), and decision curve analysis. RESULTS: Univariate and multivariate logistic regression analysis indicated posterior communicating artery (PCoA) aneurysm [hazard ratio (HR)=17.13, P<0.001] and aneurysm diameter (HR=1.31, P<0.001) were independent risk factors of ONP in IA patients. Based on the results of logistic regression analysis, a nomogram model for predicting the ONP in IA patients was constructed. The calibration curve indicated the nomogram had a good agreement between the predictions and observations. The nomogram showed a high predictive accuracy and discriminative ability with an area under the curve (AUC) of 0.863. The decision curve analysis showed that the nomogram was powerful in the clinical decision. PCoA aneurysm (HR=3.38, P=0.015) was identified to be the only independent risk factor for ONP severity. CONCLUSION: PCoA aneurysm and aneurysm diameter are independent risk factors of ONP in IA patients. The nomogram established is performed reliably and accurately for predicting ONP. PCoA aneurysm is the only independent risk factor for ONP severity.