Development of a Clinical Nomogram for Predicting Shunt-Dependent Hydrocephalus

Background Hydrocephalus (HCP) is one of the neurosurgical conditions that can lead to impaired quality of life, disability, and mortality. The nomogram is a clinical prediction tool that has been studied in a variety of medical conditions. Hence, the primary objective of the present study was to establish the nomogram for predicting shunt-dependent HCP in patients with varied etiologies. The secondary objective was to identify predictors associated with shunt-dependent HCP. Methods In the present study, 382 adult patients with various etiologies of HCP who had undergone ventriculostomy were included retrospectively. Several clinical factors, imaging findings, and ventricular indexes were analyzed for shunt-dependent HCP in both univariate and multivariable analysis. Based on binary logistic regression, the nomogram was created and internally validated from the final model. Results Shunt-dependent HCP was observed in 25.7% of the present cohort. Initially, progressive headache, preoperative seizure, Evans index, third ventricle index, cella media index, ventricular score, and mass diameter were candidate predictors from univariate analysis. The final model which had the lowest Akaike information criterion comprised the third ventricle index and cella media index. Therefore, the model's performance had an area under the receiver operating characteristic curve (AUC) of 0.712, Moreover, the AUCs of bootstrapping and cross-validation methods were 0.701 and 0.702, respectively. Conclusion In summary, clinical factors and ventricular measures that were strongly associated with shunt-dependent HCP were used to develop clinical prediction tools that could help physicians make decisions and care for high-risk patients in general practice.