Prognostic Nomogram of Predictors for Shunt-Dependent Hydrocephalus in Patients with Aneurysmal Subarachnoid Hemorrhage Receiving External Ventricular Drain Insertion: A Single-Center Experience and Narrative Review

Abstract

This study aimed to create a prediction model with a radiographic score, serum, and cerebrospinal fluid (CSF) values for the occurrence of shunt-dependent hydrocephalus (SDHC) in patients with aneurysmal subarachnoid hemorrhage (aSAH), and to review and analyze literature related to the prediction of the development of SDHC. Sixty-three patients with aSAH who underwent external ventricular drain insertion were included and separated into two subgroups: non-SDHC and SDHC. Patient characteristics, computed tomography scoring system, and serum and CSF parameters were collected. Multivariate logistic regression was conducted to illustrate a nomogram for determining the predictors of SDHC. Furthermore, we sorted and summarized previous meta-analyses for predictors of SDHC RESULTS: The SDHC group had 42 cases. Stepwise logistic regression analysis revealed three independent predictive factors associated with a higher modified Graeb (mGraeb) score, lower level of estimated glomerular filtration rate group, and lower level of CSF glucose. The nomogram, based on these three factors, was presented with significant predictive performance (area under curve, AUC = 0.895) for SDHC development, compared to other scoring systems (AUC = 0.764-0.885). In addition, a forest plot was generated to present the 12 statistically significant predictors and odds ratio for correlations with the development of SDHC. First, the development of a nomogram with combined significant factors had a good performance in estimating the risk of SDHC in primary patient evaluation and assisted in clinical decision-making. Second, a narrative review, presented with a forest plot, provided the current published data on predicting SDHC.