Nomogram for the Prediction of Shunt-Dependent Hydrocephalus in Patients with Aneurysmal Subarachnoid Hemorrhage: A Single-institute Experience

ABSTRACT BACKGROUND This study is focused to identify the risk factors of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid hemorrhage (aSAH) and develop a model to predict its incidence. METHODS Medical records of 118 consecutive patients with aSAH treated in our institution from January 2013 to October 2021 were reviewed retrospectively, 109 of them were enrolled in this cohort, the following data were analyzed based on presence or absence of SDHC: age, gender, body mass index, Fisher grade, Hunt and Hess scale (HHS), aneurysm location, treatment modality, new neurological deficits after aneurysm treatment, estimated glomerular filtration rate (eGFR), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and platelet-neutrophil ratio (PNR). We conducted univariate and multivariate logistic regression analyses to illustrate a nomogram for predicting SDHC risk. RESULTS The stepwise logistic regression analysis with backward selection revealed three independent predictive factors of SDHC: age ≥65 (odds ratio, 3.94; 95% CI, 1.4–11.00; p = 0.009), treatment modality (odds ratio, 4.36; 95% CI, 1.81–10.53; p = 0.001), and HHS ≥3 (odds ratio, 3.59; 95% CI, 1.50–8.61; p = 0.004). A nomogram for SDHC risk prediction was developed based on the weight of these 3 factors. CONCLUSIONS Age, treatment modality (clipping vs coiling), and HHS are predictive for SDHC after aSAH. Endovascular embolization of aneurysm plays an important role in reducing risk of SDHC after aSAH.