• Yao-Chung Yang, Chun-Hao Yin, Kuan-Ting Chen, Pei-Chin Lin, Ching-Chih Lee, Wei-Chuan Liao, and Jin-Shuen Chen.
• Department of Surgery, Division of Neurosurgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; School of Medicine, National Defense Medical Center, Taipei, Taiwan.
• World Neurosurg. 2021 Jun 1; 150: e12-e22.

ObjectiveThis 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.MethodsSixty-three patients with aSAH who underwent external ventricular drain insertion were included and separated into 2 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.ResultsThe SDHC group had 42 cases. Stepwise logistic regression analysis revealed 3 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 3 factors, was presented with significant predictive performance (area under curve = 0.895) for SDHC development, compared with 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.ConclusionsFirst, 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.Copyright © 2021. Published by Elsevier Inc.

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