Development and Validation of an Early Postoperative Risk Stratification Model for Hydrocephalus Following Aneurysmal Subarachnoid Hemorrhage.
Abstract
Background
Hydrocephalus is a common complication after aneurysmal subarachnoid hemorrhage (aSAH). This study aimed to identify perioperative risk factors and develop an early postoperative risk stratification model to support postoperative monitoring rather than purely preoperative prediction.Methods
Employing a retrospective case-control design, clinical data from 331 patients with aSAH treated at the First Affiliated Hospital of Henan Medical University between August 1, 2018, and December 31, 2024, were collected and analyzed. The primary endpoint was postoperative hydrocephalus occurring within 3 weeks after aneurysm treatment, diagnosed according to combined radiological and clinical criteria. The least absolute shrinkage and selection operator regression method was employed to screen risk factors. Multiple machine-learning models were compared, and Shapley additive explanations (SHAP) analysis was used for individualized model interpretation.Results
The admission Glasgow Coma Scale score, intraoperative external ventricular drainage/lateral ventricular drainage, and intraventricular hemorrhage were identified as key variables associated with postoperative hydrocephalus. The logistic regression model emerged as the optimal model; locked test set area under the curve (95% CI): 0.891 (0.739-1.000); accuracy: 0.720 (0.688-0.775); sensitivity: 0.899 (0.823-0.987); specificity: 0.683 (0.635-0.740).Conclusion
The logistic regression model with SHAP-based interpretation may support early postoperative risk stratification for hydrocephalus after aSAH. Given its modest positive predictive value, the model should be used to triage patients for intensified monitoring and timely reassessment.
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BibTeX
@article{li2026development,
title = {{Development and Validation of an Early Postoperative Risk Stratification Model for Hydrocephalus Following Aneurysmal Subarachnoid Hemorrhage.}},
author = {Fuguang Li and Kunxu Fu and Shifang Zhou and Zipeng Gong and Chaoyang Du and Yuxi Zhao and Weilong Yang and Chunyu Zhang and Wenke Zhou and Wenchao Li},
journal = {Brain and behavior},
year = {2026},
month = oct,
doi = {10.1002/brb3.71869},
url = {https://europepmc.org/article/MED/42949151},
note = {CC BY},
}
RIS
TY - JOUR
TI - Development and Validation of an Early Postoperative Risk Stratification Model for Hydrocephalus Following Aneurysmal Subarachnoid Hemorrhage.
AU - Fuguang Li
AU - Kunxu Fu
AU - Shifang Zhou
AU - Zipeng Gong
AU - Chaoyang Du
AU - Yuxi Zhao
AU - Weilong Yang
AU - Chunyu Zhang
AU - Wenke Zhou
AU - Wenchao Li
PY - 2026
DA - 2026/10/28
JO - Brain and behavior
DO - 10.1002/brb3.71869
UR - https://europepmc.org/article/MED/42949151
AB - Background Hydrocephalus is a common complication after aneurysmal subarachnoid hemorrhage (aSAH). This study aimed to identify perioperative risk factors and develop an early postoperative risk stratification model to support postoperative monitoring rather than purely preoperative prediction.Methods Employing a retrospective case-control design, clinical data from 331 patients with aSAH treated at the First Affiliated Hospital of Henan Medical University between August 1, 2018, and December 31, 2024, were collected and analyzed. The primary endpoint was postoperative hydrocephalus occurring within 3 weeks after aneurysm treatment, diagnosed according to combined radiological and clinical criteria. The least absolute shrinkage and selection operator regression method was employed to screen risk factors. Multiple machine-learning models were compared, and Shapley additive explanations (SHAP) analysis was used for individualized model interpretation.Results The admission Glasgow Coma Scale score, intraoperative external ventricular drainage/lateral ventricular drainage, and intraventricular hemorrhage were identified as key variables associated with postoperative hydrocephalus. The logistic regression model emerged as the optimal model; locked test set area under the curve (95% CI): 0.891 (0.739-1.000); accuracy: 0.720 (0.688-0.775); sensitivity: 0.899 (0.823-0.987); specificity: 0.683 (0.635-0.740).Conclusion The logistic regression model with SHAP-based interpretation may support early postoperative risk stratification for hydrocephalus after aSAH. Given its modest positive predictive value, the model should be used to triage patients for intensified monitoring and timely reassessment.
SN - 2162-3279
ER -