Abstract
Acute hydrocephalus is a severe complication that may occur early after an intracerebral hemorrhage (ICH). However, clinical factors predicting the occurrence of acute hydrocephalus have rarely been studied. This study aimed to establish a nomogram model to predict early acute hydrocephalus after ICH. We retrospectively analyzed the data of 930 patients with ICH who were treated at our hospital between January 2017 and May 2024. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen for risk factors for acute hydrocephalus, and stepwise logistic regression analysis was used to construct the prediction model, which was visualized using a nomogram. Data were randomly divided into training (n = 652) and test (n = 278) sets at a 7:3 ratio. A total of 930 patients were included, of whom 123 (13.2%) developed acute hydrocephalus within 6 h of being diagnosed with ICH. Univariate analysis revealed that 11 indicators were associated with acute hydrocephalus. In the training set, LASSO and stepwise logistic regression analyses identified four independent risk factors that were used to establish a prediction model. These were the modified Graeb score, age, infratentorial hemorrhage > 15 mL, and thalamic hemorrhage > 15 mL. A graphical nomogram was then developed. The area under the receiver operating characteristic (ROC) curve was 0.974 (95% confidence interval 0.961-0.987). In the Hosmer-Lemeshow test, the p-value was 0.887. The mean absolute error of the calibration plot was 0.012. The decision curve analysis (DCA) validated the fitness and clinical application value of this nomogram. Internal validation showed the test set was in good accordance with the training set. The nomogram prediction model showed good accuracy and could be used to predict the risk of early acute hydrocephalus after ICH, thereby aiding neurologist in making rapid clinical decisions.