Machine Learning in Developing a Predictive Model for Chronic Hydrocephalus Following Aneurysmal Subarachnoid Hemorrhage

Rao Dai¹enxi xu²Lixiang Zhang³zehui wang⁴bowen liu⁵Lu Siyuan⁶xiuhong shan⁵Eryi Sun^2*

• ¹Zhenjiang First People's Hospital Department of Surgery, Zhenjiang, China
• ²Jiangsu University Affiliated People's Hospital, Zhenjiang, China
• ³Children's Hospital of Soochow University, Suzhou, China
• ⁴Jiangsu University of Science and Technology, Zhenjiang, China
• ⁵Zhenjiang First People's Hospital Department of Radiology, Zhenjiang, China
• ⁶Zhenjiang First People's Hospital, Zhenjiang, China

Objective: Using machine learning (ML) algorithms integrated with deep learning and radiomics technologies, we developed a nomogram model through an in-depth analysis and mining of clinical data and imaging features from patients with aneurysmal subarachnoid hemorrhage (aSAH). This model was aimed to predict the risk of developing chronic hydrocephalus in aSAH patients. Results: A total of 180 patients were included, and a 3D-Unet automatic segmentation model was developed to accurately identify and quantify SAH volume. In the test set, the model achieved a Dice similarity coefficient (DSC) of 0.85 ± 0.04, an intersection over union (IoU) of 0.74 ± 0.06, a Hausdorff distance (HD) of 20.4 ± 12.3, and an average symmetric surface distance (ASSD) of 0.31 ± 0.23, demonstrating excellent performance in identifying SAHs. After screening features such as hematoma volume and radiomic score through univariate logistic regression (LR), 21 potential risk factors were identified. LASSO regression further refined these to nine key risk factors. Combining the results from both analyses, 6 independent predictive factors were determined: cerebrospinal fluid lactic acid level, sodium (Na), corpus callosum angle, interval to blood clearance, periventricular white matter changes, and hematoma volume. Among 8 ML models, the LR model showed the best performance, with AUC values of 0.884 (95% confidence interval [CI]: 0.826–0.942) in the training cohort and 0.860 (95% CI: 0.758–0.962) in the test cohort. The calibration curve of the LR model showed a high agreement between predicted probabilities and observed outcomes. Additionally, DCA and CIC analyses demonstrated significant net benefits across different risk thresholds, confirming high consistency between predictions and actual outcomes. Conclusion: The developed 3D-Unet automatic segmentation model accurately identified hematomas and calculated their volume, addressing the challenge of quantitatively assessing SAH volume in clinical practice. Hematoma volume, a key risk factor, was integrated with clinical and radiological features from computed tomography (CT) scans using ML methods to construct a clinical-radiological nomogram. This nomogram effectively predicted the development of chronic hydrocephalus in patients with aSAH.