Machine Learning-Based Prediction of Chronic Shunt-Dependent Hydrocephalus After Spontaneous Subarachnoid Hemorrhage

Abstract

Background: Chronic posthemorrhagic hydrocephalus often arises following spontaneous subarachnoid hemorrhage (SAH). Timely identification of patients predisposed to develop chronic shunt-dependent hydrocephalus may significantly enhance clinical outcomes.

Methods: We performed an analysis of 510 SAH patients treated at our institution between 2013 and 2018. Clinical and radiological variables, including age, sex, Hunt & Hess grade, Fisher Score, external ventricular drainage placement, central nervous system infection, aneurysm characteristics, and treatment modalities, were evaluated. Supervised machine learning (ML) models, trained and compared using Python and scikit-learn, were employed to predict chronic shunt-dependent hydrocephalus. Model performance was rigorously assessed through repeated cross-validation. To facilitate transparency and collaboration, we publicly released the dataset and code on GitHub (https://github.com/RISCSoftware/shuntclf) and developed an interactive web application (https://huggingface.co/spaces/risc42/shuntclf).

Results: Among the evaluated ML models, logistic regression exhibited superior performance, with an area under the receiver operating characteristic curve of 0.819 and an area under the precision-recall curve of 0.482, along with the highest F1 score of 0.473. Although the balanced accuracy scores of the models were generally proximate, ranging from 0.735 to 0.764, logistic regression consistently outperforms others in key metrics such as area under the receiver operating characteristic curve and area under the precision-recall curve. Conversely, female gender and absence of aneurysm within the anterior communicating artery were associated with reduced shunt requirement likelihood.

Conclusions: ML models, including logistic regression, demonstrate strong predictive capability for early chronic shunt-dependent hydrocephalus following spontaneous SAH, which may potentially contribute to more timely shunt placement interventions. This predictive capability is supported by our web interface, which simplifies the application of these models, aiding clinicians in efficiently determining the need for shunt placement.

Keywords: Hydrocephalus; Machine learning; Outcome prediction; Shunt-dependency; Subarachnoid hemorrhage.