Preoperative Neuroimaging Markers, Clinical Severity Measures, and Shunt Characteristics for Predicting Shunt Revision in Idiopathic Intracranial Hypertension: An Explainable Machine-Learning Study.

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Abstract

Surgical shunt placement is a common treatment for idiopathic intracranial hypertension (IIH) but is hampered by high revision rates. Prior predictive models for shunt revision in IIH have overlooked disease-specific neuroimaging markers. We developed an explainable machine learning model to identify the strongest predictors of shunt revision across neuroimaging markers, clinical severity variables, and shunt-specific factors. The primary objective was to assess the contribution of IIH-related neuroimaging markers within this multimodal predictive framework. In this single-center retrospective cohort study of IIH patients treated from 2001 to 2022, we analyzed 23 variables, including validated neuroradiologic biomarkers, clinical characteristics, and shunt-specific factors. We developed ten machine learning classifiers, which were trained and tuned on 75% of the data using stratified 5-fold cross-validation. Final model performance was validated on an independent, held-out test set comprising the remaining 25% of patients. We then employed SHapley Additive exPlanations for model interpretability and Kaplan-Meier analysis to evaluate time-dependent risk of shunt revision. Among 128 patients (78 with shunt revision, 50 without), a stacked ensemble model (random forest + XGBoost) achieved the best performance on the independent held-out test set (25% of the cohort), with an accuracy of 78.2% (95% confidence interval, 63.1%-90.2%) and an area under the curve of 82.7% (95% confidence interval, 71.5%-92.0%). Model interpretability showed that optic nerve sheath diameter (MRI-derived), papilledema and visual field deficits (ophthalmic clinical and neuro-ophthalmic measures), together with shunt characteristics (nonprogrammable valves, lumboperitoneal shunting, higher initial valve pressure), were the highest contributors to predicted revision risk. Kaplan-Meier analysis showed longer shunt survival with programmable valves and in patients without preoperative visual field deficits, papilledema, or obesity. In this cohort, MRI-derived optic nerve sheath diameter, papilledema, visual field deficits, and shunt characteristics were consistently among the most influential contributors to predicted risk of shunt revision. These findings highlight the added value of MRI-derived markers within a multimodal preoperative assessment, although prospective external validation is required before clinical adoption. SHAP = SHapley Additive exPlanations; ICP = Intracranial Pressure; IIH = Idiopathic Intracranial Hypertension; ML = Machine Learning; ONSD = Optic Nerve Sheath Diameter; LPS = Lumboperitoneal Shunt; XGBoost = Extreme Gradient Boosting.

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