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

BACKGROUND AND PURPOSE: 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 (ML) 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.

MATERIALS AND

METHODS

In this single-center retrospective cohort study of patients with IIH treated from 2001-2022, we analyzed 23 variables, including validated neuroradiologic biomarkers, clinical characteristics, and shunt-specific factors. We developed 10 ML 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 used Shapley Additive Explanations for model interpretability and Kaplan-Meier analysis to evaluate time-dependent risk of shunt revision.

RESULTS

Among 128 patients (78 with shunt revision, 50 without), a stacked ensemble model (random forest + extreme gradient boosting) achieved the best performance on the independent held-out test set (25% of the cohort), with an accuracy of 78.2% (95% CI, 63.1%-90.2%) and an area under the curve of 82.7% (95% CI, 71.5%-92.0%). Model interpretability showed that optic nerve sheath diameter (ONSD; 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.

CONCLUSIONS

In this cohort, MRI-derived ONSD, 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.