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Abstract
Intracranial pressure (ICP) is a physiological parameter that conventionally requires invasive monitoring for accurate measurement. Utilising multivariate predictive models, we sought to evaluate the utility of non-invasive, widely accessible MRI biomarkers in predicting ICP and their reversibility following cerebrospinal fluid (CSF) diversion. The retrospective study included 325 adult patients with suspected CSF dynamic disorders who underwent brain MRI scans within three months of elective 24-h ICP monitoring. Five MRI biomarkers were assessed: Yuh sella grade, optic nerve vertical tortuosity (VT), optic nerve sheath distension, posterior globe flattening and optic disc protrusion (ODP). The association between individual biomarkers and 24-h ICP was examined and reversibility of each following CSF diversion was assessed. Multivariate models incorporating these radiological biomarkers were utilised to predict 24-h median intracranial pressure. All five biomarkers were significantly associated with median 24-h ICP (p < 0.0001). Using a pair-wise approach, the presence of each abnormal biomarker was significantly associated with higher median 24-h ICP (p < 0.0001). On multivariate analysis, ICP was significantly and positively associated with Yuh sella grade (p < 0.0001), VT (p < 0.0001) and ODP (p = 0.003), after accounting for age and suspected diagnosis. The Bayesian multiple linear regression model predicted 24-h median ICP with a mean absolute error of 2.71 mmHg. Following CSF diversion, we found pituitary sella grade to show significant pairwise reversibility (p < 0.001). ICP was predicted with clinically useful precision utilising a compact Bayesian model, offering an easily interpretable tool using non-invasive MRI data. Brain MRI biomarkers are anticipated to play a more significant role in the screening, triaging, and referral of patients with suspected CSF dynamic disorders.
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1 University College London, High-Dimensional Neurology Group, UCL Queen Square Institute of Neurology, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201); National Hospital for Neurology and Neurosurgery, Victor Horsley Department of Neurosurgery, London, UK (GRID:grid.436283.8) (ISNI:0000 0004 0612 2631)
2 National Hospital for Neurology and Neurosurgery, Victor Horsley Department of Neurosurgery, London, UK (GRID:grid.436283.8) (ISNI:0000 0004 0612 2631); University College London, UCL, Division of Medicine, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201)
3 University College London, UCL, Division of Medicine, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201)
4 University College London, Department of Psychology and Language Sciences, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201)
5 Queen Mary University of London, School of Medicine, Barts and the London School of Medicine and Dentistry, London, UK (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133)
6 National Hospital for Neurology and Neurosurgery, Victor Horsley Department of Neurosurgery, London, UK (GRID:grid.436283.8) (ISNI:0000 0004 0612 2631)
7 University College London, High-Dimensional Neurology Group, UCL Queen Square Institute of Neurology, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201)