Machine Learning Approaches to Intracranial Pressure Prediction in Patients with Traumatic Brain Injury: A Systematic Review

Purpose: Intracranial pressure (ICP) monitoring is a "gold standard" monitoring modality for severe traumatic brain injury (TBI) patients. The capacity to predict ICP crises could further minimise the rate of secondary brain injury and improve the outcomes of TBI patients by facilitating timely intervention prior to a potential crisis. This systematic review sought (i) to identify the most efficacious approaches to the prediction of ICP crises within TBI patients, (ii) to access the clinical suitability of existing predictive models and (iii) to suggest potential areas for future research. Methods: Peer-reviewed primary diagnostic accuracy studies, assessing the performance of ICP crisis prediction methods within TBI patients, were included. The QUADAS-2 tool was used to evaluate the quality of the studies. Results: Three optimal solutions to predicting the ICP crisis were identified: a long short-term memory (LSTM) model, a Gaussian processes (GP) approach and a logistic regression model. These approaches performed with an area under the receiver operating characteristics curve (AUC-ROC) ranging from 0.86 to 0.95. Conclusions: The review highlights the existing disparity of the definition of an ICP crisis and what prediction horizon is the most clinically relevant. Moreover, this review draws attention to the existing lack of focus on the clinical intelligibility of algorithms, the measure of how algorithms improve patient care and how algorithms may raise ethical, legal or social concerns. The review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42022314278).