Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury: A Systematic Review

• Published in: Neurocritical care Vol. 41; no. 1; pp. 285 - 296
• Main Authors: van Hal, S. T., van der Jagt, M., van Genderen, M. E., Gommers, D., Veenland, J. F.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-08-2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Bias; Brain Injuries, Traumatic - complications; Brain Injuries, Traumatic - physiopathology; Clinical medicine; Critical Care Medicine; Data collection; Humans; Hypertension; Intensive; Internal Medicine; Intracranial Hypertension - diagnosis; Intracranial Hypertension - etiology; Intracranial Hypertension - physiopathology; Intracranial pressure; Intracranial Pressure - physiology; Machine Learning; Medicine; Medicine & Public Health; Missing data; Neurology; Neurosurgery; Review; Review Article; Systematic review; Traumatic brain injury; Traumatic brain injury; Artificial intelligence; Intracranial hypertension; Prediction
• ISSN: 1541-6933; 1556-0961; 1556-0961
• DOI: 10.1007/s12028-023-01910-2

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.