Intracranial Pressure as a Dynamic Predictor of Traumatic Brain Injury Outcomes: A Scoping Review

Intracranial pressure (ICP) monitoring remains a cornerstone in the management of severe traumatic brain injury (TBI), yet its utility as a dynamic predictor of outcomes continues to evolve. We aimed to examine the role of serial ICP measurements as a potential predictor of outcomes after TBI, to combine ICP data with cerebrovascular reactivity metrics, and to highlight emerging trends in ICP modeling such as machine learning-based predictive models. We conducted a rigorous scoping review following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines to investigate the utility of ICP monitoring as a dynamic predictor of outcomes following TBI. A systematic search of major databases identified relevant studies published between January 1, 1998, and August 1, 2024. Two reviewers identified relevant articles, and conflicts were adjudicated by a third. Data from the included studies were abstracted and synthesized. Analysis of 29 studies (N = 5,743 patients) revealed significant associations between specific ICP patterns and clinical outcomes. Key findings included threshold-dependent mortality predictions, the value of early monitoring patterns (i.e., patterns observed within the first 72 h post-injury), and the enhancement of predictive accuracy through integration with cerebrovascular reactivity indices. Many studies now explore ICP as a multidimensional metric rather than a straightforward number, but overarching conclusions are limited by inter-study variability in analysis. The integration of advanced monitoring techniques, the use of features capturing the temporal complexity of ICP, and machine learning approaches show promise in enhancing the predictive value of ICP monitoring as a new form of precision medicine. These findings support strong associations between specific ICP dynamic patterns and mortality and functional outcomes. Standardization of protocols and validation in diverse populations remain important challenges to address in future studies.