Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review

Robert McNamara, Shiv Meka, James Anstey, Daniel Fatovich, Luke Haseler, Toby Jeffcote, Andrew Udy, Rinaldo Bellomo, Melinda Fitzgerald

Research output: Contribution to journalReview ArticleResearchpeer-review

7 Citations (Scopus)

Abstract

Traumatic intracranial hypertension (tIH) is a common and potentially lethal complication of moderate to severe traumatic brain injury (m-sTBI). It often develops with little warning and is managed reactively with the tiered application of intracranial pressure (ICP)-lowering interventions administered in response to an ICP rising above a set threshold. For over 45 years, a variety of research groups have worked toward the development of technology to allow for the preemptive management of tIH in the hope of improving patient outcomes. In 2022, the first operationalizable tIH prediction system became a reality. With such a system, ICP lowering interventions could be administered prior to the rise in ICP, thus protecting the patient from potentially damaging tIH episodes and limiting the overall ICP burden experienced. In this review, we discuss related approaches to ICP forecasting and IH prediction algorithms, which collectively provide the foundation for the successful development of an operational tIH prediction system. We also discuss operationalization and the statistical assessment of tIH algorithms. This review will be of relevance to clinicians and researchers interested in development of this technology as well as those with a general interest in the bedside application of machine learning (ML) technology.

Original languageEnglish
Pages (from-to)416-434
Number of pages19
JournalJournal of Neurotrauma
Volume40
Issue number5-6
DOIs
Publication statusPublished - 1 Mar 2023

Keywords

  • intracranial hypertension
  • intracranial hypertension prediction
  • intracranial pressure forecasting
  • machine learning

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