Please use this identifier to cite or link to this item:
https://ahro.austin.org.au/austinjspui/handle/1/32341
Title: | Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review. | Austin Authors: | McNamara, Robert;Meka, Shiv;Anstey, James;Fatovich, Daniel;Haseler, Luke;Jeffcote, Toby;Udy, Andrew;Bellomo, Rinaldo ;Fitzgerald, Melinda | Affiliation: | Department of Intensive Care Medicine, Royal Perth Hospital, Perth, Western Australia, Australia. Data Innovation Laboratory, Western Australian Department of Health, Perth, Western Australia, Australia. Department of Intensive Care, The Royal Melbourne Hospital, Melbourne, Victoria, Australia. Department of Emergency Medicine, Royal Perth Hospital, Perth, Western Australia, Australia. Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia, Australia. Department of Intensive Care, Alfred Health, Melbourne, Victoria, Australia. Department of Intensive Care, Alfred Health, Melbourne, Victoria, Australia. Department of Intensive Care, The Royal Melbourne Hospital, Melbourne, Victoria, Australia. Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia, Australia. Data Analytics Research and Evaluation (DARE) Centre Intensive Care |
Issue Date: | Mar-2023 | Date: | 2022 | Publication information: | Journal of neurotrauma 2023; 40(5-6):416-434. | 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. | URI: | https://ahro.austin.org.au/austinjspui/handle/1/32341 | DOI: | 10.1089/neu.2022.0201 | ORCID: | Journal: | Journal of neurotrauma | Start page: | 416 | End page: | 434 | PubMed URL: | 36205570 | ISSN: | 1557-9042 | Type: | Journal Article | Subjects: | intracranial hypertension intracranial hypertension prediction intracranial pressure forecasting machine learning Brain Injuries, Traumatic/complications Brain Injuries, Traumatic/diagnosis Intracranial Hypertension/etiology Intracranial Hypertension/complications Intracranial Pressure/physiology |
Appears in Collections: | Journal articles |
Show full item record
Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.