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Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review
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 administe...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Mary Ann Liebert, Inc., publishers
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986028/ https://www.ncbi.nlm.nih.gov/pubmed/36205570 http://dx.doi.org/10.1089/neu.2022.0201 |
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author | McNamara, Robert Meka, Shiv Anstey, James Fatovich, Daniel Haseler, Luke Jeffcote, Toby Udy, Andrew Bellomo, Rinaldo Fitzgerald, Melinda |
author_facet | McNamara, Robert Meka, Shiv Anstey, James Fatovich, Daniel Haseler, Luke Jeffcote, Toby Udy, Andrew Bellomo, Rinaldo Fitzgerald, Melinda |
author_sort | McNamara, Robert |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9986028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Mary Ann Liebert, Inc., publishers |
record_format | MEDLINE/PubMed |
spelling | pubmed-99860282023-03-07 Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review McNamara, Robert Meka, Shiv Anstey, James Fatovich, Daniel Haseler, Luke Jeffcote, Toby Udy, Andrew Bellomo, Rinaldo Fitzgerald, Melinda J Neurotrauma Reviews 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. Mary Ann Liebert, Inc., publishers 2023-03-01 2023-03-01 /pmc/articles/PMC9986028/ /pubmed/36205570 http://dx.doi.org/10.1089/neu.2022.0201 Text en © Robert McNamara et al., 2023; Published by Mary Ann Liebert, Inc. https://creativecommons.org/licenses/by/4.0/This Open Access article is distributed under the terms of the Creative Commons License (CC-BY) (http://creativecommons.org/licenses/by/4.0 (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. |
spellingShingle | Reviews McNamara, Robert Meka, Shiv Anstey, James Fatovich, Daniel Haseler, Luke Jeffcote, Toby Udy, Andrew Bellomo, Rinaldo Fitzgerald, Melinda Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review |
title | Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review |
title_full | Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review |
title_fullStr | Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review |
title_full_unstemmed | Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review |
title_short | Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review |
title_sort | development of traumatic brain injury associated intracranial hypertension prediction algorithms: a narrative review |
topic | Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986028/ https://www.ncbi.nlm.nih.gov/pubmed/36205570 http://dx.doi.org/10.1089/neu.2022.0201 |
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