Predicting the outcome for individual patients with traumatic brain injury: a case-based review
- PMID: 26853860
- DOI: 10.3109/02688697.2016.1139048
Predicting the outcome for individual patients with traumatic brain injury: a case-based review
Abstract
Background: Traumatic brain injuries result in significant morbidity and mortality. Accurate prediction of prognosis is desirable to inform treatment decisions and counsel family members. Objective To review the currently available prognostic tools for use in traumatic brain injury (TBI), to analyse their value in individual patient management and to appraise ongoing research on prognostic modelling.
Methods and results: We present two patients who sustained a TBI in 2011-2012 and evaluate whether prognostic models could accurately predict their outcome. The methodology and validity of current prognostic models are analysed and current research that might contribute to improved individual patient prognostication is evaluated.
Conclusion: Predicting prognosis in the acute phase after TBI is complex and existing prognostic models are not suitable for use at the individual patient level. Data derived from these models should only be used as an adjunct to clinical judgement and should not be used to set limits for acute care interventions. Information from neuroimaging, physiological monitoring and analysis of biomarkers or genetic polymorphisms may be used in the future to improve accuracy of individual patient prognostication. Clinicians should consider offering full supportive treatment to patients in the early phase after injury whilst the outcome is unclear.
Keywords: Head injury; TBI; neurosurgical intensive care; outcome; prognosis.
Similar articles
-
Prognostic value of day-of-injury plasma GFAP and UCH-L1 concentrations for predicting functional recovery after traumatic brain injury in patients from the US TRACK-TBI cohort: an observational cohort study.Lancet Neurol. 2022 Sep;21(9):803-813. doi: 10.1016/S1474-4422(22)00256-3. Lancet Neurol. 2022. PMID: 35963263 Free PMC article.
-
Performance of the IMPACT and CRASH prognostic models for traumatic brain injury in a contemporary multicenter cohort: a TRACK-TBI study.J Neurosurg. 2024 Mar 15;141(2):417-429. doi: 10.3171/2023.11.JNS231425. Print 2024 Aug 1. J Neurosurg. 2024. PMID: 38489823 Free PMC article.
-
Simplifying the use of prognostic information in traumatic brain injury. Part 2: Graphical presentation of probabilities.J Neurosurg. 2018 Jun;128(6):1621-1634. doi: 10.3171/2017.12.JNS172782. Epub 2018 Apr 10. J Neurosurg. 2018. PMID: 29631517
-
The impact of brain tissue oxygenation monitoring on the Glasgow Outcome Scale/Glasgow Outcome Scale Extended in patients with moderate to severe traumatic brain injury: A systematic review.Nurs Crit Care. 2024 Nov;29(6):1460-1469. doi: 10.1111/nicc.12973. Epub 2023 Sep 21. Nurs Crit Care. 2024. PMID: 37735107
-
Paediatric traumatic brain injury: prognostic insights and outlooks.Curr Opin Neurol. 2017 Dec;30(6):565-572. doi: 10.1097/WCO.0000000000000504. Curr Opin Neurol. 2017. PMID: 28938340 Review.
Cited by
-
Machine Learning Algorithm Predicts Mortality Risk in Intensive Care Unit for Patients with Traumatic Brain Injury.Diagnostics (Basel). 2023 Sep 21;13(18):3016. doi: 10.3390/diagnostics13183016. Diagnostics (Basel). 2023. PMID: 37761383 Free PMC article.
-
Development and Validation of a Machine Learning COVID-19 Veteran (COVet) Deterioration Risk Score.Crit Care Explor. 2024 Jul 19;6(7):e1116. doi: 10.1097/CCE.0000000000001116. eCollection 2024 Jul 1. Crit Care Explor. 2024. PMID: 39028867 Free PMC article.
-
Blood Biomarkers for Traumatic Brain Injury: A Quantitative Assessment of Diagnostic and Prognostic Accuracy.Front Neurol. 2019 Apr 26;10:446. doi: 10.3389/fneur.2019.00446. eCollection 2019. Front Neurol. 2019. PMID: 31105646 Free PMC article. Review.
-
Machine learning-based dynamic mortality prediction after traumatic brain injury.Sci Rep. 2019 Nov 27;9(1):17672. doi: 10.1038/s41598-019-53889-6. Sci Rep. 2019. PMID: 31776366 Free PMC article.
-
Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.PLoS One. 2018 Nov 9;13(11):e0207192. doi: 10.1371/journal.pone.0207192. eCollection 2018. PLoS One. 2018. PMID: 30412613 Free PMC article.
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical