Background: Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI.
Methods: Continuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1-2) or good (GOSE 3-8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor.
Results: Fifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively.
Conclusions: Our study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.
Keywords: EEG; ICU; Prognosis; Random forest; Traumatic brain injury.
Conflict of interest statement
MP is the co-founder of Clinical Science Systems, a supplier of EEG systems for Medisch Spectrum Twente. Clinical Science Systems offered no funding and was not involved in the design, execution, analysis, interpretation, or publication of the study. The remaining authors declare that they do not have competing interests.
Quantitative EEG Parameters for Prediction of Outcome in Severe Traumatic Brain Injury: Development Study.Clin EEG Neurosci. 2018 Jul;49(4):248-257. doi: 10.1177/1550059417742232. Epub 2017 Nov 27. Clin EEG Neurosci. 2018. PMID: 29172703
Risk Adjustment In Neurocritical care (RAIN)--prospective validation of risk prediction models for adult patients with acute traumatic brain injury to use to evaluate the optimum location and comparative costs of neurocritical care: a cohort study.Health Technol Assess. 2013 Jun;17(23):vii-viii, 1-350. doi: 10.3310/hta17230. Health Technol Assess. 2013. PMID: 23763763 Free PMC article.
Computed tomography-estimated specific gravity at hospital admission predicts 6-month outcome in mild-to-moderate traumatic brain injury patients admitted to the intensive care unit.Anesth Analg. 2012 May;114(5):1026-33. doi: 10.1213/ANE.0b013e318249fe7a. Epub 2012 Feb 24. Anesth Analg. 2012. PMID: 22366842
Prospective independent validation of IMPACT modeling as a prognostic tool in severe traumatic brain injury.J Neurotrauma. 2012 Jan 1;29(1):47-52. doi: 10.1089/neu.2010.1482. Epub 2011 Dec 1. J Neurotrauma. 2012. PMID: 21933014
Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification.In: Kobeissy FH, editor. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (FL): CRC Press/Taylor & Francis; 2015. Chapter 25. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. 2015. PMID: 26269925 Free Books & Documents. Review.