Predicting traumatic brain injury outcomes using a posterior dominant rhythm.

Abstract

Predicting severe traumatic brain injury (sTBI) outcomes is challenging, and existing models have limited applicability to individual patients. This study aimed to identify metrics that could predict recovery following sTBI. The researchers strived to demonstrate that a posterior dominant rhythm on electroencephalography is strongly associated with positive outcomes and to develop a novel machine learning-based model that accurately forecasts the return of consciousness. In this retrospective study, the authors assessed all intubated adults admitted with sTBI (Glasgow Coma Scale [GCS] score ≤ 8) from 2010 to 2021, who underwent EEG recording < 30 days from sTBI (n = 195). Seventy-three clinical, radiographic, and EEG variables were collected. Based on the presence of a PDR within 30 days of injury, two cohorts were created-those with a PDR (PDR[+] cohort, n = 51) and those without (PDR[-] cohort, n = 144)-to assess differences in presentation and four outcomes: in-hospital survival, recovery of command following, Glasgow Outcome Scale-Extended (GOS-E) score at discharge, and GOS-E score at 6 months post discharge. AutoScore, a machine learning-based clinical score generator that selects and assigns weights to important predictive variables, was used to create a prognostic model that predicts in-hospital survival and recovery of command following. Lastly, the MRC-CRASH and IMPACT traumatic brain injury predictive models were used to compare expected patient outcomes with true outcomes. At presentation, the PDR(-) cohort had a lower mean GCS motor subscore (1.97 vs 2.45, p = 0.048). Despite no difference in predicted outcomes (via MRC-CRASH and IMPACT), the PDR(+) cohort had superior rates of in-hospital survival (84.3% vs 63.9%, p = 0.007), recovery of command following (76.5% vs 53.5%, p = 0.004), and mean discharge GOS-E score (3.00 vs 2.39, p = 0.006). There was no difference in the 6-month GOS-E score. AutoScore was then used to identify the 7 following variables that were highly predictive of in-hospital survival and recovery of command: age, body mass index, systolic blood pressure, pupil reactivity, blood glucose, and hemoglobin (all at presentation), and a PDR on EEG. This model had excellent discrimination for predicting in-hospital survival (area under the curve [AUC] 0.815) and recovery of command following (AUC 0.700). A PDR on EEG in sTBI patients predicts favorable outcomes. The authors' prognostic model has strong accuracy in predicting these outcomes, and performed better than previously reported models. The authors' model can be valuable in clinical decision-making as well as counseling families following these types of injuries.