Prognostic Models for Traumatic Brain Injury Have Good Discrimination but Poor Overall Model Performance for Predicting Mortality and Unfavorable Outcomes. Issue 1 (29th January 2023)

• Record Type: Journal Article
• Authors: Eagle, Shawn R.; Pease, Matthew; Nwachuku, Enyinna; Deng, Hansen; Okonkwo, David O.
• Is Part Of: Neurosurgery. Volume 92:Issue 1(2023)
• Page Start: 137
• Page End: 143
• Publication Date: 2023-01-29
• Subjects: TBI -- Prognostication -- CRASH -- IMPACT -- Accuracy -- Mortality -- Unfavorable outcome; Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1227/neu.0000000000002150
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract : BACKGROUND: The most extensively validated prognostic models for traumatic brain injury (TBI) are the Corticoid Randomization after Significant Head Injury (CRASH) and International Mission on Prognosis and Analysis of Clinical Trials (IMPACT). Model characteristics outside of area under the curve (AUC) are rarely reported. OBJECTIVE: To report the discriminative validity and overall model performance of the CRASH and IMPACT models for prognosticating death at 14 days (CRASH) and 6 months (IMPACT) and unfavorable outcomes at 6 months after TBI. METHODS: This retrospective cohort study included prospectively collected patients with severe TBI treated at a single level I trauma center (n = 467). CRASH and IMPACT percent risk values for the given outcome were computed. Unfavorable outcome was defined as a Glasgow Outcome Scale-Extended score of 1 to 4 at 6 months. Binary logistic regressions and receiver operating characteristic analyses were used to differentiate patients from the CRASH and IMPACT prognostic models. RESULTS: All models had low R 2 values (0.17-0.23) with AUC values from 0.77 to 0.81 and overall accuracies ranging from 72.4% to 78.3%. Sensitivity (35.3-50.0) and positive predictive values (66.7-69.2) were poor in the CRASH models, while specificity (52.3-53.1) and negative predictive values (58.1-63.6) were poor in IMPACT models. All models had unacceptable false positive rates (20.8%-33.3%). CONCLUSION: Our results were consistent with previous literature regarding discriminative validity (AUC = 0.77-0.81). However, accuracy and false positive rates of both the CRASH and IMPACT models were poor.