To the Editor:We read with great interest the publication titled "Prognostic Models for Traumatic Brain Injury Have Good Discrimination but Poor Overall Model Performance for Predicting Mortality and Unfavorable Outcomes" 1 and congratulate the authors on this important work. The authors leveraged a cohort of 467 patients with severe traumatic brain injury (TBI) at a level I trauma center to externally validate the Corticosteroid Randomization After Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) models for prediction of death and poor outcome after severe TBI at the individual patient level. 1 The authors found that both models have reasonable discrimination (ie, delineating patients with and without the outcome of interest) but poor accuracy and high false-positive rates. They urge caution when using these models to inform clinical decision-making.We are writing to discuss these results in the context of the recent update to the Commission on TBI published in Lancet Neurology titled "Traumatic brain injury: progress and challenges in prevention, clinical care, and research." 2 This roadmap paper for TBI discusses the utility of CRASH and IMPACT models in Section 6: Prognosis in TBI and supports the conclusion of Eagle et al-these models are currently not appropriate for real-time clinical decision-making.In addition to concerns about the wide range of prediction performance raised by Eagle et al, the updated Commission on TBI highlights that both models were originally developed on patient data collected 20 to 30 years ago with the purpose of identifying patients for clinical trial inclusion and only incorporate patient characteristics available upon initial presentation. Moreover, the Commission reports that including UCH-L1 to the IMPACT and CRASH models increases the explanatory power of these models. Dynamic prediction modeling represents another