ArticlePloS one2019
External validation of the TRISS, CRASH, and IMPACT prognostic models in severe traumatic brain injury in Japan.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 4 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- A Meta-analysis of Predicting Disorders of Consciousness After Traumatic Brain Injury by Machine Learning Models.Alpha psychiatry · 2024Pooled it
- Guidelines for Neuroprognostication in Critically Ill Adults with Moderate-Severe Traumatic Brain Injury.Neurocritical care · 2024Pooled it
- Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Prognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis.Neurocritical care · 2022Pooled it
- Evaluating and Updating the IMPACT Model to Predict Outcomes in Two Contemporary North American Traumatic Brain Injury Cohorts.Journal of neurotrauma · 2025Trial
- Outcome in adult patients with severe TBI, using a management protocol allowing a cerebral perfusion pressure ≤ 60 mmHg - a retrospective study over 10 years.Acta neurochirurgica · 2025Article
- Domain validation of the CRASH prognostic model for predicting 14-day mortality among patients and traumatic brain injury and intracranial hemorrhage in a Thai emergency department.International journal of emergency medicine · 2025Article
- Results of therapy in children diagnosed with severe traumatic brain injury.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Comparative Analysis of "Trauma and Injury Severity Scores" and "Madras Head Injury Prognostic Scale" in Assessing Head Trauma Prognosis in the Emergency Department of Shahid Beheshti Hospital, Sabzevar, Iran.Bulletin of emergency and trauma · 2025Article
- Settlement Is at the End-Common Trauma Scores Require a Critical Reassessment Due to the Possible Dynamics of Traumatic Brain Injuries in Patients' Clinical Course.Journal of clinical medicine · 2024Article
- Quality of pediatric trauma care: development of an age-adjusted TRISS model and survival benchmarking in a major trauma center.Frontiers in pediatrics · 2024Article
- Comparative analysis of CRASH and IMPACT in predicting the outcome of 340 patients with traumatic brain injury.Translational neuroscience · 2024Article
- Prognostic Value of CRASH and IMPACT Models for Predicting Mortality and Unfavorable Outcome in Traumatic Brain Injury; a Systematic Review and Meta-Analysis.Archives of academic emergency medicine · 2023Review
- Epidemiological characteristics for patients with traumatic brain injury and the nomogram model for poor prognosis: an 18-year hospital-based study.Frontiers in neurology · 2023Article
- Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans.Radiology · 2022Article
- A Precision Medicine Agenda in Traumatic Brain Injury.Frontiers in pharmacology · 2022Review
- Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records.Frontiers in neurology · 2022Article
- Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation.Frontiers in rehabilitation sciences · 2022Article
- Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: A data-based study.Frontiers in neuroscience · 2022Article
- Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury.Journal of personalized medicine · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In Japan, a range of patients with traumatic brain injury (TBI) has been recorded in a nationwide database (Japan Neurotrauma Data Bank; JNTDB). This study aimed to externally validate three international prediction models using JNTDB data: Trauma and Injury Severity Score (TRISS), Corticosteroid Randomization After Significant Head Injury (CRASH), and International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT). We also aimed to validate the applicability of these models in the Japanese population. Of 1,091 patients registered in the JNTDB from July 2009 to June 2011, we analyzed data for 635 patients. We examined factors associated with mortality in-hospital and unfavorable outcomes 6 months after TBI by applying the TRISS, CRASH, and IMPACT models. We also conducted an external validation of these models based on these data. The patients' mean age was 60.1 ±21.1 years, and 342 were alive at the time of discharge (53.9%). Univariate analysis revealed eight major risk factors for mortality in-hospital: age, Glasgow Coma Scale (GCS), Injury Severity Score (ISS), systolic blood pressure, heart rate, mydriasis, acute epidural hematoma (AEDH), and traumatic subarachnoid hemorrhage. A similar analysis identified five risk factors for unfavorable outcomes at 6 months: age, GCS, ISS, mydriasis, and AEDH. For mortality in-hospital, the TRISS had a satisfactory area under the curve value (0.75). For unfavorable outcomes at 6 months, the CRASH (basic and computed tomography) and IMPACT (core and core extended) models had satisfactory area under the curve values (0.86, 0.86, 0.81, and 0.85, respectively). The TRISS, CRASH, and IMPACT models were suitable for application to the JNTDB population, indicating these models had high value in Japanese patients with neurotrauma.
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