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Observational Study
IMPACT and CRASH prognostic models for traumatic brain injury: external validation in a South-American cohort.
- Kwankaew Wongchareon, Hilaire J Thompson, Pamela H Mitchell, Jason Barber, and Nancy Temkin.
- Adult and Gerontology Nursing, Naresuan University Faculty of Nursing, Phitsanulok, Thailand kwankaeww@nu.ac.th.
- Inj. Prev. 2020 Dec 1; 26 (6): 546-554.
ObjectiveTo develop a robust prognostic model, the more diverse the settings in which the system is tested and found to be accurate, the more likely it will be generalisable to untested settings. This study aimed to externally validate the International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticosteroid Randomization after Significant Head Injury (CRASH) models for low-income and middle-income countries using a dataset of patients with severe traumatic brain injury (TBI) from the Benchmark Evidence from South American Trials: Treatment of Intracranial Pressure study and a simultaneously conducted observational study.MethodA total of 550 patients with severe TBI were enrolled in the study, and 466 of those were included in the analysis. Patient admission characteristics were extracted to predict unfavourable outcome (Glasgow Outcome Scale: GOS<3) and mortality (GOS 1) at 14 days or 6 months.ResultsThere were 48% of the participants who had unfavourable outcome at 6 months and these included 38% who had died. The area under the receiver operating characteristic curve (AUC) values were 0.683-0.775 and 0.640-0.731 for the IMPACT and CRASH models respectively. The IMPACT CT model had the highest AUC for predicting unfavourable outcomes, and the IMPACT Lab model had the best discrimination for predicting 6-month mortality. The discrimination for both the IMPACT and CRASH models improved with increasing complexity of the models. Calibration revealed that there were disagreement between observed and predicted outcomes in the IMPACT and CRASH models.ConclusionThe overall performance of all IMPACT and CRASH models was adequate when used to predict outcomes in the dataset. However, some disagreement in calibration suggests the necessity for updating prognostic models to maintain currency and generalisability.© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.
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