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
Issued Date
2025-12-01
Resource Type
ISSN
18651372
eISSN
18651380
Scopus ID
2-s2.0-105018875792
Journal Title
International Journal of Emergency Medicine
Volume
18
Issue
1
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SCOPUS
Bibliographic Citation
International Journal of Emergency Medicine Vol.18 No.1 (2025)
Suggested Citation
Tienpratarn W., Phinyo P., Yuksen C., Wongwaisayawan S., Khorana J., Patumanond J. 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 Vol.18 No.1 (2025). doi:10.1186/s12245-025-01008-w Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112728
Title
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
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Abstract
Background: Traumatic brain injury (TBI) is a significant health concern, with intracranial haemorrhage (ICH) being a common complication following injury. The CRASH prediction model plays a crucial role in clinical prognostication and decision-making within this patient group. However, external validation is critical to ensure the model’s validity and applicability across different populations and settings beyond those in which it was originally developed. This study aimed to validate the CRASH prediction model for 14-day mortality among TBI patients with ICH presenting to a Thai emergency department. Methods: This retrospective study included adult TBI patients with ICH who visited the emergency department (ED) at Ramathibodi Hospital, Thailand, between 2020 and 2022. The Basic model, which incorporates age, Glasgow Coma Scale (GCS) score (3–15), pupillary reaction, and major extracranial injury, and the CT model, which extends the Basic model by including CT findings, were evaluated for their discriminative ability and calibration. Results: A total of 232 patients were included in the validation dataset. Significant differences in clinical characteristics were observed between the datasets, including older age, predominance of mild TBI, subarachnoid hemorrhage, and non-evacuated hematoma in the validation dataset. The observed 14-day mortality rate in this cohort was 9.1%, compared to 20.7% in the development dataset. The area under the receiver operating characteristics curve (AuROC) was 0.92 (95% CI: 0.84, 1.00) for the Basic model and 0.93 (95% CI: 0.86, 1.00) for the CT model. However, the calibration for both models was fair. Recalibration achieved better predictive accuracy and reduced overestimation in high-risk groups. Conclusion: The original CRASH prediction model demonstrates strong discriminative ability for predicting 14-day mortality in TBI patients; however, significant miscalibration was observed. Recalibration was therefore undertaken to improve the model’s generalisability to local populations. Nonetheless, further studies are warranted to confirm the consistency and applicability of the recalibrated models.