Literature DB >> 31959626

IMPACT and CRASH prognostic models for traumatic brain injury: external validation in a South-American cohort.

Kwankaew Wongchareon1, Hilaire J Thompson2, Pamela H Mitchell2, Jason Barber3, Nancy Temkin3.   

Abstract

OBJECTIVE: To 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.
METHOD: A 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.
RESULTS: There 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.
CONCLUSION: The 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.

Entities:  

Keywords:  functional outcome; low-middle income country; outcome evaluation; traumatic brain injury

Year:  2020        PMID: 31959626     DOI: 10.1136/injuryprev-2019-043466

Source DB:  PubMed          Journal:  Inj Prev        ISSN: 1353-8047            Impact factor:   2.399


  5 in total

Review 1.  Prognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis.

Authors:  Rita de Cássia Almeida Vieira; Juliana Cristina Pereira Silveira; Wellingson Silva Paiva; Daniel Vieira de Oliveira; Camila Pedroso Estevam de Souza; Eduesley Santana-Santos; Regina Marcia Cardoso de Sousa
Journal:  Neurocrit Care       Date:  2022-08-09       Impact factor: 3.532

2.  Development and Verification of Prognostic Prediction Models for Patients with Brain Trauma Based on Coagulation Function Indexes.

Authors:  Lanjuan Xu; Tingting An; Chengjian Li; Xin Shi; Bo Yang
Journal:  J Immunol Res       Date:  2022-07-26       Impact factor: 4.493

3.  Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records.

Authors:  João Fonseca; Xiuyun Liu; Hélder P Oliveira; Tania Pereira
Journal:  Front Neurol       Date:  2022-06-10       Impact factor: 4.086

4.  Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation.

Authors:  Irene Say; Yiling Elaine Chen; Matthew Z Sun; Jingyi Jessica Li; Daniel C Lu
Journal:  Front Rehabil Sci       Date:  2022-09-22

5.  Selection of CT variables and prognostic models for outcome prediction in patients with traumatic brain injury.

Authors:  Djino Khaki; Virpi Hietanen; Alba Corell; Helena Odenstedt Hergès; Johan Ljungqvist
Journal:  Scand J Trauma Resusc Emerg Med       Date:  2021-07-17       Impact factor: 2.953

  5 in total

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