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Abstract
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OBJECTIVES: This study aimed to evaluate the trauma and injury severity score (TRISS), IMPACT (international mission for prognosis and analysis of clinical trials), and CRASH (corticosteroid randomization after significant head injury) prognostic models for prediction of outcome after moderate-to-severe traumatic brain injury (TBI) in the elderly following road traffic accident.
DESIGN: This was a prospective observational study.
MATERIALS AND METHODS: This was a prospective observational study on 104 elderly trauma patients who were admitted to tertiary care hospital, over a consecutive period of 18 months from December 2016 to May 2018. On the day of admission, data were collected from each patient to compute the TRISS, IMPACT, and CRASH and outcome evaluation was prospectively done at discharge, 14(th) day, and 6-month follow-up.
RESULTS: This study included 104 TBI patients with a mean age of 66.75 years and with a mortality rate of 32% and 45%, respectively, at discharge and at the end of 6 months. The predictive accuracies of the TRISS, CRASH (computed tomography), and IMPACT (core, extended, laboratory) were calculated using receiver operator characteristic (ROC) curves for the prediction of mortality. Best cutoff point for predicting mortality in elderly TBI patients using TRISS system was a score of ≤88 (sensitivity 94%, specificity of 80%, and area under ROC curve 0.95), similarly cutoff point under the CRASH at 14 days was score of >35 (100%, 80%, 0.958); for CRASH at 6 months, best cutoff point was at >84 (88%, 88%, 0.959); for IMPACT (core), it was >38 (88%, 93%, 0.976); for IMPACT (extended), it was >27 (91%, 89%, 0.968); and for IMPACT (lab), it was >41 (82%, 100%, 0.954). There were statistical differences among TRISS, CRASH (at 14 days and 6 months), and IMPACT (core, extended, lab) in terms of area under the ROC curve (P < 0.0001).
CONCLUSION: IMPACT (core, extended) models were the strongest predictors of mortality in moderate-to-severe TBI when compared with the TRISS, CRASH, and IMPACT (lab) models.
Language: en |