Citation

Greenberg JK, Ahluwalia R, Hill M, Johnson G, Hale AT, Belal A, Baygani S, Olsen MA, Foraker RE, Carpenter CR, Yan Y, Ackerman L, Noje C, Jackson E, Burns E, Sayama CM, Selden NR, Vachhrajani S, Shannon CN, Kuppermann N, Limbrick DDJ. Acad. Emerg. Med. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Society for Academic Emergency Medicine, Publisher John Wiley and Sons)

DOI

10.1111/acem.14333

PMID

unavailable

Abstract

BACKGROUND: Clinical decision support may improve the post-neuroimaging management of children with mild traumatic brain injuries (mTBI) and intracranial injuries. While the CHIIDA score has been proposed for this purpose, a more sensitive risk model may have broader use. Consequently, this study's objectives were to: 1) develop a new risk model with improved sensitivity compared to the CHIIDA model; and 2) externally validate the new model and CHIIDA model in a multicenter dataset.

METHODS: We analyzed children ≤ 18 years-old with mTBI and intracranial injuries included in the PECARN head injury dataset (2004-2006). We used binary recursive partitioning to predict the composite outcome of neurosurgical intervention, intubation for > 24 hours due to TBI, or death due to TBI. The new model was externally validated in a separate dataset that included children treated at any one of six centers from 2006-2019.

RESULTS: Based on 839 patients from the PECARN dataset, a new risk model, the KIIDS-TBI model, was developed that incorporated imaging (e.g. midline shift) and clinical (e.g. GCS score) findings. Based on the model-predicted probability of the composite outcome, three cutoffs were evaluated to classify patients as 'high risk' for level of care decisions. In the external validation dataset consisting of 1,630 patients, the most conservative cutoff (i.e. any predictor present) identified 119/119 children with the composite outcome (sensitivity 100%), but had the lowest specificity (26.3%). The other two decision-making cutoffs had worse sensitivity (94.1%-96.6%) but improved specificity (67.4%-81.3%). The CHIIDA model lacked the most conservative cutoff and otherwise showed the same or slightly worse performance compared to the other two cutoffs.

CONCLUSIONS: The KIIDS-TBI model has high sensitivity and moderate specificity for risk-stratifying children with mTBI and intracranial injuries. Use of this clinical decision support tool may help improve the safe, resource-efficient management of this important patient population.

Language: en