TY  - JOUR
PY  - 2021//
TI  - Development and external validation of the KIIDS-TBI Tool for managing children with mild traumatic brain injury and intracranial injuries
JO  - Academic emergency medicine
A1  - Greenberg, Jacob K.
A1  - Ahluwalia, Ranbir
A1  - Hill, Madelyn
A1  - Johnson, Gabbie
A1  - Hale, Andrew T.
A1  - Belal, Ahmed
A1  - Baygani, Shawyon
A1  - Olsen, Margaret A.
A1  - Foraker, Randi E.
A1  - Carpenter, Christopher R.
A1  - Yan, Yan
A1  - Ackerman, Laurie
A1  - Noje, Corina
A1  - Jackson, Eric
A1  - Burns, Erin
A1  - Sayama, Christina M.
A1  - Selden, Nathan R.
A1  - Vachhrajani, Shobhan
A1  - Shannon, Chevis N.
A1  - Kuppermann, Nathan
A1  - Limbrick, David D. Jr
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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. <br><br>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. <br><br>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. <br><br>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.<p />  <p>Language: en</p>
LA  - en
SN  - 1069-6563
UR  - http://dx.doi.org/10.1111/acem.14333
ID  - ref1
ER  -