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Browsing by Author "Ackerman, Laurie"
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Item Development and external validation of the KIIDS-TBI tool for managing children with mild traumatic brain injury and intracranial injuries(Wiley, 2021-12) Greenberg, Jacob K.; Ahluwalia, Ranbir; Hill, Madelyn; Johnson, Gabbie; Hale, Andrew T.; Belal, Ahmed; Baygani, Shawyon; Olsen, Margaret A.; Foraker, Randi E.; Carpenter, Carpenter; Yan, Yan; Ackerman, Laurie; Noje, Corina; Jackson, Eric; Burns, Erin; Sayama, Christina M.; Selden, Nathan R.; Vachhrajani, Shobhan; Shannon, Chevis N.; Kuppermann, Nathan; Limbrick, David D., Jr.; Neurological Surgery, School of MedicineBackground Clinical decision support (CDS) may improve the postneuroimaging 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 data set. Methods We analyzed children ≤18 years old with mTBI and intracranial injuries included in the PECARN head injury data set (2004–2006). We used binary recursive partitioning to predict the composite outcome of neurosurgical intervention, intubation for > 24 h due to TBI, or death due to TBI. The new model was externally validated in a separate data set that included children treated at any one of six centers from 2006 to 2019. Results Based on 839 patients from the PECARN data set, a new risk model, the KIIDS-TBI model, was developed that incorporated imaging (e.g., midline shift) and clinical (e.g., Glasgow Coma Scale 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 data set consisting of 1,630 patients, the most conservative cutoff (i.e., any predictor present) identified 119 of 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 CDS tool may help improve the safe, resource-efficient management of this important patient population.Item Diagnostic Performance of Ultrafast Brain MRI for Evaluation of Abusive Head Trauma(2017-04) Kralik, Stephen; Yasrebi, Mona; Supakul, Nucharin; Lin, Chen; Netter, Lynn; Hicks, Ralph; Hibbard, Roberta; Ackerman, Laurie; Harris, Mandy; Ho, Chang; Radiology and Imaging Sciences, School of MedicineBACKGROUND AND PURPOSE: MR imaging with sedation is commonly used to detect intracranial traumatic pathology in the pediatric population. Our purpose was to compare nonsedated ultrafast MR imaging, noncontrast head CT, and standard MR imaging for the detection of intracranial trauma in patients with potential abusive head trauma. MATERIALS AND METHODS: A prospective study was performed in 24 pediatric patients who were evaluated for potential abusive head trauma. All patients received noncontrast head CT, ultrafast brain MR imaging without sedation, and standard MR imaging with general anesthesia or an immobilizer, sequentially. Two pediatric neuroradiologists independently reviewed each technique blinded to other modalities for intracranial trauma. We performed interreader agreement and consensus interpretation for standard MR imaging as the criterion standard. Diagnostic accuracy was calculated for ultrafast MR imaging, noncontrast head CT, and combined ultrafast MR imaging and noncontrast head CT. RESULTS: Interreader agreement was moderate for ultrafast MR imaging (κ = 0.42), substantial for noncontrast head CT (κ = 0.63), and nearly perfect for standard MR imaging (κ = 0.86). Forty-two percent of patients had discrepancies between ultrafast MR imaging and standard MR imaging, which included detection of subarachnoid hemorrhage and subdural hemorrhage. Sensitivity, specificity, and positive and negative predictive values were obtained for any traumatic pathology for each examination: ultrafast MR imaging (50%, 100%, 100%, 31%), noncontrast head CT (25%, 100%, 100%, 21%), and a combination of ultrafast MR imaging and noncontrast head CT (60%, 100%, 100%, 33%). Ultrafast MR imaging was more sensitive than noncontrast head CT for the detection of intraparenchymal hemorrhage (P = .03), and the combination of ultrafast MR imaging and noncontrast head CT was more sensitive than noncontrast head CT alone for intracranial trauma (P = .02). CONCLUSIONS: In abusive head trauma, ultrafast MR imaging, even combined with noncontrast head CT, demonstrated low sensitivity compared with standard MR imaging for intracranial traumatic pathology, which may limit its utility in this patient population.