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Prognostic Validation of the NINDS Standardized Pathoanatomic Terms and Definitions for the Reporting of Acute Traumatic Brain Injuries: A CENTER-TBI study
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2019 (English)In: Journal of Neurotrauma, ISSN 0897-7151, E-ISSN 1557-9042Article in journal (Refereed) Epub ahead of print
Abstract [en]

PURPOSE: To aim of this study is to investigate the prognostic value of using the NINDS standardized imaging-based pathoanatomic descriptors for the evaluation and reporting of acute TBI lesions.

METHODS: For a total of 3,392 patients (2,244 males and 1,148 females, Median = 51 years) enrolled in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study, we extracted 96 CDEs from the structured reports, spanning all three levels of pathoanatomic information (i.e. 20 "basic", 60 "descriptive" and 16 "advanced" CDE variables per patient). 6-month clinical outcome scores were dichotomized into favorable (GOS-E = 5-8) versus unfavorable (GOS-E = 1-4). Regularized logistic regression models were constructed and compared using the optimism-corrected AUC.

RESULTS: An abnormality was reported for the majority of patients (64.51%). In 79.11% of those patients, there was at least one coexisting pathoanatomic lesion or associated finding. An increase in lesion severity, laterality and volume was associated with more unfavorable outcomes. Compared to the full set of pathoanatomic descriptors (i.e. all three categories of information), reporting "basic" CDE information provides at least equal discrimination between patients with favorable versus unfavorable outcome (AUC = 0.8121 vs. 0.8155, respectively). Addition of a selected subset of "descriptive" detail (i.e. location and volume) to specific basic CDEs could improve outcome prediction (AUC = 0.8248). Addition of "advanced" or "emerging" information had minimal prognostic value.

CONCLUSION: Our results show that the NINDS standardized-imaging based pathoanatomic descriptors can be used in large-scale studies and provide important insights into acute TBI lesion patterns. When used in clinical predictive models, they can provide excellent discrimination between patients with favorable and unfavorable 6-month outcomes. If further validated, our findings could support the development of structured and itemized templates in routine clinical radiology. Key words: Traumatic Brain Injury, Computed Tomography, Common Data Elements, Structured Reporting.

Place, publisher, year, edition, pages
Mary Ann Liebert, 2019.
Keywords [en]
ADULT BRAIN INJURY, CT SCANNING, RADIOLOGY, TRAUMATIC BRAIN INJURY
National Category
Neurology
Research subject
Neurosurgery
Identifiers
URN: urn:nbn:se:umu:diva-167906DOI: 10.1089/neu.2019.6710PubMedID: 31813313OAI: oai:DiVA.org:umu-167906DiVA, id: diva2:1391851
Funder
EU, FP7, Seventh Framework ProgrammeAvailable from: 2020-02-05 Created: 2020-02-05 Last updated: 2020-02-07

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