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Journal Article

Citation

Kumar RG, Wang Z, Kesinger MR, Newman M, Huynh TT, Niemeier JP, Sperry JL, Wagner AK. Am. J. Phys. Med. Rehabil. 2018; 97(4): 236-241.

Affiliation

From the Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania (RGK, ZW, AKW); Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania (RGK, ZW); Department of Physical Medicine and Rehabilitation, Carolinas Rehabilitation, Charlotte, North Carolina (MN, JPN); Acute Care Trauma Surgery, Carolinas Healthcare, Charlotte, North Carolina (TTH); Department of Trauma, University of Pittsburgh, Pittsburgh, Pennsylvania (MRK, JLS); Center for Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania (AKW); Safar Center for Resuscitation Research, University of Pittsburgh, Pittsburgh, Pennsylvania (AKW); and Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania (AKW).

Copyright

(Copyright © 2018, Lippincott Williams and Wilkins)

DOI

10.1097/PHM.0000000000000838

PMID

29557888

Abstract

In a previous study, individuals from a single Traumatic Brain Injury Model Systems and trauma center were matched using a novel probabilistic matching algorithm. The Traumatic Brain Injury Model Systems is a multicenter prospective cohort study containing more than 14,000 participants with traumatic brain injury, following them from inpatient rehabilitation to the community over the remainder of their lifetime. The National Trauma Databank is the largest aggregation of trauma data in the United States, including more than 6 million records. Linking these two databases offers a broad range of opportunities to explore research questions not otherwise possible. Our objective was to refine and validate the previous protocol at another independent center. An algorithm generation and validation data set were created, and potential matches were blocked by age, sex, and year of injury; total probabilistic weight was calculated based on of 12 common data fields. Validity metrics were calculated using a minimum probabilistic weight of 3. The positive predictive value was 98.2% and 97.4% and sensitivity was 74.1% and 76.3%, in the algorithm generation and validation set, respectively. These metrics were similar to the previous study. Future work will apply the refined probabilistic matching algorithm to the Traumatic Brain Injury Model Systems and the National Trauma Databank to generate a merged data set for clinical traumatic brain injury research use.


Language: en

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