A Probabilistic Matching Approach to Link De-identified Data from a Trauma Registry and a Traumatic Brain Injury Model System Center
Published Date:Jan 2017
Source:Am J Phys Med Rehabil. 96(1):17-24.
Pubmed Central ID:PMC5065730
Funding:K23 GM093032/GM/NIGMS NIH HHS/United States
R25 MH054318/MH/NIMH NIH HHS/United States
U48 DP000041/DP/NCCDPHP CDC HHS/United States
UL1 TR000005/TR/NCATS NIH HHS/United States
There is no civilian TBI database that captures patients in all settings of the care-continuum. The linkage of such databases would yield valuable insight into possible care interventions. Thus, the objective of this article is to describe the creation of an algorithm used to link the Traumatic Brain Injury Model Systems (TBIMS) to trauma data in state and national trauma databases.
The TBIMS data from a single center was randomly divided into two sets. One subset was used to generate a probabilistic linking algorithm to link the TBIMS data to the center’s trauma registry. The other subset was used to validate the algorithm. Medical record numbers were obtained and used as unique identifiers to measure the quality of the linkage. Novel methods were used to maximize the positive predictive value (PPV).
The algorithm generation subset had 121 patients. It had a sensitivity of 88% and a PPV of 99%. The validation subset consisted of 120 patients, and had a sensitivity of 83% and a PPV of 99%.
The probabilistic linkage algorithm can accurately link TBIMS data across systems of trauma care. Future studies can utilize this database to answer meaningful research questions regarding the long-term impact of acute trauma complex on healthcare utilization and recovery across the care-continuum in TBI populations.
Supporting Files:No Additional Files
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