Related Experiment Video
Updated: Mar 22, 2026
Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
Published on: May 27, 2022
Probabilistic Matching Approach to Link Deidentified Data from a Trauma Registry and a Traumatic Brain Injury Model
Matthew Ryan Kesinger1, Raj Gopalan Kumar, Anne Connelly Ritter
1From the Departments of Surgery (MRK, JLS), Physical Medicine and Rehabilitation (RGK, ACR, AKW), Epidemiology (RGK, ACR), and Neuroscience (AKW) and Safar Center for Resuscitation Research (AKW), University of Pittsburgh, Pennsylvania.
Objective:
There is no civilian traumatic brain injury 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 System (TBIMS) to trauma data in state and national trauma databases.
Design:
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.
Results:
The algorithm generation subset had 121 patients. It had a sensitivity of 88% and a positive predictive value of 99%. The validation subset consisted of 120 patients and had a sensitivity of 83% and a positive predictive value of 99%.
Conclusions:
The probabilistic linkage algorithm can accurately link TBIMS data across systems of trauma care. Future studies can use this database to answer meaningful research questions regarding the long-term impact of the acute trauma complex on health care utilization and recovery across the care continuum in traumatic brain injury populations.
