Probabilistic matching approach to link deidentified data from a trauma registry and a traumatic brain injury model system center

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Article describes the creation of an algorithm used to probabilistically link the Traumatic Brain Injury Model System (TBIMS) data set to trauma data in state and national trauma databases. When linking through probabilistic means, common data elements in two large, independent data sets can be compared to assess the likelihood that two patients are the same, given equal values on a number of variables. 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. The algorithm generation subset had 121 patients. It had a sensitivity of 88 percent and a positive predictive value of 99 percent. The validation subset consisted of 120 patients and had a sensitivity of 83 percent and a positive predictive value of 99 percent. 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 healthcare utilization and recovery across the care continuum in traumatic brain injury populations.

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