ArticleAmerican journal of physical medicine & rehabilitation2017
Probabilistic Matching Approach to Link Deidentified Data from a Trauma Registry and a Traumatic Brain Injury Model System Center.
Article in American journal of physical medicine & rehabilitation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Research Needs for Prognostic Modeling and Trajectory Analysis in Patients with Disorders of Consciousness.Neurocritical care · 2021Article
- The Traumatic Brain Injury Model Systems National Database: A Review of Published Research.Neurotrauma reports · 2021Review
- Interrelationships Between Post-TBI Employment and Substance Abuse: A Cross-lagged Structural Equation Modeling Analysis.Archives of physical medicine and rehabilitation · 2020Article
- Effects of hospital-acquired pneumonia on long-term recovery and hospital resource utilization following moderate to severe traumatic brain injury.The journal of trauma and acute care surgery · 2020Article
- Linking Individual Data From the Spinal Cord Injury Model Systems Center and Local Trauma Registry: Development and Validation of Probabilistic Matching Algorithm.Topics in spinal cord injury rehabilitation · 2020Article
- Deterministic vs. Probabilistic: Best Practices for Patient Matching Based on a Comparison of Two Implementations.Journal of digital imaging · 2019Article
- Probabilistic Matching of Deidentified Data From a Trauma Registry and a Traumatic Brain Injury Model System Center: A Follow-up Validation Study.American journal of physical medicine & rehabilitation · 2018Article
- Acute Trauma Factor Associations With Suicidality Across the First 5 Years After Traumatic Brain Injury.Archives of physical medicine and rehabilitation · 2016Article
- Thirty Years of National Institute on Disability, Independent Living, and Rehabilitation Research Traumatic Brain Injury Model Systems Center Research-An Update.The Journal of head trauma rehabilitationArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveThere 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.
designThe 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.
resultsThe 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%.
conclusionsThe 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.
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