Traumatic Brain Injury Surveillance and Research with Electronic Health Records: Building New Capacities

Abstract

Between 3.2 and 5.3 million U.S. civilians live with traumatic brain injury (TBI)-related disabilities. Although the post-acute phase of TBI has been recognized as both a discrete disease process and risk factor for chronic conditions, TBI is not recognized as a chronic disease. TBI epidemiology draws upon untimely, incomplete, cross-sectional, administrative datasets. The adoption of electronic health records (EHR) may supplement traditional datasets for public health surveillance and research. Methods Indiana constructed a state-wide clinical TBI registry from longitudinal (2004-2018) EHRs. This dissertation includes three distinct studies to enhance, evaluate, and apply the registry: 1) development and evaluation of a natural language processing algorithm for identification of TBI severity within free-text notes; 2) evaluation and comparison of the performance of the ICD-9-CM and ICD-10-CM surveillance definitions; and 3) estimating the effect of mild TBI (mTBI) on the risk of post-acute chronic conditions compared to individuals without mTBI. Results Automated extraction of Glasgow Coma Scale from clinical notes was feasible and demonstrated balanced recall and precision (F-scores) for classification of mild (99.8%), moderate (100%), and severe (99.9%) TBI. We observed poor sensitivity for ICD-10-CM TBI surveillance compared to ICD-9-CM (0.212 and 0.601, respectively), resulting in potentially 5-fold underreporting. ICD-10-CM was not statistically equivalent to ICD-9-CM for sensitivity (𝑑𝑑𝑑𝑑̂=0.389, 95% CI [0.388,0.405]) or positive predictive value (𝑑𝑑𝑑𝑑̂=-0.353, 95% CI [-0.362,-0.344]). Compared to a matched cohort, individuals with mTBI were more likely to be diagnosed with mental health, substance use, neurological, cardiovascular, and endocrine conditions. Conclusion ICD-9-CM and ICD-10-CM surveillance definitions were not equivalent, and the transition resulted in a underreporting incidence for mTBI. This has direct implications on existing and future TBI registries and the Report to Congress on Traumatic Brain Injury in the United States. The supplementation of state-based trauma registries with structured and unstructured EHR data is effective for studying TBI outcomes. Our findings support the classification of TBI as a chronic disease by funding bodies, which may improve public funding to replace legacy systems to improve standardization, timeliness, and completeness of the epidemiology and post-acute outcomes of TBI.