Development and validation of an electronic health record-based algorithm for identifying TBI in the VA: A VA Million Veteran Program study

• Published in: Brain injury Vol. 38; no. 13; p. 1084
• Main Authors: Merritt, Victoria C, Chen, Alicia W, Bonzel, Clara-Lea, Hong, Chuan, Sangar, Rahul, Morini Sweet, Sara, Sorg, Scott F, Chanfreau-Coffinier, Catherine
• Format: Journal Article
• Language: English
• Published: England 09.11.2024
• Subjects: Adult; Algorithms; Brain Injuries, Traumatic - diagnosis; Electronic Health Records; Female; Humans; Male; Middle Aged; Reproducibility of Results; United States; United States Department of Veterans Affairs; Veterans; United States; electronic medical record; TBI; algorithm development; military; Veterans affairs; phenotyping
• ISSN: 1362-301X
• DOI: 10.1080/02699052.2024.2373920

The purpose of this study was to develop and validate an algorithm for identifying Veterans with a history of traumatic brain injury (TBI) in the Veterans Affairs (VA) electronic health record using VA Million Veteran Program (MVP) data. Manual chart review (  = 200) was first used to establish 'gold standard' diagnosis labels for TBI ('Yes TBI' vs. 'No TBI'). To develop our algorithm, we used PheCAP, a semi-supervised pipeline that relied on the chart review diagnosis labels to train and create a prediction model for TBI. Cross-validation was used to train and evaluate the proposed algorithm, 'TBI-PheCAP.' TBI-PheCAP performance was compared to existing TBI algorithms and phenotyping methods, and the final algorithm was run on all MVP participants (  = 702,740) to assign a predicted probability for TBI and a binary classification status choosing specificity = 90%. The TBI-PheCAP algorithm had an area under the receiver operating characteristic curve of 0.92, sensitivity of 84%, and positive predictive value (PPV) of 98% at specificity = 90%. TBI-PheCAP generally performed better than other classification methods, with equivalent or higher sensitivity and PPV than existing rules-based TBI algorithms and MVP TBI-related survey data. Given its strong classification metrics, the TBI-PheCAP algorithm is recommended for use in future population-based TBI research.