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J Trauma Stress. 2019 Apr;32(2):226-237. doi: 10.1002/jts.22399.

Validation of an Electronic Medical Record-Based Algorithm for Identifying Posttraumatic Stress Disorder in U.S. Veterans.

Author information

Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA.
Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, USA.
Psychiatry Service, VA San Diego Healthcare System, San Diego, California, USA.
Departments of Psychiatry and Family Medicine & Public Health, University of California San Diego, La Jolla, California, USA.
Psychiatry Service, VA Connecticut Healthcare System, West Haven, Connecticut, USA.
Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.
VA Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut, USA.
Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, USA.
Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, Connecticut, USA.


in English, Chinese, Spanish

We developed an algorithm for identifying U.S. veterans with a history of posttraumatic stress disorder (PTSD), using the Department of Veterans Affairs (VA) electronic medical record (EMR) system. This work was motivated by the need to create a valid EMR-based phenotype to identify thousands of cases and controls for a genome-wide association study of PTSD in veterans. We used manual chart review (n = 500) as the gold standard. For both the algorithm and chart review, three classifications were possible: likely PTSD, possible PTSD, and likely not PTSD. We used Lasso regression with cross-validation to select statistically significant predictors of PTSD from the EMR and then generate a predicted probability score of being a PTSD case for every participant in the study population (range: 0-1.00). Comparing the performance of our probabilistic approach (Lasso algorithm) to a rule-based approach (International Classification of Diseases [ICD] algorithm), the Lasso algorithm showed modestly higher overall percent agreement with chart review than the ICD algorithm (80% vs. 75%), higher sensitivity (0.95 vs. 0.84), and higher accuracy (AUC = 0.95 vs. 0.90). We applied a 0.7 probability cut-point to the Lasso results to determine final PTSD case-control status for the VA population. The final algorithm had a 0.99 sensitivity, 0.99 specificity, 0.95 positive predictive value, and 1.00 negative predictive value for PTSD classification (grouping possible PTSD and likely not PTSD) as determined by chart review. This algorithm may be useful for other research and quality improvement endeavors within the VA.


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