Ascertainment of Infection and Eradication Treatment Using a Nationwide Electronic Health Record Database.

Background And Aims: There are limited contemporary population-based data on epidemiology and outcomes in the United States. Our primary aim was to create a validated cohort of veterans with testing or treatment using Veterans Health Administration data.

Methods: Using Veterans Health Administration structured and unstructured data, we developed and validated 4 algorithms for infection (3 algorithms) and treatment status (1 algorithm). During the development phase, we iteratively modified each algorithm based on a manual review of random sets of electronic health records (reference standard). The validation goal was to achieve a one-sided 95% confidence lower bound (LB) for positive predictive value (PPV) and/or negative predictive value (NPV) >90%. We applied the Bonferroni correction when both PPV and NPV were relevant.

Results: For infection, we achieved 99.0% PPV (LB = 94.6%) and 100% NPV (LB = 96.4%) for discriminating positive vs negative status using structured (ie, laboratory tests) and 95% PPV (LB = 90.3%) and 97.9% NPV (LB = 93.9%) using unstructured (ie, histopathology reports) data. Diagnostic codes achieved 98% PPV (LB = 93.0%) for diagnosis. The treatment algorithm was composed of multiple antimicrobial combinations and overall achieved ≥98% PPV (LB = 93.0%) for treatment, except for amoxicillin/levofloxacin (PPV<60%). Application of these algorithms yielded nearly 1.2 million veterans with testing and/or treatment between 1999 and 2018.

Conclusion: We assembled a validated national cohort of veterans who were tested or treated for infection. This cohort can be used for evaluating epidemiology and treatment patterns, as well as complications of chronic infection.