Abstract
BACKGROUND: Clinical prediction models targeting patients for Barrett's esophagus (BE) screening include data obtained by interview, questionnaire, and body measurements. A tool based on electronic health records (EHR) data could reduce cost and enhance usability, particularly if combined with non-endoscopic BE screening methods. AIMS: To determine whether EHR-based data can identify BE patients. METHODS: We performed a retrospective review of patients ages 50-75 who underwent a first-time esophagogastroduodenoscopy. Data extracted from the EHR included demographics and BE risk factors. Endoscopy and pathology reports were reviewed for histologically confirmed BE. Screening criteria modified from clinical guidelines were assessed for association with BE. Subsequently, a score based on multivariate logistic regression was developed and assessed for its ability to identify BE subjects. RESULTS: A total of 2931 patients were assessed, and BE was found in 1.9%. Subjects who met screening criteria were more likely to have BE (3.3% vs. 1.1%, p = 0.001), and the criteria predicted BE with an AUROC of 0.65 (95% CI 0.59-0.71). A score based on logistic regression modeling included gastroesophageal reflux disease, sex, body mass index, and ever-smoker status and identified BE subjects with an AUROC of 0.71 (95% CI 0.64-0.77). Both prediction tools produced higher AUROCs in women than in men. CONCLUSIONS: EHR-based BE risk prediction tools identify BE patients with fair accuracy. While these tools may improve the efficiency of patient targeting for BE screening in the primary care setting, challenges remain to identify high-risk patients for non-invasive BE screening in clinical practice.