Use of the Electronic Health Record to Target Patients for Non-endoscopic Barrett’s Esophagus Screening

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 firsttime 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.

Risk Prediction of Barrett’s Esophagus in a Taiwanese Health Examination Center Based on Regression Models

“…Age, sex, GERD symptoms, and smoking were significantly associated with BE and employed to construct models. In the case of different ethnicity with different pattern of BE, our LR model had comparable performance with those of previously published models [ 11 , 15 , 16 , 17 , 18 , 19 ]. To our knowledge, only one published model used ANNs.…”

“…However, these guidelines do not provide quantitative data to stratify the risk of BE while combining multiple risk factors. A number of risk prediction models for other diseases are widely employed to help clinicians make individualized medical decisions for their patients [ 11 , 12 , 13 , 14 ]; however, most of the published BE prediction models were constructed for Western populations and have yet to be verified for Asian populations [ 11 , 15 , 16 , 17 , 18 , 19 , 20 ], a relevant issue given that BE presents different patterns for Asian and Western populations [ 21 ].…”

Systematic review with meta‐analysis: the effects of family history on the risk of Barrett's oesophagus and oesophageal adenocarcinoma

“…Eighteen studies met our inclusion and exclusion criteria, 32‐49 of which two were excluded because of overlapping patient populations 46,47 . Another two studies were excluded because the family history of gastric and oesophageal cancer was combined and no individual data could be extracted 48,49 . The sensitivity analysis on full‐text search of 1050 studies on BO and OAC did not reveal additional relevant articles on family history fulfilling the eligibility criteria (Figure S1).…”

“…Of these, 2928 were excluded after screening titles and abstracts, leaving 102 articles for full‐text assessment (Figure 1 ). Eighteen studies met our inclusion and exclusion criteria, 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 of which two were excluded because of overlapping patient populations. 46 , 47 Another two studies were excluded because the family history of gastric and oesophageal cancer was combined and no individual data could be extracted.…”

Screening for Barrett’s Oesophagus: Are We Ready for it?

“…Statistical risk prediction models using widely available and easy to obtain symptoms and risk factors have been developed to estimate the absolute risk that an individual has BO or would develop OAC [ 27 ]. For example, a BO prediction model based on electronic health records (EHR) data including gastroesophageal reflux disease, sex, body mass index and ever-smoker status was shown to identify BO patients with a modest accuracy reporting an AUC of 0.71 (95% CI 0.64–0.77) [ 28 ]. With risk prediction tools, there is an added potential opportunity for self-assessment whereby a patient can produce a personalised risk profile for BO using a web-based application.…”