Abstract
BACKGROUND & AIMS: Risk prediction models for Barrett's esophagus (BE) have been developed using multiple demographic and clinical variables, but their predictive performance has been modest. Adding a multibiomarker risk score may improve discriminatory ability. METHODS: We used data from 141 patients with definitive BE and 138 controls participating in a case-control study at the Michael E. DeBakey Veterans Affairs Medical Center (Houston, TX) (97% men, 65% of controls were white, and 89% of cases were white). We derived and compared 3 prediction models. Model 1 included only gastroesophageal reflux disease (GERD) frequency and duration; model 2 included GERD frequency and duration, age, sex, race, waist-to-hip ratio, and Helicobacter pylori status; and model 3 included the variables in model 2 as well as a multibiomarker risk score based on serum levels of interleukin (IL)12p70, IL6, IL8, IL10, and leptin. We assessed their predictive accuracy in terms of discrimination using the area under the receiver operating characteristic curve and calibration analyses. RESULTS: The multibiomarker risk score was associated significantly with risk for BE. Compared with persons with a score of 0, persons with a score of 3 or higher had a greater than 10-fold increased risk for BE (biomarker risk score, ≥3; odds ratio, 11.9; 95% confidence interval, 4.06-34.9; P trend < .001). Risk prediction using the multibiomarker score in conjunction with demographic and clinical features improved discrimination compared with using only GERD frequency and duration (area under the receiver operating characteristic curve, 0.85 vs 0.74; P = .01). CONCLUSIONS: Based on data from a case-control study of predominantly white male veterans, a risk prediction model including a multibiomarker score, derived from serum levels of cytokines and leptin, as well as GERD frequency and duration, age, sex, race, waist-to-hip ratio, and H pylori infection, can identify persons in this population with BE more accurately than previous methods.