Predicting barrett's esophagus in families: An esophagus translational research network (BETRNet) model fitting clinical data to a familial paradigm

Xiangqing Sun, Robert C. Elston, Jill S. Barnholtz-Sloan, Gary W. Falk, William M. Grady, Ashley Faulx, Sumeet K. Mittal, Marcia Canto, Nicholas J. Shaheen, Jean S. Wang, Prasad G. Iyer, Julian A. Abrams, Ye D. Tian, Joseph E. Willis, Kishore Guda, Sanford D. Markowitz, Apoorva Chandar, James M. Warfe, Wendy Brock, Amitabh Chak

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Background: Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. Methods: We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. Results: Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. Conclusions: Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. Impact: Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma.

Original languageEnglish
Pages (from-to)727-735
Number of pages9
JournalCancer Epidemiology Biomarkers and Prevention
Issue number5
StatePublished - May 2016


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