Predicting Barrett's esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

Sun X1, Elston RC2, Barnholtz-Sloan JS3, Falk GW4, Grady WM5, Faulx A6, Mittal SK7, Canto M8, Shaheen NJ9, Wang JS10, Iyer PG11, Abrams JA12, Tian YD13, Willis JE14, Guda K15, Markowitz SD16, Chandar A17, Warfe JM18, Brock W19, Chak A20.

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Abstract

BACKGROUND:

Barrett's esophagus (BE) is often asymptomatic and only a small portion of BE patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with BE. Familial aggregation of BE and esophageal adenocarcinoma (EAC), and the increased risk of EAC 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 BE pedigrees and 92 multiplex BE 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 BE pedigrees.

RESULTS:

Our results indicate family information helps to predict BE risk, and predicting in families improves both prediction calibration and discrimination accuracy.

CONCLUSIONS:

Our model can predict BE risk for anyone with family members known to have, or not have, had BE. 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 BE screening and surveillance, consequently allowing intervention at an early stage and reducing mortality from esophageal adenocarcinoma.