Dataset Information

Clinical prediction model for tumor progression in Barrett's esophagus.

ABSTRACT: BACKGROUND:Individuals with Barrett's esophagus (BE) are at increased risk of high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC), but the cost-effectiveness of general surveillance of BE is low. This study aimed to identify a risk prediction model for tumor progression in individuals with BE based on age, sex, and risk factors found at upper endoscopy, enabling tailored surveillance. METHODS:This nested case-control study originated from a cohort of 8171 adults diagnosed with BE in 2006-2013 in the Swedish Patient Registry. Cases had EAC/HGD (n = 279) as identified from the Swedish Cancer Registry, whereas controls had no EAC/HGD (n = 1089). Findings from endoscopy and histopathology reports were extracted from medical records at 71 Swedish hospitals and from the Swedish Patient Registry. Multivariable logistic regression provided odds ratios (OR) with 95% confidence intervals (CIs). RESULTS:Older age (OR 1.02 [95% CI 1.01-1.03] per year), male sex (OR 2.8 [95% CI 1.9-4.1]), and increasing maximum BE length (OR 2.3 [95% CI 1.4-3.9] for segments 3-8 cm and OR 4.3 [95% CI 2.5-7.2] for segments ≥ 8 cm) increased the risk of EAC/HGD, while the circumferential extent of the BE, hiatal hernia or reflux esophagitis did not. A model based on age, sex, and maximum BE length predicted 71% of all EAC/HGD cases. CONCLUSIONS:A simple combination of the variables age, sex and maximum BE length showed fairly good accuracy for predicting tumor progression in BE. This clinical risk prediction model may help to tailor future surveillance programs.

SUBMITTER: Holmberg D

PROVIDER: S-EPMC6684532 | biostudies-other | 2019 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Clinical prediction model for tumor progression in Barrett's esophagus.

Holmberg Dag D Ness-Jensen Eivind E Mattsson Fredrik F Lagergren Jesper J

Surgical endoscopy 20181119 9

<h4>Background</h4>Individuals with Barrett's esophagus (BE) are at increased risk of high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC), but the cost-effectiveness of general surveillance of BE is low. This study aimed to identify a risk prediction model for tumor progression in individuals with BE based on age, sex, and risk factors found at upper endoscopy, enabling tailored surveillance.<h4>Methods</h4>This nested case-control study originated from a cohort of 8171 adults diagnos ...[more]

PMID: 30456503

Similar Datasets

Project description:BackgroundBarrett'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.MethodsWe 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.ResultsOur results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy.ConclusionsOur 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.ImpactOur 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. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR.

Dataset Information

Clinical prediction model for tumor progression in Barrett's esophagus.

Publications

Clinical prediction model for tumor progression in Barrett's esophagus.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets