Literature DB >> 29559360

Model for Identifying Individuals at Risk for Esophageal Adenocarcinoma.

Andrew T Kunzmann1, Aaron P Thrift2, Chris R Cardwell3, Jesper Lagergren4, Shaohua Xie5, Brian T Johnston6, Lesley A Anderson3, John Busby7, Úna C McMenamin3, Andrew D Spence3, Helen G Coleman8.   

Abstract

BACKGROUND & AIMS: The prognosis for most patients with esophageal adenocarcinoma (EAC) is poor because they present with advanced disease. Models developed to identify patients at risk for EAC and increase early detection have been developed based on data from case-control studies. We analyzed data from a prospective study to identify factors available to clinicians that identify individuals with a high absolute risk of EAC.
METHODS: We collected data from 355,034 individuals (all older than 50 years) without a prior history of cancer enrolled in the UK Biobank prospective cohort study from 2006 through 2010; clinical data were collected through September 2014. We identified demographic, lifestyle, and medical factors, measured at baseline, that associated with development of EAC within 5 years using logistic regression analysis. We used these data to create a model to identify individuals at risk for EAC. Model performance was assessed using area under the receiver operating characteristics curve (AUROC), sensitivity, and specificity analyses.
RESULTS: Within up to 5 years of follow up, 220 individuals developed EAC. Age, sex, smoking, body mass index, and history of esophageal conditions or treatments identified individuals who developed EAC (AUROC, 0.80; 95% CI, 0.77-0.82). We used these factors to develop a scoring system and identified a point cut off that 104,723 individuals (29.5%), including 170 of the 220 cases with EAC, were above. The scoring system identified individuals who developed EAC with 77.4% sensitivity and 70.5% specificity. The 5-year risk of EAC was 0.16% for individuals with scores above the threshold and 0.02% for individuals with scores below the threshold.
CONCLUSION: We combined data on several well-established risk factors that are available to clinicians to develop a system to identify individuals with a higher absolute risk of EAC within 5 years. Studies are needed to evaluate the utility of these factors in a multi-stage, triaged, screening program.
Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  BMI; Esophagus; Risk-Prediction; Upper Gastrointestinal Cancer

Mesh:

Year:  2018        PMID: 29559360     DOI: 10.1016/j.cgh.2018.03.014

Source DB:  PubMed          Journal:  Clin Gastroenterol Hepatol        ISSN: 1542-3565            Impact factor:   11.382


  9 in total

1.  Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach.

Authors:  Avi Rosenfeld; David G Graham; Sarah Jevons; Jose Ariza; Daryl Hagan; Ash Wilson; Samuel J Lovat; Sarmed S Sami; Omer F Ahmad; Marco Novelli; Manuel Rodriguez Justo; Alison Winstanley; Eliyahu M Heifetz; Mordehy Ben-Zecharia; Uria Noiman; Rebecca C Fitzgerald; Peter Sasieni; Laurence B Lovat
Journal:  Lancet Digit Health       Date:  2019-12-05

Review 2.  Global burden and epidemiology of Barrett oesophagus and oesophageal cancer.

Authors:  Aaron P Thrift
Journal:  Nat Rev Gastroenterol Hepatol       Date:  2021-02-18       Impact factor: 46.802

3.  Validation and Comparison of Tools for Selecting Individuals to Screen for Barrett's Esophagus and Early Neoplasia.

Authors:  Joel H Rubenstein; Daniel McConnell; Akbar K Waljee; Valbona Metko; Kimberly Nofz; Maryam Khodadost; Li Jiang; Trivellore Raghunathan
Journal:  Gastroenterology       Date:  2020-02-29       Impact factor: 22.682

4.  Patients With Adenocarcinoma of the Esophagus or Esophagogastric Junction Frequently Have Potential Screening Opportunities.

Authors:  Joel H Rubenstein; Richard R Evans; Jennifer A Burns; Maria E Arasim; Ji Zhu; Akbar K Waljee
Journal:  Gastroenterology       Date:  2021-12-20       Impact factor: 22.682

5.  Determinants of participation and detection rate of upper gastrointestinal cancer from population-based screening program in China.

Authors:  Lanwei Guo; Shaokai Zhang; Shuzheng Liu; Liyang Zheng; Qiong Chen; Xiaoqin Cao; Xibin Sun; Youlin Qiao; Jiangong Zhang
Journal:  Cancer Med       Date:  2019-09-27       Impact factor: 4.452

6.  Outcomes of upper gastrointestinal cancer screening in high-risk individuals: a population-based prospective study in Northeast China.

Authors:  Zhifu Yu; Tingting Zuo; Huihui Yu; Ying Zhao; Yong Zhang; Jinghua Liu; Shoulan Dong; Ying Wu; Yunyong Liu
Journal:  BMJ Open       Date:  2022-02-15       Impact factor: 2.692

7.  Development and Validation of an Esophageal Squamous Cell Carcinoma Risk Prediction Model for Rural Chinese: Multicenter Cohort Study.

Authors:  Junming Han; Lijie Wang; Huan Zhang; Siqi Ma; Yan Li; Zhongli Wang; Gaopei Zhu; Deli Zhao; Jialin Wang; Fuzhong Xue
Journal:  Front Oncol       Date:  2021-08-30       Impact factor: 6.244

8.  Risk prediction models for esophageal cancer: A systematic review and critical appraisal.

Authors:  He Li; Dianqin Sun; Maomao Cao; Siyi He; Yadi Zheng; Xinyang Yu; Zheng Wu; Lin Lei; Ji Peng; Jiang Li; Ni Li; Wanqing Chen
Journal:  Cancer Med       Date:  2021-08-20       Impact factor: 4.452

9.  Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review.

Authors:  Ru Chen; Rongshou Zheng; Jiachen Zhou; Minjuan Li; Dantong Shao; Xinqing Li; Shengfeng Wang; Wenqiang Wei
Journal:  Front Public Health       Date:  2021-12-01
  9 in total

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