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

Objective: The risk prediction model is an effective tool for risk stratification and is expected to play an important role in the early detection and prevention of esophageal cancer. This study sought to summarize the available evidence of esophageal cancer risk predictions models and provide refer...

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Autores principales:	Ru Chen, Rongshou Zheng, Jiachen Zhou, Minjuan Li, Dantong Shao, Xinqing Li, Shengfeng Wang, Wenqiang Wei
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	methodology risk of bias systematic review esophageal cancer risk prediction model Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/2512dc6ea7a846b08dc9e896c2a49747
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spelling	oai:doaj.org-article:2512dc6ea7a846b08dc9e896c2a497472021-12-01T21:41:19ZRisk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review2296-256510.3389/fpubh.2021.680967https://doaj.org/article/2512dc6ea7a846b08dc9e896c2a497472021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpubh.2021.680967/fullhttps://doaj.org/toc/2296-2565Objective: The risk prediction model is an effective tool for risk stratification and is expected to play an important role in the early detection and prevention of esophageal cancer. This study sought to summarize the available evidence of esophageal cancer risk predictions models and provide references for their development, validation, and application.Methods: We searched PubMed, EMBASE, and Cochrane Library databases for original articles published in English up to October 22, 2021. Studies that developed or validated a risk prediction model of esophageal cancer and its precancerous lesions were included. Two reviewers independently extracted study characteristics including predictors, model performance and methodology, and assessed risk of bias and applicability with PROBAST (Prediction model Risk Of Bias Assessment Tool).Results: A total of 20 studies including 30 original models were identified. The median area under the receiver operating characteristic curve of risk prediction models was 0.78, ranging from 0.68 to 0.94. Age, smoking, body mass index, sex, upper gastrointestinal symptoms, and family history were the most commonly included predictors. None of the models were assessed as low risk of bias based on PROBST. The major methodological deficiencies were inappropriate date sources, inconsistent definition of predictors and outcomes, and the insufficient number of participants with the outcome.Conclusions: This study systematically reviewed available evidence on risk prediction models for esophageal cancer in general populations. The findings indicate a high risk of bias due to several methodological pitfalls in model development and validation, which limit their application in practice.Ru ChenRongshou ZhengJiachen ZhouMinjuan LiDantong ShaoXinqing LiShengfeng WangWenqiang WeiFrontiers Media S.A.articlemethodologyrisk of biassystematic reviewesophageal cancerrisk prediction modelPublic aspects of medicineRA1-1270ENFrontiers in Public Health, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	methodology risk of bias systematic review esophageal cancer risk prediction model Public aspects of medicine RA1-1270
spellingShingle	methodology risk of bias systematic review esophageal cancer risk prediction model Public aspects of medicine RA1-1270 Ru Chen Rongshou Zheng Jiachen Zhou Minjuan Li Dantong Shao Xinqing Li Shengfeng Wang Wenqiang Wei Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review
description	Objective: The risk prediction model is an effective tool for risk stratification and is expected to play an important role in the early detection and prevention of esophageal cancer. This study sought to summarize the available evidence of esophageal cancer risk predictions models and provide references for their development, validation, and application.Methods: We searched PubMed, EMBASE, and Cochrane Library databases for original articles published in English up to October 22, 2021. Studies that developed or validated a risk prediction model of esophageal cancer and its precancerous lesions were included. Two reviewers independently extracted study characteristics including predictors, model performance and methodology, and assessed risk of bias and applicability with PROBAST (Prediction model Risk Of Bias Assessment Tool).Results: A total of 20 studies including 30 original models were identified. The median area under the receiver operating characteristic curve of risk prediction models was 0.78, ranging from 0.68 to 0.94. Age, smoking, body mass index, sex, upper gastrointestinal symptoms, and family history were the most commonly included predictors. None of the models were assessed as low risk of bias based on PROBST. The major methodological deficiencies were inappropriate date sources, inconsistent definition of predictors and outcomes, and the insufficient number of participants with the outcome.Conclusions: This study systematically reviewed available evidence on risk prediction models for esophageal cancer in general populations. The findings indicate a high risk of bias due to several methodological pitfalls in model development and validation, which limit their application in practice.
format	article
author	Ru Chen Rongshou Zheng Jiachen Zhou Minjuan Li Dantong Shao Xinqing Li Shengfeng Wang Wenqiang Wei
author_facet	Ru Chen Rongshou Zheng Jiachen Zhou Minjuan Li Dantong Shao Xinqing Li Shengfeng Wang Wenqiang Wei
author_sort	Ru Chen
title	Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review
title_short	Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review
title_full	Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review
title_fullStr	Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review
title_full_unstemmed	Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review
title_sort	risk prediction model for esophageal cancer among general population: a systematic review
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/2512dc6ea7a846b08dc9e896c2a49747
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Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review

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