AUTHOR=Chen Ru , Zheng Rongshou , Zhou Jiachen , Li Minjuan , Shao Dantong , Li Xinqing , Wang Shengfeng , Wei Wenqiang
TITLE=Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review
JOURNAL=Frontiers in Public Health
VOLUME=9
YEAR=2021
URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2021.680967
DOI=10.3389/fpubh.2021.680967
ISSN=2296-2565
ABSTRACT=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.