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

Background and aims Esophageal cancer risk prediction models allow for risk-stratified endoscopic screening. We aimed to assess the quality of these models developed in the general population. Methods A systematic search of the PubMed and Embase databases from January 2000 through May 2021 was performed. Studies that developed or validated a model of esophageal cancer in the general population were included. Screening, data extraction, and risk of bias (ROB) assessment by the Prediction model Risk Of Bias Assessment Tool (PROBAST) were performed independently by two reviewers. Results Of the 13 models included in the qualitative analysis, 8 were developed for esophageal squamous cell carcinoma (ESCC) and the other 5 were developed for esophageal adenocarcinoma (EAC). Only two models conducted external validation. In the ESCC models, cigarette smoking was included in each model, followed by age, sex, and alcohol consumption. For EAC models, cigarette smoking and body mass index were included in each model, and gastroesophageal reflux disease, uses of acid-suppressant medicine, and nonsteroidal anti-inflammatory drug were exclusively included. The discriminative performance was reported in all studies, with C statistics ranging from 0.71 to 0.88, whereas only six models reported calibration. For ROB, all the models had a low risk in participant and outcome, but all models showed high risk in analysis, and 60% of models showed a high risk in predictors, which resulted in all models being classified as having overall high ROB. For model applicability, about 60% of these models had an overall low risk, with 30% of models of high risk and 10% of models of unclear risk, concerning the assessment of participants, predictors, and outcomes. Conclusions Most current risk prediction models of esophageal cancer have a high ROB. Prediction models need further improvement in their quality and applicability to benefit esophageal cancer screening.