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• W2997475288 abstract "Background and AimsPrediction models for esophageal squamous cell carcinoma are not common, and no model targeting a clinical population has previously been developed and validated. We aimed to develop a prediction model for estimating the risk of high-grade esophageal lesions for application in clinical settings and to validate the performance of this model in an external population.MethodsThe model was developed based on the results of endoscopic evaluation of 5624 outpatients in one hospital in a high-risk region in northern China and was validated using 5765 outpatients who had undergone endoscopy in another hospital in a non-high-risk region in southern China. Predictors were selected with unconditional logistic regression analysis. The Akaike information criterion was used to determine the final structure of the model. Discrimination was estimated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed using a calibration plot with an intercept and slope.ResultsThe final prediction model contained 5 variables, including age, smoking, body mass index, dysphagia, and retrosternal pain. This model generated an AUC of 0.871 (95% confidence interval, 0.842-0.946) in the development set, with an AUC of 0.862 after bootstrapping. The 5-variable model was superior to a single age model. In the validation population, the AUC was 0.843 (95% confidence interval, 0.793-0.894). This model successfully stratified the clinical population into 3 risk groups and showed high ability for identifying concentrated groups of cases.ConclusionsOur model for esophageal high-grade lesions has a high predictive value. It has the potential for application in clinical opportunistic screening to aid decision making for both health care professionals and individuals. Prediction models for esophageal squamous cell carcinoma are not common, and no model targeting a clinical population has previously been developed and validated. We aimed to develop a prediction model for estimating the risk of high-grade esophageal lesions for application in clinical settings and to validate the performance of this model in an external population. The model was developed based on the results of endoscopic evaluation of 5624 outpatients in one hospital in a high-risk region in northern China and was validated using 5765 outpatients who had undergone endoscopy in another hospital in a non-high-risk region in southern China. Predictors were selected with unconditional logistic regression analysis. The Akaike information criterion was used to determine the final structure of the model. Discrimination was estimated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed using a calibration plot with an intercept and slope. The final prediction model contained 5 variables, including age, smoking, body mass index, dysphagia, and retrosternal pain. This model generated an AUC of 0.871 (95% confidence interval, 0.842-0.946) in the development set, with an AUC of 0.862 after bootstrapping. The 5-variable model was superior to a single age model. In the validation population, the AUC was 0.843 (95% confidence interval, 0.793-0.894). This model successfully stratified the clinical population into 3 risk groups and showed high ability for identifying concentrated groups of cases. Our model for esophageal high-grade lesions has a high predictive value. It has the potential for application in clinical opportunistic screening to aid decision making for both health care professionals and individuals." @default.
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• W2997475288 date "2020-06-01" @default.
• W2997475288 modified "2023-10-18" @default.
• W2997475288 title "A clinical model predicting the risk of esophageal high-grade lesions in opportunistic screening: a multicenter real-world study in China" @default.
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• W2997475288 doi "https://doi.org/10.1016/j.gie.2019.12.038" @default.
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