Zhen Liu1, Chuanhai Guo1, Yujie He2, Yun Chen3, Ping Ji4, Zhengyu Fang4, Fenglei Li5, Yuefei Tang2, Xiujian Chen6, Ping Xiao4, Chengwen Wang7, Weihua Yin8, Hai Guo7, Mengfei Liu1, Yaqi Pan1, Fangfang Liu1, Ying Liu1, Zhonghu He1, Yang Ke1. 1. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, P.R. China. 2. Endoscopy Center, Hua County People's Hospital, Hua County, Henan Province, P.R. China. 3. Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, Guangdong Province, P.R. China; Shenzhen Key Laboratory for Drug Addiction and Medication Safety, Shenzhen Peking University-Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong Province, P.R. China. 4. Clinical Research Institute, Shenzhen Peking University-Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong Province, P.R. China. 5. Hua County People's Hospital, Hua County, Henan Province, P.R. China. 6. Department of Pathology, Hua County People's Hospital, Hua County, Henan Province, P.R. China. 7. Endoscope Group, Department of Gastroenterology, Peking University Shenzhen Hospital, Shenzhen, Guangdong Province, P.R. China. 8. Department of Pathology, Peking University Shenzhen Hospital, Shenzhen, Guangdong Province, P.R. China.
Abstract
BACKGROUND AND AIMS: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND AND AIMS: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.