Update and validation of a diagnostic model to identify prevalent malignant lesions in esophagus in general population. (May 2022)

• Record Type: Journal Article
• Authors: Liu, Mengfei; Zhou, Ren; Liu, Zhen; Guo, Chuanhai; Xu, Ruiping; Zhou, Fuyou; Liu, Anxiang; Yang, Haijun; Li, Fenglei; Duan, Liping; Shen, Lin; Wu, Qi; Zheng, Hongchen; Tian, Hongrui; Liu, Fangfang; Liu, Ying; Pan, Yaqi; Chen, Huanyu; Hu, Zhe; Cai, Hong; He, Zhonghu; Ke, Yang
• Is Part Of: EClinicalMedicine. Volume 47(2022)
• Publication Date: 2022-05
• Subjects: Precision screening -- Risk stratification -- Esophageal malignant lesions -- ESECC; Medicine -- Research -- Periodicals; Medical policy -- Periodicals; Clinical Medicine; Health Policy; Public Health; Medical policy; Medicine -- Research; Periodical; Electronic journals; Periodicals; 613
• DOI: 10.1016/j.eclinm.2022.101394
• Languages: English
• ISSNs: 2589-5370

Abstract

Summary: Background: Previous risk prediction models taking esophageal malignant lesions detected during endoscopy as the primary outcome are not always sufficient to identify prevalent cases which are "overlooked" at screening. We aimed to update and externally validate our previous risk prediction model for malignant esophageal lesions by redefining the predicted outcome. Methods: 15, 192 individuals from the Endoscopic Screening for Esophageal Cancer in China randomized controlled trial (ESECC trial, NCT01688908) were included as the training set, and 4576 participants from another population-based esophageal squamous cell carcinoma (ESCC) screening cohort (Anyang Esophageal Cancer Cohort Study, AECCS) served as the external validation set. Lesions with severe dysplasia or worse diagnosed at chromoendoscopy or identified via follow-up within 1 year after screening were defined as main outcome. Logistic regressions were applied to reconstruct the questionnaire-based prediction model using information collected before screening, with Akaike Information Criterion to determine the model structure. Findings: The final prediction model included age and its quadratic term, family history of ESCC, low body mass index (≤22 kg/m 2 ), use of coal or wood as main fuel for cooking, eating rapidly, and ingestion of leftover food. The area under the curve was 0·77 (95% CI: 0·73–0·80) and 0·71 (95% CI: 0·65–0·78) in the training and validation set. When screening the top 50% or 10% of high-risk individuals within population, the detection rates can be increased in both cohorts, as compared to universal screening. Interpretation: The described tool may promote the efficiency of current national screening programs for ESCC and contribute to a precision screening strategy in high-risk regions in China. Funding: This work was supported by the National Natural Science Foundation of China (82073626, 81773501), the National Science & Technology Fundamental Resources Investigation Program of China (2019FY101102), the National Key R&D Program of China (2021YFC2500405), the Beijing-Tianjin-Hebei Basic Research Cooperation Project (J200016), the Digestive Medical Coordinated Development Center of Beijing Hospitals Authority (XXZ0204) and the Beijing Nova Program (Z201100006820093). Sponsors had no role in the study design, data collection, analysis, and interpretation of data.