Cai, Quancai, Zhu, Chunping, Yuan, Yuan, Feng, Qi, Feng, Yichao, Hao, Yingxia, Li, Jichang, Zhang, Kaiguang, Ye, Guoliang, Ye, Liping, Lv, Nonghua, Zhang, Shengsheng, Liu, Chengxia, Li, Mingquan, Liu, Qi, Li, Rongzhou, Pan, Jie, Yang, Xiaocui, Zhu, Xuqing, Li, Yumei, Lao, Bo, Ling, Ansheng, Chen, Honghui, Li, Xiuling, Xu, Ping, Zhou, Jianfeng, Liu, Baozhen, Du, Zhiqiang, Du, Yiqi, and Li, Zhaoshen
ObjectiveTo develop a gastric cancer (GC) risk prediction rule as an initial prescreening tool to identify individuals with a high risk prior to gastroscopy.DesignThis was a nationwide multicentre cross-sectional study. Individuals aged 40–80 years who went to hospitals for a GC screening gastroscopy were recruited. Serum pepsinogen (PG) I, PG II, gastrin-17 (G-17) and anti-Helicobacter pyloriIgG antibody concentrations were tested prior to endoscopy. Eligible participants (n=14 929) were randomly assigned into the derivation and validation cohorts, with a ratio of 2:1. Risk factors for GC were identified by univariate and multivariate analyses and an optimal prediction rule was then settled.ResultsThe novel GC risk prediction rule comprised seven variables (age, sex, PG I/II ratio, G-17 level, H. pyloriinfection, pickled food and fried food), with scores ranging from 0 to 25. The observed prevalence rates of GC in the derivation cohort at low-risk (≤11), medium-risk (12–16) or high-risk (17–25) group were 1.2%, 4.4% and 12.3%, respectively (p<0.001).When gastroscopy was used for individuals with medium risk and high risk, 70.8% of total GC cases and 70.3% of early GC cases were detected. While endoscopy requirements could be reduced by 66.7% according to the low-risk proportion. The prediction rule owns a good discrimination, with an area under curve of 0.76, or calibration (p<0.001).ConclusionsThe developed and validated prediction rule showed good performance on identifying individuals at a higher risk in a Chinese high-risk population. Future studies are needed to validate its efficacy in a larger population.