TY - JOUR T1 - Development and validation of a prediction rule for estimating gastric cancer risk in the Chinese high-risk population: a nationwide multicentre study JF - Gut JO - Gut SP - 1576 LP - 1587 DO - 10.1136/gutjnl-2018-317556 VL - 68 IS - 9 AU - Quancai Cai AU - Chunping Zhu AU - Yuan Yuan AU - Qi Feng AU - Yichao Feng AU - Yingxia Hao AU - Jichang Li AU - Kaiguang Zhang AU - Guoliang Ye AU - Liping Ye AU - Nonghua Lv AU - Shengsheng Zhang AU - Chengxia Liu AU - Mingquan Li AU - Qi Liu AU - Rongzhou Li AU - Jie Pan AU - Xiaocui Yang AU - Xuqing Zhu AU - Yumei Li AU - Bo Lao AU - Ansheng Ling AU - Honghui Chen AU - Xiuling Li AU - Ping Xu AU - Jianfeng Zhou AU - Baozhen Liu AU - Zhiqiang Du AU - Yiqi Du AU - Zhaoshen Li A2 - , Y1 - 2019/09/01 UR - http://gut.bmj.com/content/68/9/1576.abstract N2 - Objective To develop a gastric cancer (GC) risk prediction rule as an initial prescreening tool to identify individuals with a high risk prior to gastroscopy.Design This 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 pylori IgG 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.Results The novel GC risk prediction rule comprised seven variables (age, sex, PG I/II ratio, G-17 level, H. pylori infection, 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).Conclusions The 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. ER -