Development and validation of a prediction rule for estimating gastric cancer risk in the Chinese high-risk population: a nationwide multicentre study

作者：Quan-cai Cai, Chunping Zhu, Yuan Yuan, Qi Feng, Yi-Chao Feng, Ying-Xia Hao, Jichang Li, Kaiguang Zhang, Guoliang Ye, Liping Ye, Nonghua Lv, Shengsheng Zhang, Chengxia Liu, Mingquan Li, Qi Liu, Rongzhou Li, Jie Pan, Xiaocui Yang, Xuqing Zhu, Yumei Li, Bo Lao, Ansheng Ling, Honghui Chen, Xiuling Li, Ping Xu, Jianfeng Zhou, Baozhen Liu, Zhi‐qiang Du, Yiqi Du, Zhaoshen Li · 发表于：Gut · 年份：2019 · DOI：10.1136/gutjnl-2018-317556 · 被引用次数：235 · 研究领域：Helicobacter pylori-related gastroenterology studies、Gastric Cancer Management and Outcomes、Colorectal Cancer Screening and Detection

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 performan...