RT Journal Article
SR Electronic
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
FD BMJ Publishing Group Ltd and British Society of Gastroenterology
SP 1576
OP 1587
DO 10.1136/gutjnl-2018-317556
VO 68
IS 9
A1 Quancai Cai
A1 Chunping Zhu
A1 Yuan Yuan
A1 Qi Feng
A1 Yichao Feng
A1 Yingxia Hao
A1 Jichang Li
A1 Kaiguang Zhang
A1 Guoliang Ye
A1 Liping Ye
A1 Nonghua Lv
A1 Shengsheng Zhang
A1 Chengxia Liu
A1 Mingquan Li
A1 Qi Liu
A1 Rongzhou Li
A1 Jie Pan
A1 Xiaocui Yang
A1 Xuqing Zhu
A1 Yumei Li
A1 Bo Lao
A1 Ansheng Ling
A1 Honghui Chen
A1 Xiuling Li
A1 Ping Xu
A1 Jianfeng Zhou
A1 Baozhen Liu
A1 Zhiqiang Du
A1 Yiqi Du
A1 Zhaoshen Li
A1 ,
YR 2019
UL http://gut.bmj.com/content/68/9/1576.abstract
AB 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.