Details

Autor(en) / Beteiligte

• 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
• Li, Zhaoshen

Titel

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

Ist Teil von

• Gut, 2019-09, Vol.68 (9), p.1576-1587

Ort / Verlag

England: BMJ Publishing Group LTD

Erscheinungsjahr

2019

Quelle

MEDLINE

Beschreibungen/Notizen

• 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 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.ResultsThe 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).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.