Gut · 2019 · 223 citations · 32 references
MedicineOncologyPreventive MedicineCancer ScreeningEarly DetectionRisk AssessmentCancer RiskGastrointestinal AssessmentNutritional Risk FactorsRisk Factor EpidemiologyRisk PredictionRisk ScoringCancer Risk Prediction ModelsClinical Risk Prediction ToolsGastric Cancer PrevalenceGastric Cancer ScreeningHigh-risk Population PrevalenceMultivariable Risk Prediction ModelsPredictionRisk Score Validation
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.
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