Abstract
Objective To investigate and study the related risk factors of gastric cancer (GC) patients, to establish a high-risk scoring model of GC by multiple logistic regression analysis, and to explore the establishment of a GC screening mode with clinical opportunistic screening as the main method, and by using the pattern of opportunistic screening to establish the screening of high-risk GC patients and the choice of screening methods in the clinical outpatient work. Methods Collected the epidemiological questionnaire of 99 GC cases and 284 non-GC patients (other chronic gastric diseases and normal) diagnosed by the General Hospital of Ningxia Medical University from October 2017 to March 2019. Serum pepsinogen (PG) levels were measured by enzyme-linked immunosorbent assay (ELISA) and confirmed Helicobacter pylori (Hp) infection in gastric mucosa tissues by Giemsa staining. Determined the high-risk factors and established a scoring model through unconditional logistic regression model analysis, and the ROC curve determined the cut-off value. Then, we followed up 26 patients of nongastric cancer patients constituted a validation group, which validated the model. Results The high-risk factors of GC included age ≥ 55, male, drinking cellar or well water, family history of GC, Hp infection, PGI ≤ 43.6 μg/L, and PGI/PGII ≤ 2.1. Established the high-risk model: Y = A × age + 30 × gender + 30 × drinking water + 30 × Hp infection + 50 × family history of GC + B × PG level. The ROC curve determined that the cut-off value for high-risk GC population was ≥155, and the area under the curve (AUC) was 0.875, the sensitivity and specificity were 87.9% and 71.5%. Conclusions According to the risk factors of GC, using statistical methods can establish a high-risk scoring model of GC, and the score ≥ 155 is divided into the screening cut-off value for high-risk GC population. Using this model for clinical outpatient GC screening is cost-effective and has high sensitivity and specificity.
Highlights
Gastric cancer (GC) remains one of the most common neoplasms in the world [1]
Univariate analysis performed on various factors in the epidemiological questionnaire and multiple logistic regression analysis was used to determine the statistically meaningful risk factors, and the regression coefficient β of each independent variable was obtained
For the convenience of analysis, all variables were set as categorical variables; for some continuous variables such as age and PG, according to the research data, we set corresponding cut-off values, which were converted into categorical variables, and multiple logistic regression analysis was performed, such as PGI ≤ 43:6 μg/L and PGR ≤ 2:1
Summary
To investigate and study the related risk factors of gastric cancer (GC) patients, to establish a high-risk scoring model of GC by multiple logistic regression analysis, and to explore the establishment of a GC screening mode with clinical opportunistic screening as the main method, and by using the pattern of opportunistic screening to establish the screening of high-risk GC patients and the choice of screening methods in the clinical outpatient work. Determined the high-risk factors and established a scoring model through unconditional logistic regression model analysis, and the ROC curve determined the cut-off value. Established the high-risk model: Y = A × age + 30 × gender + 30 × drinking water + 30 × Hp infection + 50 × family history of GC + B × PG level. According to the risk factors of GC, using statistical methods can establish a high-risk scoring model of GC, and the score ≥ 155 is divided into the screening cut-off value for high-risk GC population. Using this model for clinical outpatient GC screening is cost-effective and has high sensitivity and specificity
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