Establish a Scoring Model for High-Risk Population of Gastric Cancer and Study on the Pattern of Opportunistic Screening

الملخص EN

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.