Predictive factors and model validation of post-colon polyp surgery Helicobacter pylori infection

Abstract

Background: Recently, research has linked Helicobacter pylori (H. pylori) stomach infection to colonic inflammation, mediated by toxin production, potentially impacting colorectal cancer occurrence.

Aim: To investigate the risk factors for post-colon polyp surgery, H. pylori infection, and its correlation with pathologic type.

Methods: Eighty patients who underwent colon polypectomy in our hospital between January 2019 and January 2023 were retrospectively chosen. They were then randomly split into modeling (n = 56) and model validation (n = 24) sets using R. The modeling cohort was divided into an H. pylori-infected group (n = 37) and an H. pylori-uninfected group (n = 19). Binary logistic regression analysis was used to analyze the factors influencing the occurrence of H. pylori infection after colon polyp surgery. A roadmap prediction model was established and validated. Finally, the correlation between the different pathological types of colon polyps and the occurrence of H. pylori infection was analyzed after colon polyp surgery.

Results: Univariate results showed that age, body mass index (BMI), literacy, alcohol consumption, polyp pathology type, high-risk adenomas, and heavy diet were all influential factors in the development of H. pylori infection after intestinal polypectomy. Binary multifactorial logistic regression analysis showed that age, BMI, and type of polyp pathology were independent predictors of the occurrence of H. pylori infection after intestinal polypectomy. The area under the receiver operating characteristic curve was 0.969 [95% confidence interval (95%CI): 0.928-1.000] and 0.898 (95%CI: 0.773-1.000) in the modeling and validation sets, respectively. The slope of the calibration curve of the graph was close to 1, and the goodness-of-fit test was P > 0.05 in the two sets. The decision analysis curve showed a high rate of return in both sets. The results of the correlation analysis between different pathological types and the occurrence of H. pylori infection after colon polyp surgery showed that hyperplastic polyps, inflammatory polyps, and the occurrence of H. pylori infection were not significantly correlated. In contrast, adenomatous polyps showed a significant positive correlation with the occurrence of H. pylori infection.

Conclusion: Age, BMI, and polyps of the adenomatous type were independent predictors of H. pylori infection after intestinal polypectomy. Moreover, the further constructed column-line graph prediction model of H. pylori infection after intestinal polypectomy showed good predictive ability.

Keywords: Colon polyps; Columnar graphic modeling; Helicobacter pylori; Pathologic type; Risk factors.

Figure 1

Column-line diagram.

Figure 2

Calibration curves of the column graph prediction model.

Figure 3

Receiver operating characteristic curve of the column chart prediction model.

Figure 4

Decision analysis curve.

Figure 5

Verification queue receiver operating characteristic curve.

Figure 6

Verification queue calibration curve.

Figure 7

Verification queue decision analysis curve.