A Nomogram Combining Platelet to Lymphocyte Ratio (PLR) and Systemic Inflammatory Response Index (SIRI) to Predict Patients with H. pylori -Positive Gastric Dysplasia.

Objective: Patients with Helicobacter pylori-positive gastric dysplasia are at a higher risk of progressing to gastric cancer (GC). Systemic inflammatory markers have been identified as predictors of poor prognosis in patients with GC. However, their role in gastric dysplasia remains elusive. The aim of this study is to evaluate the utility of systemic inflammatory markers as predictors in patients with H. pylori-positive gastric dysplasia. Methods: This study included a total of 664 normal individuals and patients diagnosed with chronic gastritis from the Yangzhou screening cohort, as well as 135 patients with gastric dysplasia from the Affiliated Hospital of Yangzhou University, spanning the period from January 2017 to May 2024. The participants were randomly assigned to either a training group or a validation group in a ratio of 7:3. SPSS software was utilized to determine the optimal critical values for PLR and SIRI. Subsequently, univariate and multivariate regression analyses were conducted to assess the diagnostic utility of PLR and SIRI. A nomogram was developed to estimate the risk associated with gastric dysplasia. To evaluate model performance, receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA) and the Clinical Impact Curve (CIC) were generated. Results: Eight independent risk factors, including the PLR and SIRI, were identified with a significance level of p < 0.1. The area under the ROC curve (AUC) was found to be 0.859 for the training set (95% CI: 0.808-0.891) and 0.821 for the validation set (95% CI: 0.735-0.906). The results from ROC analysis calibration curves, DCA and CIC demonstrated that the nomogram possessed significant predictive value. Conclusion: PLR and SIRI are independent variables that influence the diagnosis of patients with gastric dysplasia. The nomogram model created using PLR and SIRI demonstrates a high predictive value for diagnosing individuals with gastric dysplasia.