Risk factor analysis and nomogram for predicting gastroparesis in patients with type 2 diabetes mellitus.

ABSTRACT

Purpose: The incidence of gastroparesis is higher in individuals diagnosed with type 2 diabetes mellitus (T2DM) compared to the healthy individuals. Our study aimed to explore the risk factors for gastroparesis in T2DM and to establish a clinical prediction model (nomogram). Methods: Our study enlisted 694 patients with T2DM from two medical centers over a period of time. From January 2020 to December 2022, 347 and 149 patients were recruited from the Beilun branch of Zhejiang University's First Affiliated Hospital in the training and internal validation cohorts, respectively. The external validation cohort consisted of 198 patients who were enrolled at Nanchang University's First Affiliated Hospital from October 2020 to September 2021. We conducted univariate and multivariate logistic regression analyses to select the risk factors for gastroparesis in patients with T2DM; subsequently,we developed a nomogram model. The performance of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis(DCA). Results: Four clinical variables, including age, regular exercise, glycated hemoglobin level(HbA1c), and Helicobacter pylori (H. pylori) infection, were identified and included in the model. The model demonstrated excellent discrimination, with an AUC of 0.951 (95% CI = 0.925-0.978) in the training group, and 0.910 (95% CI = 0.859-0.961) and 0.875 (95% CI = 0.813-0.937) in the internal and external validation groups, respectively. The calibration curve showed good consistency between prediction of the model and observed gastroparesis. The DCA also demonstrated good clinical efficacy. Conclusion: The nomogram model developed in this study showed good performance in predicting gastroparesis in patients with T2DM.