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

Area of Science:

• Endocrinology
• Gastroenterology
• Clinical Prediction Modeling

Background:

• Gastroparesis is more prevalent in type 2 diabetes mellitus (T2DM) patients than in the general population.
• Identifying T2DM patients at risk for gastroparesis is crucial for timely intervention and management.

Purpose of the Study:

• To investigate the risk factors associated with gastroparesis in patients with T2DM.
• To develop and validate a clinical prediction model (nomogram) for gastroparesis in T2DM.

Main Methods:

• A cohort of 694 T2DM patients was recruited from two medical centers.
• Logistic regression analysis was used to identify risk factors, followed by nomogram construction.
• Model performance was assessed using AUC, calibration curves, and decision curve analysis (DCA).

Main Results:

• Age, regular exercise, glycated hemoglobin (HbA1c), and Helicobacter pylori infection were identified as significant risk factors.
• The nomogram demonstrated excellent predictive performance with AUCs of 0.951 (training), 0.910 (internal validation), and 0.875 (external validation).
• Calibration and DCA confirmed the model's good predictive accuracy and clinical utility.

Conclusions:

• A validated nomogram model effectively predicts gastroparesis risk in T2DM patients.
• The model incorporates accessible clinical variables for practical application in patient care.
• This tool can aid clinicians in early identification and management of gastroparesis in T2DM.