Prognostic nomogram for uncontrolled type 2 diabetes using Thailand nation-wide cross-sectional studies.

Background: Uncontrolled type 2 diabetes (T2DM) and limited hemoglobin A1c (HbA1c) levels examination are a burden in community hospitals in Thailand. The nomogram from the patients' information might be a practical solution to identify a high-risk group of diabetic complications. Thus, this study aimed to establish an effective prognostic nomogram for patients with uncontrolled T2DM.

Methods: Sequential nationwide cross-sectional studies of T2DM patients in 2018 and 2015 were utilized for development and validation groups, respectively, with this chronological order aiming to capture recent trends during development and assess the nomogram's robustness across diverse timeframes. The predictive outcome was uncontrolled T2DM, defined as HbA1c ≥9%. The model was determined by multivariable regression analysis and established an effective prognostic nomogram. The receiver operating characteristic curve, Hosmer-Lemeshow goodness of fit test, and decision curve analysis (DCA) was applied to evaluate the performance of the nomogram.

Results: In 2018, 24% of the 38,568 participants in the development group had uncontrolled T2DM (defined as Hba1c ≥9%). The predictive nomogram of uncontrolled diabetes consisted of demographic characteristics, prescription medications, history of diabetic complications, and laboratory results (C-statistic of 0.77). The goodness of fit test and DCA showed good agreement between the result and clinical application for T2DM.

Conclusion: The predictive nomogram demonstrates simplicity, accuracy, and valuable prediction to enhance diabetic care in resource-limited countries, including Thailand.