Development and validation of a multivariable risk prediction model for identifying ketosis-prone type 2 diabetes

Abstract

Background: To develop and validate a multivariable risk prediction model for ketosis-prone type 2 diabetes mellitus (T2DM) based on clinical characteristics. Methods: A total of 964 participants newly diagnosed with T2DM were enrolled in the modeling and validation cohort. Baseline clinical data were collected and analyzed. Multivariable logistic regression analysis was performed to select independent risk factors, develop the prediction model, and construct the nomogram. The model's reliability and validity were checked using the receiver operating characteristic curve and the calibration curve. Results: A high morbidity of ketosis-prone T2DM was observed (20.2%), who presented as lower age and fasting C-peptide, and higher free fatty acids, glycated hemoglobin A1c and urinary protein. Based on these five independent influence factors, we developed a risk prediction model for ketosis-prone T2DM and constructed the nomogram. Areas under the curve of the modeling and validation cohorts were 0.806 (95% confidence interval [CI]: 0.760–0.851) and 0.856 (95% CI: 0.803–0.908). The calibration curves that were both internally and externally checked indicated that the projected results were reasonably close to the actual values. Conclusions: Our study provided an effective clinical risk prediction model for ketosis-prone T2DM, which could help for precise classification and management.