Abstract
OBJECTIVE AND BACKGROUND: The early detection of diabetic ketoacidosis (DKA) in patients with type 2 diabetes (T2D) plays a crucial role in enhancing outcomes. We developed a nomogram prediction model for screening DKA in T2D patients. At the same time, the input variables were adjusted to reduce misdiagnosis. METHODS: We obtained data on T2D patients from Mimic-IV V0.4 and Mimic-III V1.4 databases. A nomogram model was developed using the training data set, internally validated, subjected to sensitivity analysis, and further externally validated with data from T2D patients in Aviation General Hospital. RESULTS: Based on the established model, we analyzed 1885 type 2 diabetes patients, among whom 614 with DKA. We further additionally identified risk factors for DKA based on literature reports and multivariate analysis. We identified age, glucose, chloride, calcium, and urea nitrogen as predictors in our model. The logistic regression model demonstrated an area under the curve (AUC) of 0.86 (95%CI: 0.85-0.90]. To validate the model, we collected data from 91 T2D patients, including 15 with DKA, at our hospital. The external validation of the model yielded an AUC of 0.68 (95%CI: 0.67-0.70). The calibration plot confirmed that our model was adequate for predicting patients with DKA. The decision-curve analysis revealed that our model offered net benefits for clinical use. CONCLUSIONS: Our model offers a convenient and accurate tool for predicting whether DKA is present. Excluding input variables that may potentially hinder patient compliance increases the practical application significance of our model.