Department of The Infirmary, The Automation Engineering School of Beijing, Beijing, China.
Department of Endocrinology, Aviation General Hospital, China Medical University, Beijing, 100012, People's Republic of China.
BMC Endocr Disord. 2024 Aug 12;24(1):148. doi: 10.1186/s12902-024-01677-3.
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.
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.
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.
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.
早期发现 2 型糖尿病（T2D）患者的糖尿病酮症酸中毒（DKA）对于改善预后至关重要。我们开发了一种用于筛查 T2D 患者 DKA 的列线图预测模型。同时，调整输入变量以减少误诊。
我们从 Mimic-IV V0.4 和 Mimic-III V1.4 数据库中获得了 T2D 患者的数据。使用训练数据集开发了一个列线图模型，进行了内部验证、敏感性分析，并使用航空总医院 T2D 患者的数据进行了外部验证。
基于所建立的模型，我们分析了 1885 例 2 型糖尿病患者，其中 614 例患有 DKA。我们还根据文献报道和多变量分析进一步确定了 DKA 的危险因素。我们在模型中确定了年龄、血糖、氯、钙和尿素氮作为预测因子。逻辑回归模型显示曲线下面积（AUC）为 0.86（95%CI：0.85-0.90]。为了验证模型，我们从我院收集了 91 例 T2D 患者的数据，其中 15 例患有 DKA。模型的外部验证得到 AUC 为 0.68（95%CI：0.67-0.70）。校准图证实我们的模型足以预测 DKA 患者。决策曲线分析表明，我们的模型为临床应用提供了净收益。
我们的模型提供了一种方便、准确的工具，用于预测是否存在 DKA。排除可能会影响患者依从性的输入变量，增加了我们模型的实际应用意义。
