Xiang Zhongyuan, Hu Jingyi, Bu Shengfang, Ding Jin, Chen Xi, Li Ziyang
Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Department of Metabolism and Endocrinology, Ministry of Education, The Second Xiangya Hospital of Central South University, Changsha, China.
Sci Rep. 2025 Jan 21;15(1):2633. doi: 10.1038/s41598-025-85357-9.
Patients with Diabetic ketoacidosis (DKA) have increased critical illness and mortality during coronavirus diseases 2019 (COVID-19). The aim of our study was to develop a predictive model for the occurrence of critical illness and mortality in COVID-19 patients with DKA utilizing machine learning. Blood samples and clinical data from 242 COVID-19 patients with DKA collected from December 2022 to January 2023 at Second Xiangya Hospital. Patients were categorized into non-death (n = 202) and death (n = 38) groups, and non-severe (n = 146) and severe (n = 96) groups. We developed five machine learning-based prediction models-Extreme Gradient Boosting (XGB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP)-to evaluate the prognosis of COVID-19 patients with DKA. We employed 5-fold cross-validation for model evaluation and used the Shapley Additive Explanations (SHAP) algorithm for result interpretation to ensure reliability. The LR model demonstrated the highest accuracy (AUC = 0.933) in predicting mortality. Additionally, the LR model excelled (AUC = 0.898) in predicting progression to severe disease. This study developed a machine learning-based predictive model for the progression to severe disease or death in COVID-19 patients with DKA, which can serve as a valuable tool to guide clinical treatment decisions.
糖尿病酮症酸中毒（DKA）患者在2019冠状病毒病（COVID-19）期间发生危重症和死亡的风险增加。我们研究的目的是利用机器学习为COVID-19合并DKA患者发生危重症和死亡开发一个预测模型。2022年12月至2023年1月在中南大学湘雅二医院收集了242例COVID-19合并DKA患者的血样和临床数据。患者被分为非死亡组（n = 202）和死亡组（n = 38），以及非重症组（n = 146）和重症组（n = 96）。我们开发了五个基于机器学习的预测模型——极端梯度提升（XGB）、逻辑回归（LR）、随机森林（RF）、支持向量机（SVM）和多层感知器（MLP）——来评估COVID-19合并DKA患者的预后。我们采用5折交叉验证进行模型评估，并使用夏普利值附加解释（SHAP）算法进行结果解释以确保可靠性。LR模型在预测死亡率方面表现出最高的准确性（AUC = 0.933）。此外，LR模型在预测进展为重症疾病方面也表现出色（AUC = 0.898）。本研究为COVID-19合并DKA患者进展为重症疾病或死亡开发了一个基于机器学习的预测模型，该模型可作为指导临床治疗决策的有价值工具。
