Machine learning based prediction models for the prognosis of COVID-19 patients with DKA
Abstract 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 le...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85357-9 |
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| author | Zhongyuan Xiang Jingyi Hu Shengfang Bu Jin Ding Xi Chen Ziyang Li |
| author_facet | Zhongyuan Xiang Jingyi Hu Shengfang Bu Jin Ding Xi Chen Ziyang Li |
| author_sort | Zhongyuan Xiang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-d32a72397676440fa6001e5c980cdca6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-d32a72397676440fa6001e5c980cdca62025-01-26T12:31:55ZengNature PortfolioScientific Reports2045-23222025-01-011511810.1038/s41598-025-85357-9Machine learning based prediction models for the prognosis of COVID-19 patients with DKAZhongyuan Xiang0Jingyi Hu1Shengfang Bu2Jin Ding3Xi Chen4Ziyang Li5Department of Laboratory Medicine, The Second Xiangya Hospital, Central South UniversityDepartment of Laboratory Medicine, The Second Xiangya Hospital, Central South UniversityDepartment of Laboratory Medicine, The Second Xiangya Hospital, Central South UniversityNational 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 UniversityInformation Network Center of Xiangya Second Hospital, Central South UniversityDepartment of Laboratory Medicine, The Second Xiangya Hospital, Central South UniversityAbstract 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.https://doi.org/10.1038/s41598-025-85357-9COVID-19DiabetesKetoacidosisPredictionMachine learning |
| spellingShingle | Zhongyuan Xiang Jingyi Hu Shengfang Bu Jin Ding Xi Chen Ziyang Li Machine learning based prediction models for the prognosis of COVID-19 patients with DKA Scientific Reports COVID-19 Diabetes Ketoacidosis Prediction Machine learning |
| title | Machine learning based prediction models for the prognosis of COVID-19 patients with DKA |
| title_full | Machine learning based prediction models for the prognosis of COVID-19 patients with DKA |
| title_fullStr | Machine learning based prediction models for the prognosis of COVID-19 patients with DKA |
| title_full_unstemmed | Machine learning based prediction models for the prognosis of COVID-19 patients with DKA |
| title_short | Machine learning based prediction models for the prognosis of COVID-19 patients with DKA |
| title_sort | machine learning based prediction models for the prognosis of covid 19 patients with dka |
| topic | COVID-19 Diabetes Ketoacidosis Prediction Machine learning |
| url | https://doi.org/10.1038/s41598-025-85357-9 |
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