Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost
ObjectiveThe purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU).MethodsPatients diagnosed with DKA from the Medical Inf...
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| Format: | Article |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1087297/full |
| _version_ | 1827972046333673472 |
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| author | Tingting Fan Jiaxin Wang Luyao Li Jing Kang Wenrui Wang Chuan Zhang |
| author_facet | Tingting Fan Jiaxin Wang Luyao Li Jing Kang Wenrui Wang Chuan Zhang |
| author_sort | Tingting Fan |
| collection | DOAJ |
| description | ObjectiveThe purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU).MethodsPatients diagnosed with DKA from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database according to the International Classification of Diseases (ICD)-9/10 code were included. The patient’s medical history is extracted, along with data on their demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures. The best-performing model is chosen by contrasting the 8 Ml models. The area under the receiver operating characteristic curve (AUC), sensitivity, accuracy, and specificity were calculated to select the best-performing ML model.ResultsThe final study enrolled 1,322 patients with DKA in total, randomly split into training (1,124, 85%) and validation sets (198, 15%). 497 (37.5%) of them experienced AKI within a week of being admitted to the ICU. The eXtreme Gradient Boosting (XGBoost) model performed best of the 8 Ml models, and the AUC of the training and validation sets were 0.835 and 0.800, respectively. According to the result of feature importance, the top 5 main features contributing to the XGBoost model were blood urea nitrogen (BUN), urine output, weight, age, and platelet count (PLT).ConclusionAn ML-based individual prediction model for DKA-associated AKI (DKA-AKI) was developed and validated. The model performs robustly, identifies high-risk patients early, can assist in clinical decision-making, and can improve the prognosis of DKA patients to some extent. |
| first_indexed | 2024-04-09T19:16:59Z |
| format | Article |
| id | doaj.art-91f0e299392d4a85b9652aafaab88474 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-09T19:16:59Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-91f0e299392d4a85b9652aafaab884742023-04-06T04:46:14ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-04-011110.3389/fpubh.2023.10872971087297Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoostTingting Fan0Jiaxin Wang1Luyao Li2Jing Kang3Wenrui Wang4Chuan Zhang5Department of Endocrinology, Second Affiliated Hospital of Jilin University, Changchun, ChinaDepartment of Endocrinology, Second Affiliated Hospital of Jilin University, Changchun, ChinaDepartment of Endocrinology, Second Affiliated Hospital of Jilin University, Changchun, ChinaDepartment of Endocrinology, Second Affiliated Hospital of Jilin University, Changchun, ChinaDigestive Diseases Center, Department of Hepatopancreatobiliary Medicine, Second Affiliated Hospital of Jilin University, Changchun, ChinaDepartment of Endocrinology, Second Affiliated Hospital of Jilin University, Changchun, ChinaObjectiveThe purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU).MethodsPatients diagnosed with DKA from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database according to the International Classification of Diseases (ICD)-9/10 code were included. The patient’s medical history is extracted, along with data on their demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures. The best-performing model is chosen by contrasting the 8 Ml models. The area under the receiver operating characteristic curve (AUC), sensitivity, accuracy, and specificity were calculated to select the best-performing ML model.ResultsThe final study enrolled 1,322 patients with DKA in total, randomly split into training (1,124, 85%) and validation sets (198, 15%). 497 (37.5%) of them experienced AKI within a week of being admitted to the ICU. The eXtreme Gradient Boosting (XGBoost) model performed best of the 8 Ml models, and the AUC of the training and validation sets were 0.835 and 0.800, respectively. According to the result of feature importance, the top 5 main features contributing to the XGBoost model were blood urea nitrogen (BUN), urine output, weight, age, and platelet count (PLT).ConclusionAn ML-based individual prediction model for DKA-associated AKI (DKA-AKI) was developed and validated. The model performs robustly, identifies high-risk patients early, can assist in clinical decision-making, and can improve the prognosis of DKA patients to some extent.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1087297/fulldiabetic ketosisacute kidney injurymachine learningXGBoostoutcome |
| spellingShingle | Tingting Fan Jiaxin Wang Luyao Li Jing Kang Wenrui Wang Chuan Zhang Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost Frontiers in Public Health diabetic ketosis acute kidney injury machine learning XGBoost outcome |
| title | Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost |
| title_full | Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost |
| title_fullStr | Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost |
| title_full_unstemmed | Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost |
| title_short | Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost |
| title_sort | predicting the risk factors of diabetic ketoacidosis associated acute kidney injury a machine learning approach using xgboost |
| topic | diabetic ketosis acute kidney injury machine learning XGBoost outcome |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1087297/full |
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