A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease
Summary: Accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial for the effective treatment of Kawasaki disease(KD). This study aimed to develop a predictive model for IVIG resistance in patients with Kawasaki disease and to identify the key predictors. The training set under...
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Elsevier
2025-03-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225002640 |
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| author | Yuhan Xia Yuezhong Huang Min Gong Weirong Liu Yuanhui Meng Huiyang Wu Hui Zhang Hao Zhang Luyi Weng Xiao-Li Chen Huixian Qiu Xing Rong Rongzhou Wu Maoping Chu Xiu-Feng Huang |
| author_facet | Yuhan Xia Yuezhong Huang Min Gong Weirong Liu Yuanhui Meng Huiyang Wu Hui Zhang Hao Zhang Luyi Weng Xiao-Li Chen Huixian Qiu Xing Rong Rongzhou Wu Maoping Chu Xiu-Feng Huang |
| author_sort | Yuhan Xia |
| collection | DOAJ |
| description | Summary: Accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial for the effective treatment of Kawasaki disease(KD). This study aimed to develop a predictive model for IVIG resistance in patients with Kawasaki disease and to identify the key predictors. The training set underwent cross-validation, and models were constructed using six machine learning algorithms. Model performance was validated through cross-validation, test set evaluation, and two external validation sets evaluation. The model constructed using the random forest algorithm demonstrated the best overall performance among six models. The areas under the receiver operating characteristic curve (AUCs) for 5-fold cross-validation, internal validation, and external validations from Shaoxing and Quzhou were 0.711, 0.751, 0.827, and 0.735, respectively. According to the Shapley additive explanation (SHAP) method, C-reactive protein-to-albumin ratio, prognostic nutritional index, and sex were identified as the most important predictors. Our model demonstrates strong predictive capability for assessing IVIG resistance in Kawasaki disease patients. |
| format | Article |
| id | doaj-art-d3563adb28bb423ca9f64865da8a4f34 |
| institution | OA Journals |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-d3563adb28bb423ca9f64865da8a4f342025-08-20T02:11:11ZengElsevieriScience2589-00422025-03-0128311200410.1016/j.isci.2025.112004A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki diseaseYuhan Xia0Yuezhong Huang1Min Gong2Weirong Liu3Yuanhui Meng4Huiyang Wu5Hui Zhang6Hao Zhang7Luyi Weng8Xiao-Li Chen9Huixian Qiu10Xing Rong11Rongzhou Wu12Maoping Chu13Xiu-Feng Huang14Zhejiang Provincial Clinical Research Center for Pediatric Precision Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China; Pediatrics Discipline Group, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China; Children’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, ChinaZhejiang Provincial Clinical Research Center for Pediatric Precision Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China; Pediatrics Discipline Group, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, ChinaDepartment of Pediatrics, People’s Hospital of Quzhou, Quzhou, Zhejiang, ChinaDepartment of Pediatrics, People’s Hospital of Shaoxing, Shaoxing, ChinaChildren’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, ChinaChildren’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, ChinaChildren’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, ChinaChildren’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, ChinaChildren’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, ChinaZhejiang Provincial Clinical Research Center for Pediatric Precision Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China; Pediatrics Discipline Group, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, ChinaChildren’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, ChinaChildren’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, ChinaChildren’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, ChinaZhejiang Provincial Clinical Research Center for Pediatric Precision Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China; Pediatrics Discipline Group, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China; Children’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China; Corresponding authorZhejiang Provincial Clinical Research Center for Pediatric Precision Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China; Pediatrics Discipline Group, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China; Corresponding authorSummary: Accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial for the effective treatment of Kawasaki disease(KD). This study aimed to develop a predictive model for IVIG resistance in patients with Kawasaki disease and to identify the key predictors. The training set underwent cross-validation, and models were constructed using six machine learning algorithms. Model performance was validated through cross-validation, test set evaluation, and two external validation sets evaluation. The model constructed using the random forest algorithm demonstrated the best overall performance among six models. The areas under the receiver operating characteristic curve (AUCs) for 5-fold cross-validation, internal validation, and external validations from Shaoxing and Quzhou were 0.711, 0.751, 0.827, and 0.735, respectively. According to the Shapley additive explanation (SHAP) method, C-reactive protein-to-albumin ratio, prognostic nutritional index, and sex were identified as the most important predictors. Our model demonstrates strong predictive capability for assessing IVIG resistance in Kawasaki disease patients.http://www.sciencedirect.com/science/article/pii/S2589004225002640Cardiovascular medicineMachine learning |
| spellingShingle | Yuhan Xia Yuezhong Huang Min Gong Weirong Liu Yuanhui Meng Huiyang Wu Hui Zhang Hao Zhang Luyi Weng Xiao-Li Chen Huixian Qiu Xing Rong Rongzhou Wu Maoping Chu Xiu-Feng Huang A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease iScience Cardiovascular medicine Machine learning |
| title | A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease |
| title_full | A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease |
| title_fullStr | A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease |
| title_full_unstemmed | A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease |
| title_short | A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease |
| title_sort | machine learning based model to predict intravenous immunoglobulin resistance in kawasaki disease |
| topic | Cardiovascular medicine Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225002640 |
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