Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study
Abstract Kawasaki disease (KD) is a leading cause of acquired heart disease in children, often resulting in coronary artery complications such as dilation, aneurysms, and stenosis. While intravenous immunoglobulin (IVIG) is effective in reducing immunologic inflammation, 10–15% of patients do not re...
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2025-01-01
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| author | Eun Jung Cheon Gi Beom Kim Seung Park |
| author_facet | Eun Jung Cheon Gi Beom Kim Seung Park |
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| description | Abstract Kawasaki disease (KD) is a leading cause of acquired heart disease in children, often resulting in coronary artery complications such as dilation, aneurysms, and stenosis. While intravenous immunoglobulin (IVIG) is effective in reducing immunologic inflammation, 10–15% of patients do not respond to initial therapy, and some show resistance even after two consecutive treatments. Predicting which patients will not respond to these two IVIG treatments is crucial for guiding treatment strategies and improving outcomes. This study aimed to forecast resistance to two consecutive IVIG treatments using advanced machine learning models based on clinical and laboratory data. Data from the 9th National Kawasaki Disease Patient Survey by the Korean Kawasaki Disease Society encompassing 15,378 patients (mean age 33.0 ± 24.8 months; sex ratio 1.4:1) were used. Clinical and laboratory findings included white blood cell count, absolute neutrophil count (ANC), platelet count, erythrocyte sedimentation rate, serum protein, aspartate aminotransferase, alanine aminotransferase, total bilirubin, N-terminal pro-brain natriuretic peptide, and presence of pyuria. Machine learning models, including Logistic Regression (LR), Multi-Layer Perceptron (MLP), Random Forest (RF), CATBoost, Explainable Boosting Machine (EBM), and Gradient Boosting Machine (GBM), were applied to predict treatment resistance. The machine learning models achieved Area Under the Receiver Operating Characteristic Curve (AUROC) values between 0.664 and 0.791, with the GBM model exhibiting the highest AUROC of 0.791. Analysis of feature importance revealed that ANC, serum protein, platelet count, and C-reactive protein (CRP) levels were the most significant predictors of treatment resistance. The cutoff values for these predictors were 7,860/mm³ for ANC, 7.0 g/dL for serum protein, 519,000/mm³ for platelet count, and 10.4 mg/dL for CRP. Among the patients, 12.2% were refractory to the first IVIG infusion, and 2.8% did not respond to the second IVIG treatment. Additionally, 13.1% of these patients had confirmed coronary artery dilatation (CAD) in the acute phase, and 4.7% developed CAD after the acute phase. Machine learning models effectively predict resistance to consecutive IVIG treatments, allowing for early identification of high-risk patients. Key predictors include ANC, serum protein, platelet count, and CRP levels. These findings can guide personalized treatment strategies and improve outcomes for Kawasaki Disease. |
| format | Article |
| id | doaj-art-0a6c4d75fca54c09949b677df73cc6f1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-0a6c4d75fca54c09949b677df73cc6f12025-01-12T12:17:11ZengNature PortfolioScientific Reports2045-23222025-01-011511910.1038/s41598-025-85394-4Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide studyEun Jung Cheon0Gi Beom Kim1Seung Park2Chungbuk National University HospitalSeoul National University Children’s HospitalChungbuk National University HospitalAbstract Kawasaki disease (KD) is a leading cause of acquired heart disease in children, often resulting in coronary artery complications such as dilation, aneurysms, and stenosis. While intravenous immunoglobulin (IVIG) is effective in reducing immunologic inflammation, 10–15% of patients do not respond to initial therapy, and some show resistance even after two consecutive treatments. Predicting which patients will not respond to these two IVIG treatments is crucial for guiding treatment strategies and improving outcomes. This study aimed to forecast resistance to two consecutive IVIG treatments using advanced machine learning models based on clinical and laboratory data. Data from the 9th National Kawasaki Disease Patient Survey by the Korean Kawasaki Disease Society encompassing 15,378 patients (mean age 33.0 ± 24.8 months; sex ratio 1.4:1) were used. Clinical and laboratory findings included white blood cell count, absolute neutrophil count (ANC), platelet count, erythrocyte sedimentation rate, serum protein, aspartate aminotransferase, alanine aminotransferase, total bilirubin, N-terminal pro-brain natriuretic peptide, and presence of pyuria. Machine learning models, including Logistic Regression (LR), Multi-Layer Perceptron (MLP), Random Forest (RF), CATBoost, Explainable Boosting Machine (EBM), and Gradient Boosting Machine (GBM), were applied to predict treatment resistance. The machine learning models achieved Area Under the Receiver Operating Characteristic Curve (AUROC) values between 0.664 and 0.791, with the GBM model exhibiting the highest AUROC of 0.791. Analysis of feature importance revealed that ANC, serum protein, platelet count, and C-reactive protein (CRP) levels were the most significant predictors of treatment resistance. The cutoff values for these predictors were 7,860/mm³ for ANC, 7.0 g/dL for serum protein, 519,000/mm³ for platelet count, and 10.4 mg/dL for CRP. Among the patients, 12.2% were refractory to the first IVIG infusion, and 2.8% did not respond to the second IVIG treatment. Additionally, 13.1% of these patients had confirmed coronary artery dilatation (CAD) in the acute phase, and 4.7% developed CAD after the acute phase. Machine learning models effectively predict resistance to consecutive IVIG treatments, allowing for early identification of high-risk patients. Key predictors include ANC, serum protein, platelet count, and CRP levels. These findings can guide personalized treatment strategies and improve outcomes for Kawasaki Disease.https://doi.org/10.1038/s41598-025-85394-4Kawasaki diseaseTreatment resistanceMachine learningImmunoglobulinsIntravenousCoronary artery disease |
| spellingShingle | Eun Jung Cheon Gi Beom Kim Seung Park Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study Scientific Reports Kawasaki disease Treatment resistance Machine learning Immunoglobulins Intravenous Coronary artery disease |
| title | Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study |
| title_full | Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study |
| title_fullStr | Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study |
| title_full_unstemmed | Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study |
| title_short | Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study |
| title_sort | predictive modeling of consecutive intravenous immunoglobulin treatment resistance in kawasaki disease a nationwide study |
| topic | Kawasaki disease Treatment resistance Machine learning Immunoglobulins Intravenous Coronary artery disease |
| url | https://doi.org/10.1038/s41598-025-85394-4 |
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