Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease
Abstract Background Kawasaki disease (KD) is a leading cause of acquired heart disease in children that is treated with intravenous immunoglobulin (IVIG). However, 10–20% of cases exhibit IVIG resistance, which increases the risk of coronary complications. Existing predictive models do not integrate...
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| Format: | Article |
| Language: | English |
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BMC
2025-06-01
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| Series: | Italian Journal of Pediatrics |
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| Online Access: | https://doi.org/10.1186/s13052-025-02036-1 |
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| author | Ying He Fan Lin Xin Zheng Qiaobin Chen Meng Xiao Xiaoting Lin Hongbiao Huang |
| author_facet | Ying He Fan Lin Xin Zheng Qiaobin Chen Meng Xiao Xiaoting Lin Hongbiao Huang |
| author_sort | Ying He |
| collection | DOAJ |
| description | Abstract Background Kawasaki disease (KD) is a leading cause of acquired heart disease in children that is treated with intravenous immunoglobulin (IVIG). However, 10–20% of cases exhibit IVIG resistance, which increases the risk of coronary complications. Existing predictive models do not integrate multiple machine learning (ML) algorithms or facilitate real-time clinical use. This study presents a region-specific, interpretable ML model for early IVIG resistance prediction in KD. Methods A retrospective cohort of 463 children diagnosed with KD at Fuzhou University Affiliated Provincial Hospital (2012–2024) was analyzed. Thirteen ML algorithms were evaluated via cross-validation, with performance assessed by AUC and other metrics. Feature importance was determined using SHapley Additive exPlanations (SHAP), and risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool. Results The random forest (RF) model demonstrated the highest predictive performance (AUC = 0.78). After feature selection based on SHAP values, a final interpretable RF model incorporating 10 key features was developed, and a web-based tool integrating the Youden index (16.9%) was deployed for real-time risk estimation. Conclusion This region-specific, interpretable ML model ( https://milailai.shinyapps.io/data1/ ) is a practical tool for early risk stratification and personalized treatment of IVIG resistance in KD. |
| format | Article |
| id | doaj-art-5ccf0497dc114593aeeff074c8deeb45 |
| institution | DOAJ |
| issn | 1824-7288 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Italian Journal of Pediatrics |
| spelling | doaj-art-5ccf0497dc114593aeeff074c8deeb452025-08-20T02:39:44ZengBMCItalian Journal of Pediatrics1824-72882025-06-0151111710.1186/s13052-025-02036-1Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki diseaseYing He0Fan Lin1Xin Zheng2Qiaobin Chen3Meng Xiao4Xiaoting Lin5Hongbiao Huang6Department of Pediatric, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalDepartment of Pediatric, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalDepartment of Pediatric, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalDepartment of Pediatric, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalDepartment of Pediatric, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalDepartment of Pediatric, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalDepartment of Pediatric, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalAbstract Background Kawasaki disease (KD) is a leading cause of acquired heart disease in children that is treated with intravenous immunoglobulin (IVIG). However, 10–20% of cases exhibit IVIG resistance, which increases the risk of coronary complications. Existing predictive models do not integrate multiple machine learning (ML) algorithms or facilitate real-time clinical use. This study presents a region-specific, interpretable ML model for early IVIG resistance prediction in KD. Methods A retrospective cohort of 463 children diagnosed with KD at Fuzhou University Affiliated Provincial Hospital (2012–2024) was analyzed. Thirteen ML algorithms were evaluated via cross-validation, with performance assessed by AUC and other metrics. Feature importance was determined using SHapley Additive exPlanations (SHAP), and risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool. Results The random forest (RF) model demonstrated the highest predictive performance (AUC = 0.78). After feature selection based on SHAP values, a final interpretable RF model incorporating 10 key features was developed, and a web-based tool integrating the Youden index (16.9%) was deployed for real-time risk estimation. Conclusion This region-specific, interpretable ML model ( https://milailai.shinyapps.io/data1/ ) is a practical tool for early risk stratification and personalized treatment of IVIG resistance in KD.https://doi.org/10.1186/s13052-025-02036-1Kawasaki diseaseImmunoglobulinsIntravenousPrognosisMachine learningInternet-based intervention |
| spellingShingle | Ying He Fan Lin Xin Zheng Qiaobin Chen Meng Xiao Xiaoting Lin Hongbiao Huang Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease Italian Journal of Pediatrics Kawasaki disease Immunoglobulins Intravenous Prognosis Machine learning Internet-based intervention |
| title | Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease |
| title_full | Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease |
| title_fullStr | Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease |
| title_full_unstemmed | Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease |
| title_short | Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease |
| title_sort | interpretable web based machine learning model for predicting intravenous immunoglobulin resistance in kawasaki disease |
| topic | Kawasaki disease Immunoglobulins Intravenous Prognosis Machine learning Internet-based intervention |
| url | https://doi.org/10.1186/s13052-025-02036-1 |
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