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A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease
INTRODUCTION: In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions. OBJECTIVE: To establish a simple scoring model predicting IVIG resistance in KD patients based on the machine learning model...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832252/ https://www.ncbi.nlm.nih.gov/pubmed/36627530 http://dx.doi.org/10.1007/s10067-023-06502-1 |
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author | Sunaga, Yuto Watanabe, Atsushi Katsumata, Nobuyuki Toda, Takako Yoshizawa, Masashi Kono, Yosuke Hasebe, Yohei Koizumi, Keiichi Hoshiai, Minako Kawakami, Eiryo Inukai, Takeshi |
author_facet | Sunaga, Yuto Watanabe, Atsushi Katsumata, Nobuyuki Toda, Takako Yoshizawa, Masashi Kono, Yosuke Hasebe, Yohei Koizumi, Keiichi Hoshiai, Minako Kawakami, Eiryo Inukai, Takeshi |
author_sort | Sunaga, Yuto |
collection | PubMed |
description | INTRODUCTION: In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions. OBJECTIVE: To establish a simple scoring model predicting IVIG resistance in KD patients based on the machine learning model. METHODS: A retrospective cohort study of 1002 KD patients diagnosed at 12 facilities for 10 years, in which 22.7% were resistant to initial IVIG treatment. We performed machine learning with diverse models using 30 clinical variables at diagnosis in 801 and 201 cases for training and test datasets, respectively. SHAP was applied to identify the variables that influenced the prediction model. A scoring model was designed using the influential clinical variables based on the Shapley additive explanation results. RESULTS: Light gradient boosting machine model accurately predicted IVIG resistance (area under the receiver operating characteristic curve (AUC), 0.78; sensitivity, 0.50; specificity, 0.88). Next, using top three influential features (days of illness at initial therapy, serum levels of C-reactive protein, and total cholesterol), we designed a simple scoring system. In spite of its simplicity, it predicted IVIG resistance (AUC, 0.72; sensitivity, 0.49; specificity, 0.82) as accurately as machine learning models. Moreover, accuracy of our scoring system with three clinical features was almost identical to that of Gunma score with seven clinical features (AUC, 0.73; sensitivity, 0.53; specificity, 0.83), a well-known logistic regression scoring model. CONCLUSION: A simple scoring system based on the findings in machine learning seems to be a useful tool to accurately predict IVIG resistance in KD patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10067-023-06502-1. |
format | Online Article Text |
id | pubmed-9832252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-98322522023-01-11 A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease Sunaga, Yuto Watanabe, Atsushi Katsumata, Nobuyuki Toda, Takako Yoshizawa, Masashi Kono, Yosuke Hasebe, Yohei Koizumi, Keiichi Hoshiai, Minako Kawakami, Eiryo Inukai, Takeshi Clin Rheumatol Original Article INTRODUCTION: In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions. OBJECTIVE: To establish a simple scoring model predicting IVIG resistance in KD patients based on the machine learning model. METHODS: A retrospective cohort study of 1002 KD patients diagnosed at 12 facilities for 10 years, in which 22.7% were resistant to initial IVIG treatment. We performed machine learning with diverse models using 30 clinical variables at diagnosis in 801 and 201 cases for training and test datasets, respectively. SHAP was applied to identify the variables that influenced the prediction model. A scoring model was designed using the influential clinical variables based on the Shapley additive explanation results. RESULTS: Light gradient boosting machine model accurately predicted IVIG resistance (area under the receiver operating characteristic curve (AUC), 0.78; sensitivity, 0.50; specificity, 0.88). Next, using top three influential features (days of illness at initial therapy, serum levels of C-reactive protein, and total cholesterol), we designed a simple scoring system. In spite of its simplicity, it predicted IVIG resistance (AUC, 0.72; sensitivity, 0.49; specificity, 0.82) as accurately as machine learning models. Moreover, accuracy of our scoring system with three clinical features was almost identical to that of Gunma score with seven clinical features (AUC, 0.73; sensitivity, 0.53; specificity, 0.83), a well-known logistic regression scoring model. CONCLUSION: A simple scoring system based on the findings in machine learning seems to be a useful tool to accurately predict IVIG resistance in KD patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10067-023-06502-1. Springer International Publishing 2023-01-11 2023 /pmc/articles/PMC9832252/ /pubmed/36627530 http://dx.doi.org/10.1007/s10067-023-06502-1 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Sunaga, Yuto Watanabe, Atsushi Katsumata, Nobuyuki Toda, Takako Yoshizawa, Masashi Kono, Yosuke Hasebe, Yohei Koizumi, Keiichi Hoshiai, Minako Kawakami, Eiryo Inukai, Takeshi A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease |
title | A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease |
title_full | A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease |
title_fullStr | A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease |
title_full_unstemmed | A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease |
title_short | A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease |
title_sort | simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in kawasaki disease |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832252/ https://www.ncbi.nlm.nih.gov/pubmed/36627530 http://dx.doi.org/10.1007/s10067-023-06502-1 |
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