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A decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in Kawasaki disease
OBJECTIVES: This study aims to develop a new algorithm for predicting intravenous immunoglobulin (IVIG) resistance and coronary artery involvement in Kawasaki disease (KD) through decision tree models. METHODS: Medical records of children hospitalized for KD were analysed retrospectively. We compare...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354345/ https://www.ncbi.nlm.nih.gov/pubmed/35931986 http://dx.doi.org/10.1186/s12887-022-03533-6 |
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author | Joung, Jinwoon Oh, Jun Suk Yoon, Jung Min Ko, Kyung Ok Yoo, Gyeong Hee Cheon, Eun Jung |
author_facet | Joung, Jinwoon Oh, Jun Suk Yoon, Jung Min Ko, Kyung Ok Yoo, Gyeong Hee Cheon, Eun Jung |
author_sort | Joung, Jinwoon |
collection | PubMed |
description | OBJECTIVES: This study aims to develop a new algorithm for predicting intravenous immunoglobulin (IVIG) resistance and coronary artery involvement in Kawasaki disease (KD) through decision tree models. METHODS: Medical records of children hospitalized for KD were analysed retrospectively. We compared the clinical characteristics, and the laboratory data in the groups with IVIG resistance and coronary artery dilatations (CADs) in KD patients. The decision tree models were developed to predict IVIG resistance and CADs. RESULTS: A total 896 patients (511 males and 385 females; 1 month-12 years) were eligible. IVIG resistance was identified in 111 (12.3%) patients, and CADs were found in 156 (17.4%). Total bilirubin and nitrogen terminal- pro-brain natriuretic peptide (NT-proBNP) were significantly higher in IVIG resistant group than in IVIG responsive group (0.62 ± 0.8 mg/dL vs 1.38 ± 1.4 mg/dL and 1231 ± 2136 pg/mL vs 2425 ± 4459 mL, respectively, P < 0.01). Also, CADs were more developed in the resistant group (39/111; 14.9% vs. 117/785; 35.1%, P < 0.01). The decision tree for predicting IVIG resistance was classified based on total bilirubin (0.7 mg/mL, 1.46 mg/dL) and NT-proBNP (1561 pg/mL), consisting of two layers and four nodes, with 86.2% training accuracy and 90.5% evaluation accuracy. The Receiver Operating Characteristic (ROC) evaluated the predictive ability of the decision tree, and the area under the curve (AUC) (0.834; 95% confidence interval, 0.675–0.973; P < 0.05) showed relatively higher accuracy. The group with CADs had significantly higher total bilirubin and NT-proBNP levels than the control group (0.64 ± 0.82 mg/dL vs 1.04 ± 1.14 mg/dL and 1192 ± 2049 pg/mL vs 2268 ± 4136 pg/mL, respectively, P < 0.01). The decision trees for predicting CADs were classified into two nodes based on NT-proBNP (789 pg/mL) alone, with 83.5% training accuracy and 90.3% evaluation accuracy. CONCLUSION: A new algorithm decision tree model presents for predicting IVIG resistance and CADs in KD, confirming the usefulness of NT-proBNP as a predictor of KD. |
format | Online Article Text |
id | pubmed-9354345 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93543452022-08-06 A decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in Kawasaki disease Joung, Jinwoon Oh, Jun Suk Yoon, Jung Min Ko, Kyung Ok Yoo, Gyeong Hee Cheon, Eun Jung BMC Pediatr Research OBJECTIVES: This study aims to develop a new algorithm for predicting intravenous immunoglobulin (IVIG) resistance and coronary artery involvement in Kawasaki disease (KD) through decision tree models. METHODS: Medical records of children hospitalized for KD were analysed retrospectively. We compared the clinical characteristics, and the laboratory data in the groups with IVIG resistance and coronary artery dilatations (CADs) in KD patients. The decision tree models were developed to predict IVIG resistance and CADs. RESULTS: A total 896 patients (511 males and 385 females; 1 month-12 years) were eligible. IVIG resistance was identified in 111 (12.3%) patients, and CADs were found in 156 (17.4%). Total bilirubin and nitrogen terminal- pro-brain natriuretic peptide (NT-proBNP) were significantly higher in IVIG resistant group than in IVIG responsive group (0.62 ± 0.8 mg/dL vs 1.38 ± 1.4 mg/dL and 1231 ± 2136 pg/mL vs 2425 ± 4459 mL, respectively, P < 0.01). Also, CADs were more developed in the resistant group (39/111; 14.9% vs. 117/785; 35.1%, P < 0.01). The decision tree for predicting IVIG resistance was classified based on total bilirubin (0.7 mg/mL, 1.46 mg/dL) and NT-proBNP (1561 pg/mL), consisting of two layers and four nodes, with 86.2% training accuracy and 90.5% evaluation accuracy. The Receiver Operating Characteristic (ROC) evaluated the predictive ability of the decision tree, and the area under the curve (AUC) (0.834; 95% confidence interval, 0.675–0.973; P < 0.05) showed relatively higher accuracy. The group with CADs had significantly higher total bilirubin and NT-proBNP levels than the control group (0.64 ± 0.82 mg/dL vs 1.04 ± 1.14 mg/dL and 1192 ± 2049 pg/mL vs 2268 ± 4136 pg/mL, respectively, P < 0.01). The decision trees for predicting CADs were classified into two nodes based on NT-proBNP (789 pg/mL) alone, with 83.5% training accuracy and 90.3% evaluation accuracy. CONCLUSION: A new algorithm decision tree model presents for predicting IVIG resistance and CADs in KD, confirming the usefulness of NT-proBNP as a predictor of KD. BioMed Central 2022-08-05 /pmc/articles/PMC9354345/ /pubmed/35931986 http://dx.doi.org/10.1186/s12887-022-03533-6 Text en © The Author(s) 2022 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Joung, Jinwoon Oh, Jun Suk Yoon, Jung Min Ko, Kyung Ok Yoo, Gyeong Hee Cheon, Eun Jung A decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in Kawasaki disease |
title | A decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in Kawasaki disease |
title_full | A decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in Kawasaki disease |
title_fullStr | A decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in Kawasaki disease |
title_full_unstemmed | A decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in Kawasaki disease |
title_short | A decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in Kawasaki disease |
title_sort | decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in kawasaki disease |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354345/ https://www.ncbi.nlm.nih.gov/pubmed/35931986 http://dx.doi.org/10.1186/s12887-022-03533-6 |
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