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Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study
The adverse drug reaction (ADR) of traditional Chinese medicine injection (TCMI) has become one of the major concerns of public health in China. There are significant advantages for developing methods to improve the use of TCMI in routine clinical practice. The method of predicting TCMI-induced ADR...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6853959/ https://www.ncbi.nlm.nih.gov/pubmed/31723184 http://dx.doi.org/10.1038/s41598-019-53267-2 |
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author | Jiang, Cheng Shen, Jie Shou, Dan Wang, Nani Jing, Jing Zhang, Guodi Gu, Jing Tian, Yunlong Sun, Caihua He, Jiaqi Ma, Jiaqi Wang, Xiaojun Li, Gonghua |
author_facet | Jiang, Cheng Shen, Jie Shou, Dan Wang, Nani Jing, Jing Zhang, Guodi Gu, Jing Tian, Yunlong Sun, Caihua He, Jiaqi Ma, Jiaqi Wang, Xiaojun Li, Gonghua |
author_sort | Jiang, Cheng |
collection | PubMed |
description | The adverse drug reaction (ADR) of traditional Chinese medicine injection (TCMI) has become one of the major concerns of public health in China. There are significant advantages for developing methods to improve the use of TCMI in routine clinical practice. The method of predicting TCMI-induced ADR was illustrated using a nested case-control study in 123 cases and 123 controls. The partial least squares regression (PLSR) models, which mapped the influence of basic characteristics and routine examinations to ADR, were established to predict the risk of ADR. The software was devised to provide an easy-to-use tool for clinic application. The effectiveness of the method was evaluated through its application to new patients with 95.7% accuracy of cases and 91.3% accuracy of controls. By using the method, the patients at high-risk could be conveniently, efficiently and economically recognized without any extra financial burden for additional examination. This study provides a novel insight into individualized management of the patients who will use TCMI. |
format | Online Article Text |
id | pubmed-6853959 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68539592019-11-19 Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study Jiang, Cheng Shen, Jie Shou, Dan Wang, Nani Jing, Jing Zhang, Guodi Gu, Jing Tian, Yunlong Sun, Caihua He, Jiaqi Ma, Jiaqi Wang, Xiaojun Li, Gonghua Sci Rep Article The adverse drug reaction (ADR) of traditional Chinese medicine injection (TCMI) has become one of the major concerns of public health in China. There are significant advantages for developing methods to improve the use of TCMI in routine clinical practice. The method of predicting TCMI-induced ADR was illustrated using a nested case-control study in 123 cases and 123 controls. The partial least squares regression (PLSR) models, which mapped the influence of basic characteristics and routine examinations to ADR, were established to predict the risk of ADR. The software was devised to provide an easy-to-use tool for clinic application. The effectiveness of the method was evaluated through its application to new patients with 95.7% accuracy of cases and 91.3% accuracy of controls. By using the method, the patients at high-risk could be conveniently, efficiently and economically recognized without any extra financial burden for additional examination. This study provides a novel insight into individualized management of the patients who will use TCMI. Nature Publishing Group UK 2019-11-13 /pmc/articles/PMC6853959/ /pubmed/31723184 http://dx.doi.org/10.1038/s41598-019-53267-2 Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Jiang, Cheng Shen, Jie Shou, Dan Wang, Nani Jing, Jing Zhang, Guodi Gu, Jing Tian, Yunlong Sun, Caihua He, Jiaqi Ma, Jiaqi Wang, Xiaojun Li, Gonghua Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study |
title | Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study |
title_full | Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study |
title_fullStr | Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study |
title_full_unstemmed | Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study |
title_short | Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study |
title_sort | identification of high-risk patients for adr induced by traditional chinese medicine injection: a nested case-control study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6853959/ https://www.ncbi.nlm.nih.gov/pubmed/31723184 http://dx.doi.org/10.1038/s41598-019-53267-2 |
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