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Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom
BACKGROUND: The early prediction of significant coronary artery lesion, including coronary vasospasm, have yet to be studied. It is essential to discern the disorders with significant coronary lesions (SCDs) requiring coronary angiography from mimicking disease. We aimed to determine which of all cl...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550076/ https://www.ncbi.nlm.nih.gov/pubmed/36215242 http://dx.doi.org/10.1371/journal.pone.0274416 |
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author | Choi, Jae Young Lee, Jae Hoon Choi, Yuri Hyon, YunKyong Kim, Yong Hwan |
author_facet | Choi, Jae Young Lee, Jae Hoon Choi, Yuri Hyon, YunKyong Kim, Yong Hwan |
author_sort | Choi, Jae Young |
collection | PubMed |
description | BACKGROUND: The early prediction of significant coronary artery lesion, including coronary vasospasm, have yet to be studied. It is essential to discern the disorders with significant coronary lesions (SCDs) requiring coronary angiography from mimicking disease. We aimed to determine which of all clinical variables were more important using conventional logistic regression (cLR) and machine learning (ML). MATERIALS: Of 3382 patients with chest pain/discomfort or dyspnea in whom CAG was performed, 1893 were included. All clinical data were divided as follows (i): Demographics, history, and physical examination; (ii): (i) plus electrocardiography; and (iii): (ii) plus echocardiography, and analyzed by cLR and ML. RESULTS: In multivariable analysis via cLR, the AUC and accuracy of the model using the final 20 variables were 0.795 and 72.62%, respectively. In multivariable analysis via ML, the best AUCs in the internal validation were 0.8 with (i), 0.81 with (ii), 0.83 with (iii), and in external validation, the best AUCs were 0.71 with (i), 0.74 with (ii), and 0.79 with (iii). The best AUCs and accuracy of the fittest model including 21 importance variables by ML were 0.81 and 72.48% in internal validation; and 0.75 and 70.5% in external validation, respectively. The importance variables in ML and cLR were similar, but slightly different and the additional discriminators via ML were found. CONCLUSION: The assessment using the fittest importance variables can assist physicians in differentiating mimicking diseases in which coronary angiography may not be required in patients suspected of having acute coronary syndrome in emergency department. |
format | Online Article Text |
id | pubmed-9550076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-95500762022-10-11 Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom Choi, Jae Young Lee, Jae Hoon Choi, Yuri Hyon, YunKyong Kim, Yong Hwan PLoS One Research Article BACKGROUND: The early prediction of significant coronary artery lesion, including coronary vasospasm, have yet to be studied. It is essential to discern the disorders with significant coronary lesions (SCDs) requiring coronary angiography from mimicking disease. We aimed to determine which of all clinical variables were more important using conventional logistic regression (cLR) and machine learning (ML). MATERIALS: Of 3382 patients with chest pain/discomfort or dyspnea in whom CAG was performed, 1893 were included. All clinical data were divided as follows (i): Demographics, history, and physical examination; (ii): (i) plus electrocardiography; and (iii): (ii) plus echocardiography, and analyzed by cLR and ML. RESULTS: In multivariable analysis via cLR, the AUC and accuracy of the model using the final 20 variables were 0.795 and 72.62%, respectively. In multivariable analysis via ML, the best AUCs in the internal validation were 0.8 with (i), 0.81 with (ii), 0.83 with (iii), and in external validation, the best AUCs were 0.71 with (i), 0.74 with (ii), and 0.79 with (iii). The best AUCs and accuracy of the fittest model including 21 importance variables by ML were 0.81 and 72.48% in internal validation; and 0.75 and 70.5% in external validation, respectively. The importance variables in ML and cLR were similar, but slightly different and the additional discriminators via ML were found. CONCLUSION: The assessment using the fittest importance variables can assist physicians in differentiating mimicking diseases in which coronary angiography may not be required in patients suspected of having acute coronary syndrome in emergency department. Public Library of Science 2022-10-10 /pmc/articles/PMC9550076/ /pubmed/36215242 http://dx.doi.org/10.1371/journal.pone.0274416 Text en © 2022 Choi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Choi, Jae Young Lee, Jae Hoon Choi, Yuri Hyon, YunKyong Kim, Yong Hwan Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom |
title | Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom |
title_full | Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom |
title_fullStr | Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom |
title_full_unstemmed | Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom |
title_short | Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom |
title_sort | prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550076/ https://www.ncbi.nlm.nih.gov/pubmed/36215242 http://dx.doi.org/10.1371/journal.pone.0274416 |
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