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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...

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Autores principales: Choi, Jae Young, Lee, Jae Hoon, Choi, Yuri, Hyon, YunKyong, Kim, Yong Hwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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.
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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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