Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom
<h4>Background</h4> 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 deter...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550076/?tool=EBI |
| _version_ | 1811240899908206592 |
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| author | Jae Young Choi Jae Hoon Lee Yuri Choi YunKyong Hyon Yong Hwan Kim |
| author_facet | Jae Young Choi Jae Hoon Lee Yuri Choi YunKyong Hyon Yong Hwan Kim |
| author_sort | Jae Young Choi |
| collection | DOAJ |
| description | <h4>Background</h4> 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). <h4>Materials</h4> 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. <h4>Results</h4> 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. <h4>Conclusion</h4> 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. |
| first_indexed | 2024-04-12T13:27:46Z |
| format | Article |
| id | doaj.art-e75422d89b104b288623508079de69ea |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T13:27:46Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-e75422d89b104b288623508079de69ea2022-12-22T03:31:16ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011710Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptomJae Young ChoiJae Hoon LeeYuri ChoiYunKyong HyonYong Hwan Kim<h4>Background</h4> 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). <h4>Materials</h4> 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. <h4>Results</h4> 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. <h4>Conclusion</h4> 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.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550076/?tool=EBI |
| spellingShingle | Jae Young Choi Jae Hoon Lee Yuri Choi YunKyong Hyon Yong Hwan Kim Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom PLoS ONE |
| 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 |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550076/?tool=EBI |
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