A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin
Coronary artery disease (CAD) is one of the most common subtypes of cardiovascular disease. The progression of CAD initiates from the plaque of atherosclerosis and coronary artery stenosis, and eventually turns into acute myocardial infarction (AMI) or stable CAD. Alpha-1-antichymotrypsin (AACT) has...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/6/1415 |
| _version_ | 1797488286338383872 |
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| author | Chen-Chi Chang I-Jung Tsai Wen-Chi Shen Hung-Yi Chen Po-Wen Hsu Ching-Yu Lin |
| author_facet | Chen-Chi Chang I-Jung Tsai Wen-Chi Shen Hung-Yi Chen Po-Wen Hsu Ching-Yu Lin |
| author_sort | Chen-Chi Chang |
| collection | DOAJ |
| description | Coronary artery disease (CAD) is one of the most common subtypes of cardiovascular disease. The progression of CAD initiates from the plaque of atherosclerosis and coronary artery stenosis, and eventually turns into acute myocardial infarction (AMI) or stable CAD. Alpha-1-antichymotrypsin (AACT) has been highly associated with cardiac events. In this study, we proposed incorporating clinical data on AACT levels to establish a model for estimating the severity of CAD. Thirty-six healthy controls (HCs) and 162 CAD patients with stenosis rates of <30%, 30–70%, and >70% were included in this study. Plasma concentration of AACT was determined by enzyme-linked immunosorbent assay (ELISA). The receiver operating characteristic (ROC) curve analysis and associations were conducted. Further, five machine learning models, including decision tree, random forest, support vector machine, XGBoost, and lightGBM were implemented. The lightGBM model obtained a sensitivity of 81.4%, a specificity of 67.3%, and an area under the curve (AUC) of 0.822 for identifying CAD patients with a stenosis rate of <30% versus >30%. In this study, we provided a demonstration of a monitoring model with clinical data and AACT. |
| first_indexed | 2024-03-10T00:00:55Z |
| format | Article |
| id | doaj.art-fda39dcf96af4c42b2b1878517cf3465 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T00:00:55Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-fda39dcf96af4c42b2b1878517cf34652023-11-23T16:17:45ZengMDPI AGDiagnostics2075-44182022-06-01126141510.3390/diagnostics12061415A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-AntichymotrypsinChen-Chi Chang0I-Jung Tsai1Wen-Chi Shen2Hung-Yi Chen3Po-Wen Hsu4Ching-Yu Lin5Department of Laboratory Medicine, Taipei City Hospital Heping-Fuyou Branch, Taipei 10027, TaiwanPh.D. Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, TaiwanInstitute of Biotechnology, National Tsing Hua University, Hsinchu 30013, TaiwanDepartment of Cardiology, Taipei City Hospital Heping-Fuyou Branch, Taipei 10027, TaiwanPreventive Medical Center, Lo-Hsu Medical Foundation Luodong Poh-Ai Hospital, Yilan 26546, TaiwanPh.D. Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, TaiwanCoronary artery disease (CAD) is one of the most common subtypes of cardiovascular disease. The progression of CAD initiates from the plaque of atherosclerosis and coronary artery stenosis, and eventually turns into acute myocardial infarction (AMI) or stable CAD. Alpha-1-antichymotrypsin (AACT) has been highly associated with cardiac events. In this study, we proposed incorporating clinical data on AACT levels to establish a model for estimating the severity of CAD. Thirty-six healthy controls (HCs) and 162 CAD patients with stenosis rates of <30%, 30–70%, and >70% were included in this study. Plasma concentration of AACT was determined by enzyme-linked immunosorbent assay (ELISA). The receiver operating characteristic (ROC) curve analysis and associations were conducted. Further, five machine learning models, including decision tree, random forest, support vector machine, XGBoost, and lightGBM were implemented. The lightGBM model obtained a sensitivity of 81.4%, a specificity of 67.3%, and an area under the curve (AUC) of 0.822 for identifying CAD patients with a stenosis rate of <30% versus >30%. In this study, we provided a demonstration of a monitoring model with clinical data and AACT.https://www.mdpi.com/2075-4418/12/6/1415coronary artery diseasemachine learningplasmabiomarker |
| spellingShingle | Chen-Chi Chang I-Jung Tsai Wen-Chi Shen Hung-Yi Chen Po-Wen Hsu Ching-Yu Lin A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin Diagnostics coronary artery disease machine learning plasma biomarker |
| title | A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin |
| title_full | A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin |
| title_fullStr | A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin |
| title_full_unstemmed | A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin |
| title_short | A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin |
| title_sort | coronary artery disease monitoring model built from clinical data and alpha 1 antichymotrypsin |
| topic | coronary artery disease machine learning plasma biomarker |
| url | https://www.mdpi.com/2075-4418/12/6/1415 |
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