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Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD

Conventional prognostic risk analysis in patients undergoing noninvasive imaging is based upon a limited selection of clinical and imaging findings, whereas machine learning (ML) algorithms include a greater number and complexity of variables. Therefore, this paper aimed to explore the predictive va...

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Autores principales: Dou, Guanhua, Shan, Dongkai, Wang, Kai, Wang, Xi, Liu, Zinuan, Zhang, Wei, Li, Dandan, He, Bai, Jing, Jing, Wang, Sicong, Chen, Yundai, Yang, Junjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025955/
https://www.ncbi.nlm.nih.gov/pubmed/35455712
http://dx.doi.org/10.3390/jpm12040596
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author Dou, Guanhua
Shan, Dongkai
Wang, Kai
Wang, Xi
Liu, Zinuan
Zhang, Wei
Li, Dandan
He, Bai
Jing, Jing
Wang, Sicong
Chen, Yundai
Yang, Junjie
author_facet Dou, Guanhua
Shan, Dongkai
Wang, Kai
Wang, Xi
Liu, Zinuan
Zhang, Wei
Li, Dandan
He, Bai
Jing, Jing
Wang, Sicong
Chen, Yundai
Yang, Junjie
author_sort Dou, Guanhua
collection PubMed
description Conventional prognostic risk analysis in patients undergoing noninvasive imaging is based upon a limited selection of clinical and imaging findings, whereas machine learning (ML) algorithms include a greater number and complexity of variables. Therefore, this paper aimed to explore the predictive value of integrating coronary plaque information from coronary computed tomographic angiography (CCTA) with ML to predict major adverse cardiovascular events (MACEs) in patients with suspected coronary artery disease (CAD). Patients who underwent CCTA due to suspected coronary artery disease with a 30-month follow-up for MACEs were included. We collected demographic characteristics, cardiovascular risk factors, and information on coronary plaques by analyzing CCTA information (plaque length, plaque composition and coronary artery stenosis of 18 coronary artery segments, coronary dominance, myocardial bridge (MB), and patients with vulnerable plaque) and follow-up information (cardiac death, nonfatal myocardial infarction and unstable angina requiring hospitalization). An ML algorithm was used for survival analysis (CoxBoost). This analysis showed that chest symptoms, the stenosis severity of the proximal anterior descending branch, and the stenosis severity of the middle right coronary artery were among the top three variables in the ML model. After the 22nd month of follow-up, in the testing dataset, ML showed the largest C-index and AUC compared with Cox regression, SIS, SIS score + clinical factors, and clinical factors. The DCA of all the models showed that the net benefit of the ML model was the highest when the treatment threshold probability was between 1% and 9%. Integrating coronary plaque information from CCTA based on ML technology provides a feasible and superior method to assess prognosis in patients with suspected coronary artery disease over an approximately three-year period.
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spelling pubmed-90259552022-04-23 Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD Dou, Guanhua Shan, Dongkai Wang, Kai Wang, Xi Liu, Zinuan Zhang, Wei Li, Dandan He, Bai Jing, Jing Wang, Sicong Chen, Yundai Yang, Junjie J Pers Med Article Conventional prognostic risk analysis in patients undergoing noninvasive imaging is based upon a limited selection of clinical and imaging findings, whereas machine learning (ML) algorithms include a greater number and complexity of variables. Therefore, this paper aimed to explore the predictive value of integrating coronary plaque information from coronary computed tomographic angiography (CCTA) with ML to predict major adverse cardiovascular events (MACEs) in patients with suspected coronary artery disease (CAD). Patients who underwent CCTA due to suspected coronary artery disease with a 30-month follow-up for MACEs were included. We collected demographic characteristics, cardiovascular risk factors, and information on coronary plaques by analyzing CCTA information (plaque length, plaque composition and coronary artery stenosis of 18 coronary artery segments, coronary dominance, myocardial bridge (MB), and patients with vulnerable plaque) and follow-up information (cardiac death, nonfatal myocardial infarction and unstable angina requiring hospitalization). An ML algorithm was used for survival analysis (CoxBoost). This analysis showed that chest symptoms, the stenosis severity of the proximal anterior descending branch, and the stenosis severity of the middle right coronary artery were among the top three variables in the ML model. After the 22nd month of follow-up, in the testing dataset, ML showed the largest C-index and AUC compared with Cox regression, SIS, SIS score + clinical factors, and clinical factors. The DCA of all the models showed that the net benefit of the ML model was the highest when the treatment threshold probability was between 1% and 9%. Integrating coronary plaque information from CCTA based on ML technology provides a feasible and superior method to assess prognosis in patients with suspected coronary artery disease over an approximately three-year period. MDPI 2022-04-07 /pmc/articles/PMC9025955/ /pubmed/35455712 http://dx.doi.org/10.3390/jpm12040596 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dou, Guanhua
Shan, Dongkai
Wang, Kai
Wang, Xi
Liu, Zinuan
Zhang, Wei
Li, Dandan
He, Bai
Jing, Jing
Wang, Sicong
Chen, Yundai
Yang, Junjie
Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD
title Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD
title_full Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD
title_fullStr Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD
title_full_unstemmed Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD
title_short Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD
title_sort integrating coronary plaque information from ccta by ml predicts mace in patients with suspected cad
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025955/
https://www.ncbi.nlm.nih.gov/pubmed/35455712
http://dx.doi.org/10.3390/jpm12040596
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