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New Model for Predicting the Presence of Coronary Artery Calcification

Coronary artery calcification (CAC) is a feature of coronary atherosclerosis and a well-known risk factor for cardiovascular disease (CVD). As the absence of CAC is associated with a lower incidence rate of CVD, measurement of a CAC score is helpful for risk stratification when the risk decision is...

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Autores principales: Park, Samel, Hong, Min, Lee, HwaMin, Cho, Nam-jun, Lee, Eun-Young, Lee, Won-Young, Rhee, Eun-Jung, Gil, Hyo-Wook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865676/
https://www.ncbi.nlm.nih.gov/pubmed/33503990
http://dx.doi.org/10.3390/jcm10030457
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author Park, Samel
Hong, Min
Lee, HwaMin
Cho, Nam-jun
Lee, Eun-Young
Lee, Won-Young
Rhee, Eun-Jung
Gil, Hyo-Wook
author_facet Park, Samel
Hong, Min
Lee, HwaMin
Cho, Nam-jun
Lee, Eun-Young
Lee, Won-Young
Rhee, Eun-Jung
Gil, Hyo-Wook
author_sort Park, Samel
collection PubMed
description Coronary artery calcification (CAC) is a feature of coronary atherosclerosis and a well-known risk factor for cardiovascular disease (CVD). As the absence of CAC is associated with a lower incidence rate of CVD, measurement of a CAC score is helpful for risk stratification when the risk decision is uncertain. This was a retrospective study with an aim to build a model to predict the presence of CAC (i.e., CAC score = 0 or not) and evaluate the discrimination and calibration power of the model. Our data set was divided into two set (80% for training set and 20% for test set). Ten-fold cross-validation was applied with ten times of interaction in each fold. We built prediction models using logistic regression (LRM), classification and regression tree (CART), conditional inference tree (CIT), and random forest (RF). A total of 3302 patients from two cohorts (Soonchunhyang University Cheonan Hospital and Kangbuk Samsung Health Study) were enrolled. These patients’ ages were between 40 and 75 years. All models showed acceptable accuracies (LRM, 70.71%; CART, 71.32%; CIT, 71.32%; and RF, 71.02%). The decision tree model using CART and CIT showed a reasonable accuracy without complexity. It could be implemented in real-world practice.
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spelling pubmed-78656762021-02-07 New Model for Predicting the Presence of Coronary Artery Calcification Park, Samel Hong, Min Lee, HwaMin Cho, Nam-jun Lee, Eun-Young Lee, Won-Young Rhee, Eun-Jung Gil, Hyo-Wook J Clin Med Article Coronary artery calcification (CAC) is a feature of coronary atherosclerosis and a well-known risk factor for cardiovascular disease (CVD). As the absence of CAC is associated with a lower incidence rate of CVD, measurement of a CAC score is helpful for risk stratification when the risk decision is uncertain. This was a retrospective study with an aim to build a model to predict the presence of CAC (i.e., CAC score = 0 or not) and evaluate the discrimination and calibration power of the model. Our data set was divided into two set (80% for training set and 20% for test set). Ten-fold cross-validation was applied with ten times of interaction in each fold. We built prediction models using logistic regression (LRM), classification and regression tree (CART), conditional inference tree (CIT), and random forest (RF). A total of 3302 patients from two cohorts (Soonchunhyang University Cheonan Hospital and Kangbuk Samsung Health Study) were enrolled. These patients’ ages were between 40 and 75 years. All models showed acceptable accuracies (LRM, 70.71%; CART, 71.32%; CIT, 71.32%; and RF, 71.02%). The decision tree model using CART and CIT showed a reasonable accuracy without complexity. It could be implemented in real-world practice. MDPI 2021-01-25 /pmc/articles/PMC7865676/ /pubmed/33503990 http://dx.doi.org/10.3390/jcm10030457 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Park, Samel
Hong, Min
Lee, HwaMin
Cho, Nam-jun
Lee, Eun-Young
Lee, Won-Young
Rhee, Eun-Jung
Gil, Hyo-Wook
New Model for Predicting the Presence of Coronary Artery Calcification
title New Model for Predicting the Presence of Coronary Artery Calcification
title_full New Model for Predicting the Presence of Coronary Artery Calcification
title_fullStr New Model for Predicting the Presence of Coronary Artery Calcification
title_full_unstemmed New Model for Predicting the Presence of Coronary Artery Calcification
title_short New Model for Predicting the Presence of Coronary Artery Calcification
title_sort new model for predicting the presence of coronary artery calcification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865676/
https://www.ncbi.nlm.nih.gov/pubmed/33503990
http://dx.doi.org/10.3390/jcm10030457
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