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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-7865676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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