19 31

Cited 0 times in

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry

Authors
 Donghee Han  ;  Kranthi K Kolli  ;  Subhi J Al'Aref  ;  Lohendran Baskaran  ;  Alexander R van Rosendael  ;  Heidi Gransar  ;  Daniele Andreini  ;  Matthew J Budoff  ;  Filippo Cademartiri  ;  Kavitha Chinnaiyan  ;  Jung Hyun Choi  ;  Edoardo Conte  ;  Hugo Marques  ;  Pedro de Araújo Gonçalves  ;  Ilan Gottlieb  ;  Martin Hadamitzky  ;  Jonathon A Leipsic  ;  Erica Maffei  ;  Gianluca Pontone  ;  Gilbert L Raff  ;  Sangshoon Shin  ;  Yong-Jin Kim  ;  Byoung Kwon Lee  ;  Eun Ju Chun  ;  Ji Min Sung  ;  Sang-Eun Lee  ;  Renu Virmani  ;  Habib Samady  ;  Peter Stone  ;  Jagat Narula  ;  Daniel S Berman  ;  Jeroen J Bax  ;  Leslee J Shaw  ;  Fay Y Lin  ;  James K Min  ;  Hyuk-Jae Chang 
Citation
 JOURNAL OF THE AMERICAN HEART ASSOCIATION, Vol.9(5) : e013958, 2020-03 
Journal Title
 JOURNAL OF THE AMERICAN HEART ASSOCIATION 
Issue Date
2020-03
Keywords
coronary artery disease ; coronary computed tomography angiography ; machine learning ; plaque progression ; risk prediction
Abstract
Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP.
Files in This Item:
T202001209.pdf Download
DOI
10.1161/JAHA.119.013958
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Lee, Byoung Kwon(이병권) ORCID logo https://orcid.org/0000-0001-9259-2776
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
Han, Donghee(한동희)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/176038
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links