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Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry
- Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry
- Han D.; Kolli K.K.; Al'Aref S.J.; Baskaran L.; van Rosendael A.R.; Gransar H.; Andreini D.; Budoff M.J.; Cademartiri F.; Chinnaiyan K.; Choi J.H.; Conte E.; Marques H.; de Araújo Gonçalves P.; Gottlieb I.; Hadamitzky M.; Leipsic J.A.; Maffei E.; Pontone G.; Raff G.L.; Shin S.; Kim Y.-J.; Lee B.K.; Chun E.J.; Sung J.M.; Lee S.-E.; Virmani R.; Samady H.; Stone P.; Narula J.; Berman D.S.; Bax J.J.; Shaw L.J.; Lin F.Y.; Min J.K.; Chang H.-J.
- Ewha Authors
- Issue Date
- Journal Title
- Journal of the American Heart Association
- Journal of the American Heart Association vol. 9, no. 5, pp. e013958
- coronary artery disease; coronary computed tomography angiography; machine learning; plaque progression; risk prediction
- NLM (Medline)
- SCIE; SCOPUS
- Document Type
- 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.
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