A machine learning model integrating quantitative plaque features with clinical variables predicted rapid plaque progression better than the ASCVD risk score (AUC 0.83 vs 0.60; P<0.001).
Observational (n=1,083)
Does a machine learning framework integrating quantitative and qualitative CCTA plaque features improve the prediction of rapid coronary plaque progression compared to clinical risk scores?
A machine learning framework incorporating quantitative CCTA plaque features significantly improves the identification of patients at risk for rapid coronary plaque progression compared to traditional clinical risk scores.
Effect estimate: AUC (95% CI 0.78-0.89)
Absolute Event Rate: 0.83% vs 0.6%
p-value: p=<0.001
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.
Han et al. (Sat,) conducted a observational in Coronary Atherosclerosis (n=1,083). Machine learning framework (ML model 3) vs. Atherosclerotic cardiovascular disease (ASCVD) risk score was evaluated on Prediction of rapid coronary plaque progression (annual progression of percentage atheroma volume ≥1.0%) (AUC, 95% CI 0.78-0.89, p=<0.001). A machine learning model integrating quantitative plaque features with clinical variables predicted rapid plaque progression better than the ASCVD risk score (AUC 0.83 vs 0.60; P<0.001).
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