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

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.

Quantitative assessment of coronary plaque volume change related to triglyceride glucose index: The Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography IMaging (PARADIGM) registry

“…While total percent atheroma volume (PAV total ) (%) was defined as PV divided by vessel volume, multiplied by 100, annual change of PAV total (%/year) was defined as total PAV divided by inter-scan period, and plaque progression (PP) was defined as the difference in plaque volume between follow-up and baseline CCTA > 0. Further, rapid PP (%/ year) was defined as an annual progression of PAV ≥ 1.0% [19,20]. Representative CCTA images are presented in Fig.…”

Application of machine learning in understanding atherosclerosis: Emerging insights

“… 30 A recent ML application related to CCTA was based on data from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. 20 The PARADIGM data were collected between 2003 and 2015 as part of a multi-center effort. At the time of the analysis, 1083 consecutive patients had undergone serial CCTA and met the inclusion criteria for the analysis.…”

“…At the time of the analysis, 1083 consecutive patients had undergone serial CCTA and met the inclusion criteria for the analysis. 20 After analysis using ML focused on selecting and ranking each feature's importance for classifying individuals at risk for rapid plaque progression, the authors were able to determine that quantitative atherosclerotic plaque characterization was most influential. This was followed by qualitative plaque characterization by CCTA variables and then clinical and laboratory measures.…”

“…This was followed by qualitative plaque characterization by CCTA variables and then clinical and laboratory measures. 20 …”

Ética, Inteligência Artificial e Cardiologia

“…Particularmente, na cardiologia, várias aplicações se mostraram exitosas. Han et al, 2 por exemplo, usaram o Machine Learning (ML), um subconjunto da IA, para analisar se essa ferramenta seria útil para identificar pacientes com risco de rápida progressão de placa coronariana. Foram utilizadas características epidemiológicas clínicas e informações quantitativas e qualitativas da angiografia tomográfica computadorizada das coronárias (todas obtidas a partir do estudo PARADIGM).…”