Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry

A.R. van Rosendael, G. Maliakal, K.K. Kolli, A. Beecy, S.J. Al'Aref, A. Dwivedi, G. Singh, M. Panday, A. Kumar, X. Ma, S. Achenbach, M.H. Al-Mallah, D. Andreini, J.J. Bax, D.S. Berman, M.J. Budoff, F. Cademartiri, T.Q. Callister, H.-J. Chang, K. ChinnaiyanB.J.W. Chow, R.C. Cury, A. DeLago, G. Feuchtner, M. Hadamitzky, J. Hausleiter, P.A. Kaufmann, Y.-J. Kim, J.A. Leipsic, E. Maffei, H. Marques, G. Pontone, G.L. Raff, R. Rubinshtein, L.J. Shaw, T.C. Villines, H. Gransar, Y. Lu, E.C. Jones, J.M. Peña, F.Y. Lin, J.K. Min

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Introduction: Machine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores. Methods: From the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1–24%, 25–49%, 50–69%, 70–99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data). Results: In total, 8844 patients (mean age 58.0 ± 11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ± 1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P <0.001. Net reclassification improvement analysis showed that the improved risk stratification was the result of down-classification of risk among patients that did not experience events (non-events). Conclusion: A risk score created by a ML based algorithm, that utilizes standard 16 coronary segment stenosis and composition information derived from detailed CCTA reading, has greater prognostic accuracy than current CCTA integrated risk scores. These findings indicate that a ML based algorithm can improve the integration of CCTA derived plaque information to improve risk stratification. © 2018
Original languageEnglish
Pages (from-to)204-209
Number of pages6
JournalJournal of Cardiovascular Computed Tomography
Issue number3
Publication statusPublished - 2018


  • adult
  • Article
  • cardiovascular disease assessment
  • cardiovascular mortality
  • clinical outcome
  • computed tomographic angiography
  • coronary angiography
  • coronary artery obstruction
  • duke prognostic index
  • female
  • follow up
  • heart infarction
  • human
  • leaman risk score
  • machine learning
  • major clinical study
  • male
  • middle aged
  • multicenter study
  • priority journal
  • segment involvement score
  • segment stenosis score
  • aged
  • algorithm
  • area under the curve
  • atherosclerotic plaque
  • clinical trial
  • computer assisted diagnosis
  • coronary artery disease
  • coronary blood vessel
  • diagnostic imaging
  • mortality
  • multidetector computed tomography
  • pathology
  • predictive value
  • procedures
  • prognosis
  • receiver operating characteristic
  • register
  • reproducibility
  • risk assessment
  • risk factor
  • severity of illness index
  • time factor
  • Aged
  • Algorithms
  • Area Under Curve
  • Computed Tomography Angiography
  • Coronary Angiography
  • Coronary Artery Disease
  • Coronary Stenosis
  • Coronary Vessels
  • Female
  • Humans
  • Machine Learning
  • Male
  • Middle Aged
  • Multidetector Computed Tomography
  • Myocardial Infarction
  • Plaque, Atherosclerotic
  • Predictive Value of Tests
  • Prognosis
  • Radiographic Image Interpretation, Computer-Assisted
  • Registries
  • Reproducibility of Results
  • Risk Assessment
  • Risk Factors
  • ROC Curve
  • Severity of Illness Index
  • Time Factors


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