Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry

JACC Cardiovasc Imaging. 2020 Mar;13(3):760-770. doi: 10.1016/j.jcmg.2019.06.027. Epub 2019 Aug 14.


Objectives: This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-FFR).

Background: CT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated.

Methods: A total of 482 vessels from 314 patients (age 62.3 ± 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and ≥400) on a per-vessel level with invasive FFR as the reference standard.

Results: The diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC ≥400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57 to 0.85] vs. 0.85 [95% CI: 0.82 to 0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC ≥ 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001).

Conclusions: Machine-learning-based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621).

Keywords: computational fractional flow reserve; coronary artery disease; coronary computed tomography angiography; invasive coronary angiography.

Publication types

  • Multicenter Study

MeSH terms

  • Aged
  • Asia
  • Computed Tomography Angiography*
  • Coronary Angiography*
  • Coronary Artery Disease / diagnostic imaging*
  • Coronary Artery Disease / physiopathology
  • Diagnosis, Computer-Assisted*
  • Europe
  • Female
  • Fractional Flow Reserve, Myocardial*
  • Humans
  • Machine Learning*
  • Male
  • Middle Aged
  • North America
  • Predictive Value of Tests
  • Radiographic Image Interpretation, Computer-Assisted*
  • Registries
  • Reproducibility of Results
  • Severity of Illness Index
  • Vascular Calcification / diagnostic imaging*
  • Vascular Calcification / physiopathology

Associated data