Frontiers in Cardiovascular Medicine (Feb 2023)

Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events

  • Hassan A. Zaidi,
  • Richard E. Jones,
  • Richard E. Jones,
  • Daniel J. Hammersley,
  • Daniel J. Hammersley,
  • Suzan Hatipoglu,
  • Gabriel Balaban,
  • Gabriel Balaban,
  • Lukas Mach,
  • Lukas Mach,
  • Brian P. Halliday,
  • Brian P. Halliday,
  • Pablo Lamata,
  • Sanjay K. Prasad,
  • Sanjay K. Prasad,
  • Martin J. Bishop

DOI
https://doi.org/10.3389/fcvm.2023.1082778
Journal volume & issue
Vol. 10

Abstract

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BackgroundMachine learning analysis of complex myocardial scar patterns affords the potential to enhance risk prediction of life-threatening arrhythmia in stable coronary artery disease (CAD).ObjectiveTo assess the utility of computational image analysis, alongside a machine learning (ML) approach, to identify scar microstructure features on late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) that predict major arrhythmic events in patients with CAD.MethodsPatients with stable CAD were prospectively recruited into a CMR registry. Shape-based scar microstructure features characterizing heterogeneous (‘peri-infarct’) and homogeneous (‘core’) fibrosis were extracted. An ensemble of machine learning approaches were used for risk stratification, in addition to conventional analysis using Cox modeling.ResultsOf 397 patients (mean LVEF 45.4 ± 16.0) followed for a median of 6 years, 55 patients (14%) experienced a major arrhythmic event. When applied within an ML model for binary classification, peri-infarct zone (PIZ) entropy, peri-infarct components and core interface area outperformed a model representative of the current standard of care (LVEF<35% and NYHA>Class I): AUROC (95%CI) 0.81 (0.81–0.82) vs. 0.64 (0.63–0.65), p = 0.002. In multivariate cox regression analysis, these features again remained significant after adjusting for LVEF<35% and NYHA>Class I: PIZ entropy hazard ratio (HR) 1.88, 95% confidence interval (CI) 1.38–2.56, p < 0.001; number of PIZ components HR 1.34, 95% CI 1.08–1.67, p = 0.009; core interface area HR 1.6, 95% CI 1.29–1.99, p = <0.001.ConclusionMachine learning models using LGE-CMR scar microstructure improved arrhythmic risk stratification as compared to guideline-based clinical parameters; highlighting a potential novel approach to identifying candidates for implantable cardioverter defibrillators in stable CAD.

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