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Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and

Julie K Shade1,2, Adityo Prakosa1,2, Dan M Popescu2,3

  • 1Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.

Science Advances
|July 29, 2021
PubMed

Insights

Cardiac sarcoidosis (CS) patients face high sudden cardiac death (SCD) risk. A new precision technology using imaging and AI improves risk prediction, outperforming current methods for better prevention strategies.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Computational Biology

Background:

  • Cardiac sarcoidosis (CS) presents a significant risk for sudden cardiac death (SCD) due to ventricular arrhythmias.
  • Current risk assessment guidelines for SCD in CS are inadequate, leading to suboptimal primary prevention.
  • Heterogeneous ventricular remodeling in CS contributes to arrhythmogenesis.

Purpose of the Study:

  • To develop and validate a novel, two-step precision risk prediction technology for identifying patients with cardiac sarcoidosis at high risk of SCD.
  • To improve upon existing clinical metrics for SCD risk stratification in CS.

Main Methods:

  • A personalized mechanistic model integrating magnetic-resonance imaging (MRI) and positron-emission tomography (PET) was used to assess arrhythmogenic propensity.
  • Simulations of arrhythmogenesis were combined with imaging and clinical biomarkers.
  • A supervised classifier was trained and tested on retrospective data from 45 CS patients.

Main Results:

  • The precision risk prediction technology demonstrated a sensitivity of 60% (95% CI: 57-63%) and specificity of 72% (95% CI: 70-74%).
  • The area under the receiver operating characteristic curve (AUC) was 0.754 (95% CI: 0.710-0.797).
  • The developed technology outperformed traditional clinical metrics in predicting SCD risk.

Conclusions:

  • This novel precision risk prediction technology shows significant potential for transforming SCD risk stratification in cardiac sarcoidosis patients.
  • Personalized modeling and machine learning offer a promising approach to enhance primary prevention strategies for CS.
  • Accurate risk assessment is crucial for guiding interventions and improving outcomes in CS.

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