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Updated: May 5, 2026
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
Published on: February 12, 2011
Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony
Addison Gearhart1, Sunakshi Bassi2, Rahul H Rathod1
1Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
Insights
Machine learning identified four distinct patient groups in Fontan circulation using cardiovascular magnetic resonance (CMR) data. This approach helps stratify risk and understand outcomes in this complex patient population.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Fontan circulation patients are a heterogeneous group with poor outcomes.
- Ventricular dilation, dysfunction, and dyssynchrony contribute to adverse events.
- Current risk stratification for Fontan patients is limited.
Purpose of the Study:
- To apply unsupervised machine learning to cardiovascular magnetic resonance (CMR)-derived dyssynchrony metrics.
- To differentiate Fontan ventricles from normal controls.
- To identify prognostically distinct subgroups within the Fontan population.
Main Methods:
- Retrospective analysis of 503 Fontan patient CMR studies and 42 controls.
- Feature tracking assessed ventricular strain, strain rate, and displacement.
- Unsupervised K-means clustering analyzed 24 mechanical dyssynchrony metrics.
Main Results:
- Four distinct phenotypic clusters were identified in Fontan patients.
- The highest-risk cluster showed a significantly higher hazard for adverse outcomes (death or transplantation).
- Risk stratification was independent of ventricular morphology and indexed end-diastolic volume.
Conclusions:
- Unsupervised machine learning effectively identified distinct patient clusters in Fontan circulation.
- This method reveals varying clinical characteristics and risk profiles.
- The technique aids in stratifying heterogeneous Fontan populations for targeted research and care.
Background:
Individuals with a Fontan circulation encompass a heterogeneous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster analysis of cardiovascular magnetic resonance (CMR)-derived dyssynchrony metrics can separate ventricles in the Fontan circulation from normal control left ventricles and identify prognostically distinct subgroups within the Fontan cohort.
Methods:
This single-center, retrospective study used 503 CMR studies from Fontan patients (median age 15 y) and 42 from age-matched controls from January 2005 to May 2011. Feature tracking on short-axis cine stacks assessed radial and circumferential strain, strain rate, and displacement. Unsupervised K-means clustering was applied to 24 mechanical dyssynchrony metrics derived from these deformation measurements. Clusters were compared for demographic, anatomical, and composite outcomes of death, or heart transplantation.
Results:
Four distinct phenotypic clusters were identified. Over a median follow-up of 4.2 y (interquartile ranges 1.7-8.8 y), 58 (11.5%) patients met the composite outcome. The highest-risk cluster (largely comprised of right or mixed ventricular morphology and dilated, dyssynchronous ventricles) exhibited a higher hazard for the composite outcome compared to the lowest-risk cluster while controlling for ventricular morphology (hazard ratio [HR] 6.4; 95% confidence interval [CI] 2.1-19.3; P value 0.001) and higher indexed end-diastolic volume (HR 3.2; 95% CI 1.04-10.0; P value 0.043) per 10 mL/m2.
Conclusion:
Unsupervised machine learning using CMR-derived dyssynchrony metrics identified four distinct clusters of patients with Fontan circulation and healthy controls with varying clinical characteristics and risk profiles. This technique can be used to guide future studies and identify more homogeneous subsets of patients from an overall heterogeneous population.
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