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Updated: Jun 14, 2026
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
Unsupervised clustering of intra-ventricular haemodynamic forces for the phenotyping of left ventricular function in
Lorenzo Airale1,2, Alessandro Giustiniani2,3, Eduard Ródenas-Alesina2,4,5
1Division of Internal Medicine, Hypertension Unit, Department of Medical Sciences, Città della Salute e della Scienza Hospital, University of Turin, Turin, Italy.
Insights
Unsupervised clustering of cardiac haemodynamic forces (HDFs) in non-ischaemic LV cardiomyopathy identifies distinct patient groups. These clusters stratify cardiovascular risk beyond traditional measures like LGE and EF.
Area of Science:
- Cardiology
- Biomedical Engineering
- Medical Imaging
Background:
- Cardiac magnetic resonance (CMR) is crucial for diagnosing cardiomyopathy.
- Haemodynamic forces (HDFs) analysis from cine CMR images offers novel insights into heart-blood energy exchange.
- Previous studies focused on peak HDF values, necessitating exploration of dynamic HDF patterns.
Purpose of the Study:
- To investigate if unsupervised clustering of HDF curves can identify distinct phenotypes in non-ischaemic LV cardiomyopathy (NILVC).
- To determine if these identified clusters can stratify cardiovascular (CV) risk in NILVC patients.
- To assess the prognostic value of HDF patterns beyond established markers.
Main Methods:
- Retrospective analysis of 279 NILVC patients undergoing CMR.
- Unsupervised clustering of longitudinal and transversal HDF curves using dynamic time warping and partitioning around medoids.
- Composite outcome definition: CV mortality, heart failure hospitalization, and ventricular arrhythmias.
Main Results:
- Three distinct HDF clusters were identified, correlating with worsening left atrial and left ventricular function.
- A stepwise increase in CV risk profile was observed across the clusters.
- Clusters 2 and 3 showed significantly higher risk of adverse CV events compared to Cluster 1, even after adjusting for LGE and LV ejection fraction.
Conclusions:
- Analysis of longitudinal and transversal HDFs throughout the cardiac cycle reveals distinct patient phenotypes in NILVC.
- These HDF-derived phenotypes possess prognostic value exceeding that of ejection fraction and late gadolinium enhancement.
- Unsupervised clustering of HDFs offers a promising tool for CV risk stratification in NILVC.
Aims:
Cardiac magnetic resonance (CMR) is essential for diagnosing cardiomyopathy, serving as the gold standard for assessing heart chamber volumes and tissue characterization. Haemodynamic forces (HDFs) analysis, a novel approach using standard cine CMR images, estimates energy exchange between the left ventricular (LV) wall and blood. While prior research has focused on peak or mean longitudinal HDF values, this study aims to investigate whether unsupervised clustering of HDF curves can identify clinically significant patterns and stratify cardiovascular (CV) risk in non-ischaemic LV cardiomyopathy (NILVC).
Methods And Results:
A retrospective cohort of 279 patients with NILVC who underwent cardiac CMR at Vall d'Hebron University Hospital (Barcelona) was examined. Unsupervised clustering of longitudinal and transversal HDF curves was performed using dynamic time warping for dissimilarity measurement and the partitioning around medoids algorithm. Outcomes were defined as a composite of CV mortality, heart failure hospitalization, and ventricular arrhythmias. The median age was 65 (57.0; 74.0) years, with 27.2% females and 35.5% showing late gadolinium enhancement (LGE). Unsupervised clustering identified three distinct clusters, delineating risk groups with worsening LA and LV function, indicating a stepwise increase in CV risk profile. Over a median follow-up of 40 months, 60 patients experienced the composite outcome. After adjusting for LGE, LV ejection fraction (EF), and LV size, Clusters 2 and 3 demonstrated a significantly higher risk of adverse events (both P < 0.05) compared with Cluster 1.
Conclusion:
Analysing both longitudinal and transversal HDFs throughout the cardiac cycle enables the identification of distinct phenotypes with prognostic value beyond EF and LGE in NILVC patients.
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