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Updated: Jul 16, 2025
3D Whole-heart Myocardial Tissue Analysis
Published on: April 12, 2017
Quantification of Myocardial Contraction Fraction with Three-Dimensional Automated, Machine-Learning-Based
Andrea Barbieri1, Jacopo F Imberti1,2, Mario Bartolomei1
1Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, Policlinico di Modena, University of Modena and Reggio Emilia, 41124 Modena, Italy.
Insights
Myocardial contraction fraction (MCF) measured by dynamic heart model (DHM) effectively distinguishes cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) in patients with normal ejection fraction (EF). This novel approach aids in diagnosing hypertrophic phenotypes.
Area of Science:
- Cardiology
- Medical Imaging
- Biomedical Engineering
Background:
- Differentiating left ventricular (LV) hypertrophic phenotypes with normal ejection fraction (EF) presents a clinical challenge.
- Myocardial contraction fraction (MCF) is a dimensionless index, previously calculated via cardiac magnetic resonance, aiding in identifying cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM).
- Automated, machine-learning-based LV chamber metrics, such as the dynamic heart model (DHM), offer advanced analysis of cardiac function.
Purpose of the Study:
- To evaluate the utility of MCF, derived from DHM, in discriminating between various hypertrophic phenotypes.
- To assess MCF's ability to differentiate CA and HCM from isolated hypertension (IH) and healthy controls in patients with normal EF.
Main Methods:
- Analysis of DHM-derived LV metrics in patients with CA (n=10), HCM (n=36), IH (n=87), and healthy controls (n=54).
- Calculation of MCF by dividing LV stroke volume by LV myocardial volume.
- Receiver Operating Characteristic (ROC) curve analysis to determine diagnostic performance.
Main Results:
- MCF was significantly reduced in HCM (median 48.55%) and CA (median 40.92%) compared to controls (median 61.95%), but not in IH (median 59.35%).
- MCF demonstrated weak correlation with EF across all groups, highlighting its independence as a metric.
- ROC analysis indicated that MCF could differentiate healthy adults from HCM (AUC 0.814) and CA (AUC 0.959), with optimal cut-off values of 55.3% and 52.8% respectively.
Conclusions:
- Easily quantifiable MCF using DHM enhances echocardiographic discrimination of hypertrophic phenotypes with normal EF.
- MCF derived from DHM shows significant potential as a diagnostic tool for CA and HCM.
- Incorporating MCF into the diagnostic workup is recommended for patients presenting with hypertrophic phenotypes and normal EF.
Abstract:
Aims: The differentiation of left ventricular (LV) hypertrophic phenotypes is challenging in patients with normal ejection fraction (EF). The myocardial contraction fraction (MCF) is a simple dimensionless index useful for specifically identifying cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) when calculated by cardiac magnetic resonance. The purpose of this study was to evaluate the value of MCF measured by three-dimensional automated, machine-learning-based LV chamber metrics (dynamic heart model [DHM]) for the discrimination of different forms of hypertrophic phenotypes. Methods and Results: We analyzed the DHM LV metrics of patients with CA (n = 10), hypertrophic cardiomyopathy (HCM, n = 36), isolated hypertension (IH, n = 87), and 54 healthy controls. MCF was calculated by dividing LV stroke volume by LV myocardial volume. Compared with controls (median 61.95%, interquartile range 55.43-67.79%), mean values for MCF were significantly reduced in HCM-48.55% (43.46-54.86% p < 0.001)-and CA-40.92% (36.68-46.84% p < 0.002)-but not in IH-59.35% (53.22-64.93% p < 0.7). MCF showed a weak correlation with EF in the overall cohort (R2 = 0.136) and the four study subgroups (healthy adults, R2 = 0.039 IH, R2 = 0.089; HCM, R2 = 0.225; CA, R2 = 0.102). ROC analyses showed that MCF could differentiate between healthy adults and HCM (sensitivity 75.9%, specificity 77.8%, AUC 0.814) and between healthy adults and CA (sensitivity 87.0%, specificity 100%, AUC 0.959). The best cut-off values were 55.3% and 52.8%. Conclusions: The easily derived quantification of MCF by DHM can refine our echocardiographic discrimination capacity in patients with hypertrophic phenotype and normal EF. It should be added to the diagnostic workup of these patients.
