Key result
A functional data analysis approach using functional principal component analysis scores achieved a discrimination accuracy of 98.2% between healthy and hypertrophic subjects, outperforming traditional clinical parameters which reached 65.96%.
Why the study?
Left ventricular remodeling is often overlooked because routine diagnostic parameters mainly assess volumetric aspects rather than quantitative structural modifications.
Does a Functional Data Analysis approach improve the discrimination between healthy subjects and those with left ventricular hypertrophy compared to traditional clinical parameters?
Does a Functional Data Analysis approach improve the discrimination between healthy subjects and those with left ventricular hypertrophy compared to traditional clinical parameters?
A novel Functional Data Analysis approach applied to cardiac magnetic resonance scans significantly improves the quantitative discrimination of left ventricular hypertrophy compared to traditional volumetric parameters like ejection fraction.
Hypothesis-generating for functional data analysis in LVH discrimination on CMR; leaves open prospective validation before clinical adoption.
Left ventricular remodeling is a mechanism common to various cardiovascular diseases affecting myocardial morphology. It can be often overlooked in clinical practice since the parameters routinely employed in the diagnostic process (e.g., the ejection fraction) mainly focus on evaluating volumetric aspects. Nevertheless, the integration of a quantitative assessment of structural modifications can be pivotal in the early individuation of this pathology. In this work, we propose an approach based on functional data analysis to evaluate myocardial contractility. A functional representation of ventricular shape is introduced, and functional principal component analysis and depth measures are used to discriminate healthy subjects from those affected by left ventricular hypertrophy. Our approach enables the integration of higher informative content compared to the traditional clinical parameters, allowing for a synthetic representation of morphological changes in the myocardium, which could be further explored and considered for future clinical practice implementation.
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Clementi et al. (2021) studied Left ventricular hypertrophy (n=21). Functional Data Analysis (fPCA scores) vs. Traditional clinical parameters (EF and ESV) was evaluated on Discrimination accuracy. A functional data analysis approach using functional principal component analysis scores achieved a discrimination accuracy of 98.2% between healthy and hypertrophic subjects, outperforming traditional clinical parameters which reached 65.96%.
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