Phenotypic Clustering for Assessing Diastolic Dysfunction
What is the utility of cluster analysis of left atrial and left ventricular (LV) mechanical deformation parameters for Doppler-independent assessment of LV diastolic function?
The investigators performed a cluster analysis and developed a model of LV diastolic function from an initial exploratory cohort of 130 patients that was subsequently tested in a prospective cohort of 44 patients undergoing cardiac catheterization. Patients in both study groups had standard echocardiographic examination with Doppler-derived assessment of diastolic function. Both the left ventricle and the left atrium were tracked simultaneously using speckle-tracking echocardiography (STE) for measuring simultaneous changes in left atrial and ventricular volumes, volume rates, longitudinal strains, and strain rates. Patients in the validation group also underwent invasive measurements of pulmonary capillary wedge pressure and LV end-diastolic pressure immediately after echocardiography. The similarity between STE and conventional two-dimensional (2D) and Doppler methods of diastolic function was investigated in both the exploratory and validation cohorts.
STE demonstrated strong correlations with the conventional indices and independently clustered the patients into three groups with conventional measurements verifying increasing severity of diastolic dysfunction and LV filling pressures. A multivariable linear regression model also allowed estimation of E/e' and pulmonary capillary wedge pressure by STE in the validation cohort.
The authors concluded that tracking deformation of the left-sided cardiac chambers from routine cardiac ultrasound images provides accurate information for Doppler-independent phenotypic characterization of LV diastolic function.
This study reports a high statistical level of overlap between STE-derived data and conventional echocardiographic methods of diastolic function assessment. Furthermore, clustering of the patients based on STE data into three different groups corresponded to worsening severity of diastolic dysfunction grades, as verified by the conventional parameters, and a linear multivariable model from Doppler-independent STE data demonstrated good diagnostic accuracy in predicting LV filling pressures. These findings appear to suggest that the information content of STE variables corresponds to that derived from 2D and Doppler-based analysis, and can provide an independent assessment of diastolic function and LV filling pressures. The ability of machine learning models capable of capturing data for automated analyses has the potential of increasing diagnostic throughput and efficiency.
Keywords: Blood Pressure, Cardiac Catheterization, Diagnostic Imaging, Diastole, Echocardiography, Heart Atria, Heart Failure, Heart Ventricles, Pulmonary Wedge Pressure, Ventricular Dysfunction, Left
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