Evaluation of the Left Ventricular Diastolic Function Using Three-Dimensional Echocardiography

Assessment of left ventricular diastolic function by three-dimensional transthoracic echocardiography

Doppler echocardiography assessment of left ventricular (LV) filling pressures at rest and during exercise is the most widely used imaging technique to assess LV diastolic function in clinical practice. However, a sizable number of patients evaluated for suspected LV diastolic function show an inconsistency between the various parameters included in the flowchart recommended by current Doppler echocardiography guidelines and results in an undetermined LV diastolic function. Current three-dimensional echocardiography technology allows obtaining accurate measurements of the left atrial volumes and functions that have been shown to improve the diagnostic accuracy and prognostic value of the algorithms recommended for assessing both LV diastolic dysfunction and heart failure with preserved ejection fraction. Moreover, current software packages used to quantify LV size and function provide also volume-time curves showing the dynamic LV volume change throughout the cardiac cycle. Examining the diastolic part of these curves allows the measurement of several indices of LV filling that have been reported to be useful to differentiate patients with normal LV diastolic function from patients with different degrees of diastolic dysfunction. Finally, several software packages allow to obtain also myocardial deformation parameters from the three-dimensional datasets of both the left atrium and the LV providing additional functional parameters that may be useful to improve the diagnostic yield of three-dimensional echocardiography for the LV diastolic dysfunction. This review summarizes the current applications of three-dimensional echocardiography to assess LV diastolic function.

Machine learning based quantification of ejection and filling parameters by fully automated dynamic measurement of left ventricular volumes from cardiac magnetic resonance images

BACKGROUND

Although analysis of cardiac magnetic resonance (CMR) images provides accurate and reproducible measurements of left ventricular (LV) volumes, these measurements are usually not performed throughout the cardiac cycle because of lack of tools that would allow such analysis within a reasonable timeframe. A fully-automated machine-learning (ML) algorithm was recently developed to automatically generate LV volume-time curves. Our aim was to validate ejection and filling parameters calculated from these curves using conventional analysis as a reference.

METHODS

We studied 21 patients undergoing clinical CMR examinations. LV volume-time curves were obtained using the ML-based algorithm (Neosoft), and independently using slice-by-slice, frame-by-frame manual tracing of the endocardial boundaries. Ejection and filling parameters derived from these curves were compared between the two techniques. For each parameter, Bland-Altman bias and limits of agreement (LOA) were expressed in percent of the mean measured value.

RESULTS

Time-volume curves were generated using the automated ML analysis within 2.5 ± 0.5 min, considerably faster than the manual analysis (43 ± 14 min per patient, including ~10 slices with 25-32 frames per slice). Time-volume curves were similar between the two techniques in magnitude and shape. Size and function parameters extracted from these curves showed no significant inter-technique differences, reflected by high correlations, small biases (<10%) and mostly reasonably narrow LOA.

CONCLUSION

ML software for dynamic LV volume measurement allows fast and accurate, fully automated analysis of ejection and filling parameters, compared to manual tracing based analysis. The ability to quickly evaluate time-volume curves is important for a more comprehensive evaluation of the patient's cardiac function.

Machine learning based automated dynamic quantification of left heart chamber volumes

AIMS

Studies have demonstrated the ability of a new automated algorithm for volumetric analysis of 3D echocardiographic (3DE) datasets to provide accurate and reproducible measurements of left ventricular and left atrial (LV, LA) volumes at end-systole and end-diastole. Recently, this methodology was expanded using a machine learning (ML) approach to automatically measure chamber volumes throughout the cardiac cycle, resulting in LV and LA volume-time curves. We aimed to validate ejection and filling parameters obtained from these curves by comparing them to independent well-validated reference techniques.

METHODS AND RESULTS

We studied 20 patients referred for cardiac magnetic resonance (CMR) examinations, who underwent 3DE imaging the same day. Volume-time curves were obtained for both LV and LA chambers using the ML algorithm (Philips HeartModel), and independently conventional 3DE volumetric analysis (TomTec), and CMR images (slice-by-slice, frame-by-frame manual tracing). Automatically derived LV and LA volumes and ejection/filling parameters were compared against both reference techniques. Minor manual correction of the automatically detected LV and LA borders was needed in 4/20 and 5/20 cases, respectively. Time required to generate volume-time curves was 35 ± 17 s using ML algorithm, 3.6 ± 0.9 min using conventional 3DE analysis, and 96 ± 14 min using CMR. Volume-time curves obtained by all three techniques were similar in shape and magnitude. In both comparisons, ejection/filling parameters showed no significant inter-technique differences. Bland-Altman analysis confirmed small biases, despite wide limits of agreement.

CONCLUSION

The automated ML algorithm can quickly measure dynamic LV and LA volumes and accurately analyse ejection/filling parameters. Incorporation of this algorithm into the clinical workflow may increase the utilization of 3DE imaging.

[Relationship between myocardial modelling and diastolic function in patients with essential hypertension]

BACKGROUND AND OBJECTIVES

To analyze the relationship between sFas and soluble TNF receptor 1 (sTNF-R1) with type iii (PIIINP) and i (PINP) amino-terminal propeptide procollagens, and diastole in hypertension (HT).

PATIENTS AND METHODS

A group of 253 Caucasian asymptomatic hypertensive patients (age 60±13 years, 139 males) were studied, in whom a physical examination, laboratory analyses (determination of serum PIIINP, PINP, sFas and by radioimmunoassay and ELISA, respectively), and echo-Doppler study were performed.

RESULTS

Serum PINP and PIIINP were increased in left ventricular hypertrophy compared to non-hypertrophy [41 (31-52) vs. 35 (28-47) μg/l, P=.010; and 4.33 (3.71-5.29) vs. 3.98 (3.49-4.58) μg/l, P=.005, respectively]. Furthermore, sFas and sTNF-R1 were also elevated [1.47 (1.2-1.77) vs. 1.37 (1.1-1.59), P=.012; and 466 (331-657) vs. 317 (260-427) μg/l, P<.0001, respectively]. Moreover, serum PIIINP was associated with sFas (r=.386, P<.0001) and sTNF-R1 (r=.298, P<.001); PINP was also associated with these cytokines (r=0.158, P=.011 and r=.241, P<.0001, respectively). Multivariable analyses included sFas (P<.0001) and sTNF-R1 (P<.0001) as independent factors related with serum PIIINP. Finally, marker concentrations were significantly related with left ventricular diastolic function parameters.

CONCLUSION

Procollagen and anti-apoptotic cytokine levels were increased in our hypertrophic patients. Furthermore, sFas and sTNF-R1 are independent related factors of serum PIIINP. Diastolic parameters were associated with myocardial fibrosis and anti-apoptotic cytokines.