Assessment of aortic and mitral annuli dynamics during the cardiac cycle using speckle tracking echocardiography

Diastolic flow in the left ventricular outflow tract - A normal physiological and underappreciated echocardiographic finding

PURPOSE

Diastolic waveforms in the left ventricular outflow tract (LVOT) are commonly observed with Doppler echocardiography. The incidence and mechanism are not well described.

METHODS

This was a retrospective observational study of 186 adult patients, athletes and non-athletes, free of known cardiac disease, presenting for comprehensive transthoracic echocardiography at a research institute. We aimed to evaluate the incidence and echocardiographic associations between LVOT diastolic waveforms.

RESULTS

Left ventricular outflow tract early to mid-diastolic waveforms were present in 100% of athletes and 95% of non-athletes. The LVOT diastolic velocity time integral was larger in athletes than non-athletes with a mean 8.3 cm (95% CI (7.6-8.9)) vs. 5.1 cm (4.4-5.9) (P < 0.0001). Multivariate predictors of this diastolic waveform were age (P = 0.002), slower heart rate (P = 0.035), higher stroke volume (P = 0.003), large mitral E (P = 0.019) and higher E/e' (P = 0.015).

DISCUSSION

An LVOT early diastolic wave is a normal physiological finding. It is related to a flow vortex redirecting diastolic mitral inflow around anterior mitral valve leaflet into the LVOT.

CONCLUSIONS

Early to mid-diastolic LVOT waves are present in almost all patients but more prominent in young athletes than non-athletes. Diastolic LVOT waves increase with younger age, slower heart rate, larger stroke volume and enhanced diastolic function.

Artificial intelligence in mitral valve analysis

Background:

Echocardiographic analysis of mitral valve (MV) has become essential for diagnosis and management of patients with MV disease. Currently, the various software used for MV analysis require manual input and are prone to interobserver variability in the measurements.

Aim:

The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis.

Settings and Design:

Retrospective analysis of intraoperative three-dimensional transesophageal echocardiography data acquired from four patients with normal MV undergoing coronary artery bypass graft surgery in a tertiary hospital.

Materials and Methods:

Echocardiographic data were analyzed using the eSie Valve Software (Siemens Healthcare, Mountain View, CA, USA). Three examiners analyzed three end-systolic (ES) frames from each of the four patients. A total of 36 ES frames were analyzed and included in the study.

Statistical Analysis:

A multiple mixed-effects ANOVA model was constructed to determine if the examiner, the patient, and the loop had a significant effect on the average value of each parameter. A Bonferroni correction was used to correct for multiple comparisons, and P = 0.0083 was considered to be significant.

Results:

Examiners did not have an effect on any of the six parameters tested. Patient and loop had an effect on the average parameter value for each of the six parameters as expected (P < 0.0083 for both).

Conclusion:

We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention.