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(Hypertension. 1997;30:788-795.)
© 1997 American Heart Association, Inc.
Articles |
Can Transmitral Doppler E-Waves Differentiate Hypertensive Hearts From Normal?
From the Cardiovascular Biophysics Laboratory, Cardiovascular Division, Barnes-Jewish Hospital at Washington University Medical Center, St Louis, Mo.
Correspondence to Dr Sándor J. Kovács, Cardiovascular Biophysics Laboratory, Cardiovascular Division, Barnes-Jewish Hospital at Washington University Medical Center, 216 South Kingshighway Blvd, St Louis, MO 63110. E-mail sjk{at}howdy.wustl.edu
Abstract Physiological models of
transmitral flow predict E-wave contour alteration in response to
variation of model parameters (stiffness, relaxation, mass)
reflecting the physiology of hypertension. Accordingly,
analysis of only the E-wave (rather than the E-to-A ratio)
should be able to differentiate between hypertensive subjects and
control subjects. Conventional versus model-based image processing
methods have never been compared in their ability to differentiate
E-waves of hypertensive subjects with respect to age-matched control
subjects. Digitally acquired transmitral Doppler flow images were
analyzed by an automated model-based image processing method.
Model-derived indexes were compared with conventional E-wave indexes in
22 subjects: 11 with hypertension and
echocardiographically verified ventricular
hypertrophy and 11 age-matched nonhypertensive control
subjects. Conventional E-wave indexes included peak E, 
E, and
acceleration and deceleration times. Model-based image
processing–derived indexes included acceleration and deceleration
times, potential energy index, and damping and kinematic constants.
Intergroup comparison yielded lower probability values for
model-based compared with conventional indexes. In the subjects
studied, Doppler E-wave images analyzed by this automated
method (which eliminates the need for hand-digitizing contours or the
manual placement of cursors) demonstrate diastolic function
alteration secondary to hypertension made discernible by model-based
indexes. The method uses the entire E-wave contour, quantitatively
differentiates between hypertensive subjects and control subjects, and
has potential for automated noninvasive diastolic function
evaluation in large patient populations, such as hypertension and other
transmitral flow velocity–altering
pathophysiological states.
Key Words: diastole • models, physiological • ultrasonics • hypertension, model-based image processing
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