Bayesian parameter estimation for characterizing the cyclic variation of
echocardiographic backscatter to assess the hearts of asymptomatic type 2
diabetes mellitus subjects.
Authors Anderson CC, Gibson AA, Schaffer JE, Peterson LR, Holland MR, Miller JG
Submitted By Submitted Externally on 4/15/2014
Status Published
Journal Ultrasound in medicine & biology
Year 2011
Date Published 5/1/2011
Volume : Pages 37 : 805 - 812
PubMed Reference 21439721
Abstract Previous studies have shown that effective quantification of the cyclic
variation of myocardial ultrasonic backscatter over the heart cycle might
provide a non-invasive technique for identifying the early onset of cardiac
abnormalities. These studies have demonstrated the potential for measurements of
the magnitude and time delay of cyclic variation for identifying early onset of
disease. The goal of this study was to extend this approach by extracting
additional parameters characterizing the cyclic variation in an effort to better
assess subtle changes in myocardial properties in asymptomatic subjects with
type 2 diabetes. Echocardiographic images were obtained on a total of 43
age-matched normal control subjects and 100 type 2 diabetics. Cyclic variation
data were generated by measuring the average level of ultrasonic backscatter
over the heart cycle within a region of interest placed in the posterior wall of
the left ventricle. Cyclic variation waveforms were modeled as piecewise linear
functions, and quantified using a novel Bayesian parameter estimation method.
Magnitude, rise time and slew rate parameters were extracted from models of the
data. The ability of each of these parameters to distinguish between normal and
type 2 diabetic subjects, and between subjects grouped by glycated hemoglobin
(HbA1c) was compared. Results suggest a significant improvement in using
measurements of the rise time and slew rate parameters of cyclic variation to
differentiate (P < 0.001) the hearts of patients segregated based on widely
employed indices of diabetic control compared to differentiation based on the
magnitude of cyclic variation.

Investigators with authorship
Jean SchafferWashington University in St Louis