Improved Acoustic Detection of Coronary Blockages

Sonomedica, Llc, Mc Lean VA

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): Heart disease is the leading cause of death for both women and men in the industrial world and despite the fact that early detection of CAD allows for successful and cost effective treatment of the disease, only 20% of CAD cases are diagnosed prior to a heart attack. Based on principles pioneered by the PI, SonoMedica Inc. has developed a device that can non-invasively detect CAD in both its early (30% to 50% occlusive) and later stages (50% to 85% occlusive) by identifying the acoustic signatures associated with turbulent blood flow through partially occluded coronary arteries. No other approach to the detection of CAD promises to be as inexpensive, simple to perform, and risk free as the acoustic approach. The benefits to society of this device include wellness testing (screening) for asymptomatic individuals as well as longitudinal tracking of symptomatic patients The performance of the current SonoMedica device should make it a useful adjunct to evaluating patients with potential stenosis; however, a new signal processing approach developed by the PI indicates that current performance can be substantially improved. This approach is based on a global, or multivariate, analysis of the data: the approach searches for a general structure, or information, within the acoustic data set. If the acoustic data set contains only noise, it will be largely unstructured and have gaussian properties, since the noise will be the result of multiple sources unrelated to the coronary cycle. Conversely, signals associated turbulence will add structure to the data set so a measurement based on data structure could be used to identify such signals. The objective milestone of Phase I research is to improve acoustic-based detection of CAD to the level where this approach will be of unquestionable clinical value. Specifically, a successful outcome of Phase I will be the ability to detect blockages of 30% to 85% with a sensitivity greater than > 90 % and a specificity greater than 80% in a retrospective data analysis.