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Usefulness of Conventional and Tissue Doppler Echocardiography to Predict Congestive Heart Failure in Dogs with Myxomatous Mitral Valve Disease
Authors:J‐H Kim  H‐M Park
Institution:Department of Veterinary Internal Medicine, College of Veterinary Medicine, Konkuk University, Seoul, South Korea
Abstract:

Background

Systolic and diastolic functions have been evaluated to predict outcome in congestive heart failure (CHF). Recently, tissue Doppler imaging (TDI) has become useful for the estimation of myocardial function in cardiac diseases of humans and animals.

Objective

This study was designed to assess whether myocardial function as assessed by TDI is associated with the occurrence of CHF in dogs with myxomatous mitral valve disease (MMVD) and whether additional information is gained over conventional Doppler variables.

Animals

Forty‐one privately owned dogs (15 healthy dogs and 26 dogs with MMVD) were included. Dogs with MMVD were divided into non‐CHF (n = 10) and CHF groups (n = 16).

Methods

Conventional echocardiographic examinations were performed. In addition, TDI‐derived variables, including radial and longitudinal velocities, strain, and strain rate were assessed.

Results

Several (12 of 47, 26%) conventional and tissue Doppler echocardiography variables were significant predictors of CHF in a univariate analysis (P < .05). However, TDI‐derived E/E m sept was the only load‐independent significant predictor of CHF (P < .05) after multivariate logistic regression analysis. The E/E m sept cut‐off value of >18.7 had a sensitivity of 56% and specificity of 90% in predicting CHF in dogs with MMVD.

Conclusions and Clinical Importance

The combination of TDI of the mitral annulus and mitral inflow velocity provided better estimates of diastolic dysfunction in dogs with MMVD and CHF. Additional study is warranted to assess TDI‐derived E/E m sept, an index of diastolic function that could contribute to the management of dogs with MMVD and CHF.
Keywords:Acquired valvular disease  Canine  E/Em  Tissue Doppler imaging
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