Principle component analysis of myocardial strain to quantify left ventricular dyssynchrony

Raghav Ramachandran,Bhairav B Mehta,Frederick H Epstein,Xiao Chen,Kenneth C Bilchick

doi:10.1186/1532-429x-15-s1-p74

Abstract

Background Cardiac resynchronization therapy (CRT) is effective for selected heart failure (HF) patients, but is associated with a 30-40% nonresponse rate. Identification of CRT responders may be improved with myocardial strain imaging. The circumferential uniformity ratio estimate (CURE ) measures mechanical dyssynchrony by Fourier series fitting of myocardial strains over space, but requires user interaction to define a range of cardiac phases over which CURE is calculated (time dependence). We hypothesize that principal component analysis (PCA) can quantify dyssynchrony in myocardial strain in a data-driven, time-independent manner that does not require any subjective user assessments of strain data.

Highlights

Cardiac resynchronization therapy (CRT) is effective for selected heart failure (HF) patients, but is associated with a 30-40% nonresponse rate
The circumferential uniformity ratio estimate (CURE )[1] measures mechanical dyssynchrony by Fourier series fitting of myocardial strains over space, but requires user interaction to define a range of cardiac phases over which CURE is calculated
Dyssynchronous HF was induced in canines (N=5) with tachycardia pacing and left bundle branch ablation (LBBB-HF), while synchronous HF with narrow QRS (NQRS-HF) was induced in canines with tachycardia pacing only (N=5)

Summary

Background

Cardiac resynchronization therapy (CRT) is effective for selected heart failure (HF) patients, but is associated with a 30-40% nonresponse rate. Identification of CRT responders may be improved with myocardial strain imaging. The circumferential uniformity ratio estimate (CURE )[1] measures mechanical dyssynchrony by Fourier series fitting of myocardial strains over space, but requires user interaction to define a range of cardiac phases over which CURE is calculated (time dependence). We hypothesize that principal component analysis (PCA) can quantify dyssynchrony in myocardial strain in a data-driven, time-independent manner that does not require any subjective user assessments of strain data

Methods

Results

Conclusions