Original ArticleOptimization and validation of radionuclide angiography phase analysis parameters for quantification of mechanical dyssynchrony
Introduction
Cardiac resynchronization therapy (CRT) has emerged as a new treatment for a subset of patients with congestive heart failure (CHF) and mechanical dyssynchrony. CRT has been shown to improve survival and reduce heart failure hospitalization and symptoms.1,2 However, up to 50% of apparently suitable patients do not obtain objective benefit.3,4 There are three main theories as to why some patients do not respond to this therapy: inadequate mechanical dyssynchrony,5,6 inappropriate LV lead placement,7, 8, 9 and excessive scar burden.10,11 Techniques are being developed to quantify mechanical dyssynchrony, including tissue Doppler imaging (TDI), MRI, and phase analysis of both radionuclide angiography (RNA) myocardial perfusion imaging (MPI).
A significant amount of research has focused on echocardiographic assessment of mechanical dyssynchrony, using TDI, with promising early single-centre data.12 Despite advances in TDI, recent results have shown low sensitivity and specificity values for detection of response to CRT varying between 0.5 and 0.7, and intra- and inter-observer variabilities of between 4% and 70%.13 Early results using phase analysis of MPI shows promising results that phase analysis based measures of dyssynchrony can predict CRT response with a 74% sensitivity and specificity.14 However, further large-scale, multi-centre studies are required to validate the methodology. Finally, MRI-based measures of dyssynchrony have also shown promise at predicting CRT outcome, but the techniques can be time consuming and costly.15,16 RNA phase analysis, a simple and widely available technique, may still play a role in quantifying mechanical dyssynchrony and predicting CRT outcome. However, further investigation is needed to optimize RNA techniques.
Phase analysis of radionuclide angiography (RNA) images, in which each gated-pixel in the images is fit to the first Fourier harmonic, has been investigated for its use in measuring cardiac dyssynchrony and LV pacing.17 Several studies have shown that the standard deviation of the phase values (phaseSD) can be used to assess mechanical dyssynchrony18, 19, 20; however, other studies have found that phaseSD could not differentiate between various types of mechanical dyssynchrony.19 In response to this, two newer parameters, synchrony (S) and entropy (E), have been proposed and reported to provide good distinction between various forms of mechanical dyssynchrony in a computer simulation.19 A full characterization and optimization of these variables, using patient data and including intra- and inter-observer reproducibility, has not yet been performed.
The aim of this paper was to provide a characterization of, and to optimize the previously defined phase analysis parameters (phaseSD, S, and E) at detecting mechanical dyssynchrony using ROC analysis.
Section snippets
Patient Population
A database of normal subjects and patients with known mechanical dyssynchrony was prospectively included in this study. Normal subjects were referred to our clinic for assessment of LV function. Normal LV function was defined as LVEF > 50%, EDV < 130 mL, ESV < 60 mL, QRS duration < 120 ms, and normal wall motion, as determined by an expert observer. The mechanical dyssynchrony subjects were defined as LVEF < 30%, QRS duration > 130 ms, and dyssynchronous wall motion as determined by an expert
Patient Population
A total of 37 normal subjects were included in this study. Fifty-three cardiomyopathy patients with known cardiac mechanical dyssynchrony were also included. Of the 53 mechanical dyssynchrony subjects, 45 exhibited typical LBBB, 1 with RBBB, and 8 with non-specific intraventricular conduction defect. The patient demographics are tabulated in Table 1.
Optimization of RNA Phase Analysis
Figure 3A-C plots the ROC area versus amplitude threshold value for the three parameters (phaseSD, S, and E) and using the three filters. For all
Discussion
Compared to echo, little work has been done validating and improving RNA phase analysis for quantification of mechanical dyssynchrony. Port22 recently voiced this in an editorial: “…when there was suddenly a clinical calling for the quantitative analysis of variation in the timing of contraction in and between ventricles, the majority of the nuclear community was caught sleeping, and the echocardiographers advanced tissue Doppler imaging as a tool for selection of and subsequent assessment of
Limitations and Future Directions
One of the limitations of this work is the use of the same cohort of subjects to both optimize the parameters and then determine the sensitivity and specificity of the method at detecting mechanical dyssynchrony (Table 2). This has the potential to positively bias our sensitivity and specificity results. However, given the high values for the AUC, we do not expect this bias to be large and would expect similar results from a separate cohort. In addition, the ability to detect mechanical
Conclusion
Planar RNA phase analysis parameters (phaseSD, S, and E) were optimized for detecting mechanical dyssynchrony. phaseSD, S and E displayed excellent intra- and inter-observer variability with correlation coefficients >0.99. Studies assessing the ability of these parameters to predict CRT outcome are required.
Acknowledgments
The authors would like to thank Mary Dalipaj and Brian Marvin for their assistance in collecting the data. The authors would also like to thank Benoit Galarneau and Hermes Medical Solutions for assistance in developing the phase analysis program.
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