Journal of Nuclear Cardiology

, Volume 24, Issue 1, pp 158–161 | Cite as

Measuring mechanical cardiac dyssynchrony in the 3-D era

Editorial
  • 498 Downloads

The use of dobutamine in heart failure (HF) patients can be connected to either a therapeutic or a diagnostic strategy. When dilated cardiomyopathy patients with acute decompensated heart failure (ADHF) and low left ventricular ejection fraction (LVEF) are treated with inotropic agents in an hospital setting, the main goal is that of improving cardiac function and relieving symptoms until definitive therapy (coronary revascularization, heart transplantation, etc.) is performed, or the acute precipitating problem has resolved.1-3 While there are concerns that inotropic agents may adversely affect mortality and clinical outcomes,4,5 dobutamine is nevertheless widely used in ADHF patients with low LVEF and is effective in inducing improvements in both myocardial contractility and vascular endothelial function,6 with the effects sometime lasting up to a month or longer and inspiring the term “dobutamine holiday.”7

From a diagnostic perspective, dobutamine stress testing has been employed to identify potential cardiac resynchronization therapy (CRT) responders. Logically speaking, CRT can be expected to be effective only if dyssynchrony exists, either at rest or at stress, and dobutamine stress may help bring about regional differences in myocardial contractility. Moreover, a dobutamine-induced variation in dyssynchrony, whether positive or negative, may bear a greater relationship to clinical outcomes than rest dyssynchrony, similarly to what has been reported for exercise stress.8 Of note, dyssynchrony may compensate the typical stress-induced increase in contractile reserve (CR), resulting in a paradoxical lack of increase in ejection fraction in spite of greater myocardial contractility.9

While the standard for referral to CRT is still a wide (>120 ms) QRS complex,10 it has been reported that one-third of heart failure patients with a wide QRS do not have mechanical dyssynchrony, while 40-50% of those without a wide QRS do,11 these findings probably representing a major reason why about 30-40% of patients referred to CRT do not benefit from it.12 If dyssynchrony is a better predictor of response to CRT than QRS width, as well as possibly better correlated to adverse outcomes—for example, dyssynchrony that develops under exercise stress has been reported to be associated with a higher frequency of adverse cardiac outcomes in patients with dilated cardiomyopathy (DCM) and narrow QRS complex13—it would appear important to expend the effort to measure cardiac dyssynchrony in the most accurate and precise (reproducible) manner.

Cardiac dyssynchrony has been typically measured using echocardiographic techniques, which are mostly two-dimensional and not as standardized as one might want, in terms of both study performance and image analysis. Indeed, the Predictors of Response to CRT (PROSPECT) multicenter trial, whose results suggested that no individual echocardiographic measure of mechanical cardiac dyssynchrony could predict response to CRT with good sensitivity and specificity, suffered from unacceptably high inter-observer variabilities (32-72%) and intra-observer variabilities (16-24%).14 The implied message here is perhaps that a three-dimensional, standardized and automated imaging modality is the most natural and suitable choice to measure a “difficult” parameter such as dyssynchrony, which ought to retain an essential role in the evaluation of the prospective CRT patient.8

Cardiac Magnetic Resonance Imaging (cMRI) allows high-resolution tracking of myocardial surfaces in the 3-D space (albeit via non-isotropic voxels), using advanced techniques such as myocardial tagging, harmonic phase analysis, and strain encoding.15 While promising, cMRI suffers from high equipment cost, complexity of image acquisition, and lack of fully automated algorithms for image quantification, leading to its narrowly spread application to the measurement of mechanical cardiac dyssynchrony. Similarly, multi-detector computed tomography (MDCT), although capable of acquiring high-resolution images of the entire heart in the time of a breath-hold, is challenged both by a still limited temporal resolution and by the lack of standardized and automated analysis tools.16

In historical and practical terms, the main alternatives to echocardiography for the assessment of mechanical dyssynchrony have been nuclear cardiology techniques, with the planar radionuclide ventriculography (PRNV) approach originally described in 1980,17 eventually evolving into gated blood pool SPECT (GBPS) to better quantify the 3-D heart.18 Both PRNV and GBPS can measure mechanical dyssynchrony associated with endocardial motion by analyzing the variation of counts within, or at the periphery of, a region of interest over the cardiac cycle. Accordingly, the paper by Salimian et al. in this issue of the Journal19 uses a previously described and validated GBPS algorithm to measure intra- and inter-ventricular dyssynchrony, as a function of varying degrees of dobutamine stress, in eight dogs with tachycardia-induced dilated cardiomyopathy (DCM). With the caveats that coronary artery disease (CAD) rather than DCM causes systolic heart failure in most cases in the United States,20 and that induced DCM in a canine model is not by its nature equivalent to chronic HF in humans, this study contributes to the growing body of literature seeking to clarify the relationship between mechanical cardiac dyssynchrony and heart failure.

Of interest, gated myocardial perfusion imaging (MPI) can also be used to measure dyssynchrony. Because of the high prevalence of clinical use of MPI, which accounts for perhaps 10 times as many studies as GBPS in the USA, automated and highly reproducible algorithms for the quantification of MPI motion and thickening have been developed, validated, and widely used for the past 20 years,21 and phase analysis of the onset of MPI-derived regional left ventricular (LV) contraction has been proposed over 10 years ago for the specific purpose of assessing dyssynchrony.22 As demonstrated in Figure 1, regional LV motion as well as its phase histogram are substantially equivalent when measured by GBPS and MPI in the same patient. Partial volume effect, which is supposedly less of a problem for GBPS due to the larger dimensions of the blood pool compared to the myocardium, is actually used in MPI as a tool to more accurately estimate myocardial thickening through image “brightening.”21
Figure 1

Wall motion phase analysis of a 59-year-old male patient with LAD disease and an apical LV aneurysm. Top row GBPS study, processed with an automated algorithm.25 Bottom row resting MPI study of the same patient, acquired 4 months later and processed with an automated algorithm21 modified to perform endocardial motion phase analysis. From left to right End-diastolic (ED) contours, end-systolic (ES) contours, wall motion phase polar map, and wall motion phase histogram. Both BPGS and MPI allow the visualization and quantification of apical dyskinesia, as demonstrated by the similarity of the phase polar maps and histograms, despite the large apical perfusion defect evident in the MPI data

While this is obviously not a concern in DCM patients, GBPS is often thought to offer increased robustness in the presence of large perfusion defects; however, a simulation study has demonstrated the accuracy of MPI phase analysis in ventricles where defect uptake is as low as 10% of that in normal regions,23 as also shown in Figure 1. Perhaps more importantly, myocardial thickening may better assess the dyssynchrony of myocardial contraction by being less susceptible to tethering effects of defect areas24—indeed, in some MPI quantitative algorithms, endocardial motion is defined as a combination of mid-myocardial wall motion and myocardial thickening.21

A potential advantage of GBPS over MPI is its ability to assess both LV and right ventricular (RV) motion, and consequently inter-ventricular dyssynchrony. Using phase analysis, inter-ventricular dyssynchrony is measured by evaluating the delay between the LV and RV onset of contraction, and can be calculated as the difference between the means of the LV and RV phase histograms, or through cross-correlation of LV and RV time-activity curves approach.19 This advantage may become less relevant in the future, as the increasing use of PET MPI and high-resolution, dedicated cardiac SPECT hardware and reconstruction algorithms allow us to better visualize and quantify RV function, as shown in Figure 2.
Figure 2

LV and RV segmentation of F-18 flurpiridaz PET MPI images. The high resolution of PET (compared to SPECT) along with the favorable imaging characteristics of F-18 allow for clear delineation of both LV and RV

Notes

Disclosure

The authors have no conflict of interest to disclose with respect to this editorial.

References

  1. 1.
    Lindenfeld J, Albert NM, Boehmer JP, Collins SP, Ezekowitz JA, Givertz MM, et al. Executive summary: HFSA 2010 comprehensive heart failure practice guideline. J Cardiac Fail 2010;16:475-539.CrossRefGoogle Scholar
  2. 2.
    McMurray JJV, Adamopoulos S, Anker SD, Auricchio A, Boehm M, Dickstein K, et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012 The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail 2012;14:803-69.CrossRefPubMedGoogle Scholar
  3. 3.
    Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr, Drazner MH, et al. 2013 ACCF/AHA guideline for the management of heart failure: Executive summary a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation 2013;128:1810-52.CrossRefPubMedGoogle Scholar
  4. 4.
    Cuffe MS, Califf RM, Adams KF, Benza R, Bourge R, Colucci WS, et al. Short-term intravenous milrinone for acute exacerbation of chronic heart failure—A randomized controlled trial. JAMA 2002;287:1541-7.CrossRefPubMedGoogle Scholar
  5. 5.
    Abraham WT, Adams KF, Fonarow GC, Costanzo MR, Berkowitz RL, LeJemtel TH, et al. In-hospital mortality in patients with acute decompensated heart failure requiring intravenous vasoactive medications—An analysis from the Acute Decompensated Heart Failure National Registry (ADHERE). J Am Coll Cardiol 2005;46:57-64.CrossRefPubMedGoogle Scholar
  6. 6.
    Patel MB, Kaplan IV, Patni RN, Levy D, Strom JA, Shirani J, et al. Sustained improvement in flow-mediated vasodilation after short-term administration of dobutamine in patients with severe congestive heart failure. Circulation 1999;99:60-4.CrossRefPubMedGoogle Scholar
  7. 7.
    Liang CS, Sherman LG, Doherty JU, Wellington K, Lee VW, Hood WB. Sustained improvement of cardiac-function in patients with congestive heart-failure after short-term infusion of dobutamine. Circulation 1984;69:113-9.CrossRefPubMedGoogle Scholar
  8. 8.
    Zhang Q, Yu CM. Is mechanical dyssynchrony still a major determinant for responses after cardiac resynchronization therapy? J Cardiol 2011;57:239-48.CrossRefPubMedGoogle Scholar
  9. 9.
    Stankovic I, Aarones M, Smith H-J, Voeroes G, Kongsgaard E, Neskovic AN, et al. Dynamic relationship of left-ventricular dyssynchrony and contractile reserve in patients undergoing cardiac resynchronization therapy. Eur Heart J 2014;35:48.CrossRefPubMedGoogle Scholar
  10. 10.
    Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, et al. 2009 focused update incorporated into the ACC/AHA 2005 guidelines for the diagnosis and management of heart failure in adults a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation 2009;119:E391-479.CrossRefPubMedGoogle Scholar
  11. 11.
    Yu CM, Lin H, Zhang Q, Sanderson JE. High prevalence of left ventricular systolic and diastolic asynchrony in patients with congestive heart failure and normal QRS duration. Heart 2003;89:54-60.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Birnie DH, Tang ASL. The problem of non-response to cardiac resynchronization therapy. Curr Opin Cardiol 2006;21:20-6.CrossRefPubMedGoogle Scholar
  13. 13.
    D’Andrea A, Mele D, Nistri S, Riegler L, Galderisi M, Agricola E, et al. The prognostic impact of dynamic ventricular dyssynchrony in patients with idiopathic dilated cardiomyopathy and narrow QRS. Eur Heart J Cardiovasc Imaging 2013;14:183-9.CrossRefPubMedGoogle Scholar
  14. 14.
    Chung ES, Leon AR, Tavazzi L, Sun J-P, Nihoyannopoulos P, Merlino J, et al. Results of the predictors of response to CRT (PROSPECT) trial. Circulation 2008;117:2608-16.CrossRefPubMedGoogle Scholar
  15. 15.
    Lardo AC, Abraham TP, Kass DA. Magnetic resonance imaging assessment of ventricular dyssynchrony—Current and emerging concepts. J Am Coll Cardiol 2005;46:2223-8.CrossRefPubMedGoogle Scholar
  16. 16.
    Mangalat D, Kalogeropoulos A, Georgiopoulou V, Stillman A, Butler J. Value of cardiac CT in patients with heart failure. Curr Cardiovasc Imaging Rep 2009;2:410-7.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Links JM, Douglass KH, Wagner HN Jr. Patterns of ventricular emptying by Fourier analysis of gated blood-pool studies. J Nucl Med 1980;21:978-82.PubMedGoogle Scholar
  18. 18.
    Lalonde M, Birnie D, Ruddy TD, deKemp RA, Wassenaar RW. SPECT blood pool phase analysis can accurately and reproducibly quantify mechanical dyssynchrony. J Nucl Cardiol 2010;17:803-10.CrossRefPubMedGoogle Scholar
  19. 19.
    Salimian S, Thibauld B, Finnerty V, Gregoire J, Harel F. Phase analysis of gated blood pool SPECT for multiple stress testing assessments of ventricular mechanical dyssynchrony in a tachycardia-induced dilated cardiomyopathy canine model. J Nucl Cardiol 2016 (in press).Google Scholar
  20. 20.
    He J, Ogden LG, Bazzano LA, Vupputuri S, Loria C, Whelton PK. Risk factors for congestive heart failure in US men and women—NHANES I epidemiologic follow-up study. Arch Intern Med 2001;161:996-1002.CrossRefPubMedGoogle Scholar
  21. 21.
    Germano G, Kiat H, Kavanagh PB, Moriel M, Mazzanti M, Su HT, et al. Automatic quantification of ejection fraction from gated myocardial perfusion SPECT. J Nucl Med 1995;36:2138-47.PubMedGoogle Scholar
  22. 22.
    Chen J, Garcia EV, Folks RD, Cooke CD, Faber TL, Tauxe L, et al. Onset of left ventricular mechanical contraction as determined by phase analysis of ECG-gated myocardial perfusion SPECT imaging: Development of a diagnostic tool for assessment of cardiac mechanical dyssynchrony. J Nucl Cardiol 2005;12:687-95.CrossRefPubMedGoogle Scholar
  23. 23.
    Cheung A, Zhou Y, Faber TL, Garcia EV, Zhu L, Chen J. The performance of phase analysis of gated SPECT myocardial perfusion imaging in the presence of perfusion defects: A simulation study. J Nucl Cardiol 2012;19:500-6.CrossRefPubMedGoogle Scholar
  24. 24.
    Kass DA. An epidemic of dyssynchrony—But what does it mean? J Am Coll Cardiol 2008;51:12-7.CrossRefPubMedGoogle Scholar
  25. 25.
    Van Kriekinge S, Berman D, Germano G. Quantitative gated blood pool SPECT. In: Germano G, Berman D, editors. Clinical gated cardiac SPECT. 2nd ed. Oxford: Blackwell; 2006. p. 273-84.CrossRefGoogle Scholar

Copyright information

© American Society of Nuclear Cardiology 2015

Authors and Affiliations

  1. 1.Cedars-Sinai Medical CenterLos AngelesUSA
  2. 2.UCLADavid Geffen School of MedicineLos AngelesUSA

Personalised recommendations