Animal Models of Dyssynchrony
ABSTRACT Cardiac resynchronization therapy (CRT) is an important therapy for patients with heart failure and conduction pathology, but the benefits are heterogeneous between patients and approximately a third of patients do not show signs of clinical or echocardiographic response. This calls for a better understanding of the underlying conduction disease and resynchronization. In this review, we discuss to what extent established and novel animal models can help to better understand the pathophysiology of dyssynchrony and the benefits of CRT.
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Full-textDOI: · Available from: Marc Strik, May 30, 2015
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ABSTRACT: In cardiac resynchronization therapy (CRT), specific changes in motion/deformation happen with left-bundle-branch-block (LBBB) and following treatment. However, they remain sub-optimally studied. We propose a two-fold improvement of their characterization. This includes controlling them through an experimental model and using more suitable quantification techniques. We used a swine model of acute LBBB and CRT with/without chronic infarct (pure-LBBB: N = 11; LBBB + left-anterior-descending infarct: N = 11). Myocardial displacement, velocity and strain were extracted from short-axis echocardiographic sequences using 2D speckle-tracking. The data was transformed to a single spatiotemporal system of coordinates to perform subject comparisons and quantify pattern changes at similar locations and instants. Pure-LBBB animals showed a specific intra-ventricular dyssynchrony pattern with LBBB (11/11 animals), and the recovery towards a normal pattern with CRT (10/11 animals). Pattern variability was low within the pure-LBBB population, as quantified by our method. This was not correctly assessed by more conventional measurements. Infarct presence affected the pattern distribution and CRT efficiency (improvements in 6/11 animals). Pattern changes correlated with global cardiac function (global circumferential strain) changes in all the animals (corrected: pLBBBvsBaseline < 0.001, pCRTvsBaseline = NS; non-corrected: pLBBBvsBaseline = NS, pCRTvsBaseline = 0.028). Our LBBB/CRT experimental model allowed controlling specific factors responsible for changes in mechanical dyssynchrony and therapy. We illustrated the importance of our quantification method to study these changes and their variability. Our findings confirm the importance of myocardial viability and of specific LBBB-related mechanical dyssynchrony patterns.The international journal of cardiovascular imaging 03/2014; 30(5). DOI:10.1007/s10554-014-0403-2 · 2.32 Impact Factor
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ABSTRACT: A strong body of evidence exists to support cardiac resynchronization therapy (CRT) as a mainstay in the management of advanced heart failure for patients with LBBB-type QRS prolongation. Progress in technology has made percutaneous CRT easier to achieve. Skillful operators can readily reach implantation success rates in excess of 95%. Nevertheless, not every patient selected for CRT, according to current criteria, beneﬁts from this therapy. Several factors contribute to the lack of benefit in these patients, including inadequate patient selection, lack of control of atrial arrhythmias, procedural factors and suboptimal pacemaker settings. It remains to be seen whether newer technology and pacing algorithms will increase response rates to CRT. The focus of this review will be to examine which patients benefit most from CRT and to assess methods for optimizing patient selection in order to achieve maximum benefit from this pivotal therapy.Expert Review of Cardiovascular Therapy 05/2014; 12(5):573-87. DOI:10.1586/14779072.2014.909284
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ABSTRACT: The aim of our study was to evaluate the relationship between insulin resistance and the detection of precocious echocardiographic signs of heart failure in patients with cardiovascular risk factors. We enrolled 34 consecutive patients with cardiovascular risk factors. All patients underwent coronary angiography, echocardiography, and laboratory tests. Exclusion criteria were diabetes (fasting glucose greater than 126 mg/dl or treatment with insulin or oral hypoglycemic agents), coronary artery disease, creatinine above 1.5 mg/dl, left-ventricular hypertrophy, valvular heart disease, ejection fraction below 50%, atrial fibrillation, or other severe arrhythmia. The presence of insulin resistance was assessed by using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR). Ventricular function was investigated by echocardiography. Distinguishing patients with insulin resistance, based on the median value of HOMA-IR (<4.06 and >4.06), we observed that in the group with higher levels of HOMA-IR, there were echocardiographic signs of subclinical ventricular dysfunction statistically more frequent (E/A in group with HOMA <4.06: 1.159 + 0.33 vs. group with HOMA >4.06: 0.87 + 0.29, P = 0.0136; E/E': 6.42 + 4 vs. 15.52 + 3.26, P = 0.001; Tei index: 0.393 + 0.088 vs. 0.489 + 0.079, P = 0.0029; S wave: 0112 + 0.015 vs. 0.114 + 0.027, P = 0.0001; ejection fraction 59.11 + 4.75 vs. 58.88 + 6.81, P = 0.9078). Grade II diastolic dysfunction was observed in 5 patients, grade I in 12 patients, and 17 patients had normal diastolic function. On multivariate analysis, HOMA-IR (P = 0.0092), hypertension (P = 0.0287), waist circumference (P = 0.0009), high-density lipoprotein (P = 0.0004), and fasting blood glucose (P = 0.0003) were variables independently associated with diastolic dysfunction. On analysis of covariance, we found that the variables that influence diastolic dysfunction are HOMA-IR, waist circumference, BMI, and age, and that the only variable that influences Tei index is HOMA-IR. Insulin resistance is frequently associated with subclinical left-ventricular dysfunction. Patients with cardiovascular risk factors and increased HOMA-IR levels, although without diabetes mellitus, overt coronary artery disease, or hypertensive cardiomyopathy, may represent a target population for screening programs, recommended changes in lifestyle, and possibly the use of pharmacological interventions to prevent the onset of heart failure.Journal of Cardiovascular Medicine 02/2014; 15(2). DOI:10.2459/JCM.0b013e3283638164 · 1.41 Impact Factor