Multimodality imaging of hypertrophic cardiomyopathy in a transplanted heart.
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ABSTRACT:
SUBMITTER: Abbasi SA
PROVIDER: S-EPMC4758361 | biostudies-literature | 2014 Sep
REPOSITORIES: biostudies-literature
Publications
Multimodality imaging of hypertrophic cardiomyopathy in a transplanted heart.
Abbasi Siddique A SA Cheezum Michael K MK Ghosh Nina N Stewart Garrick C GC Di Carli Marcelo F MF
Circulation 20140901 12
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Project description:Hypertrophic cardiomyopathy (HCM) is characterized by abnormal growth of the myocardium with myofilament disarray and myocardial hyper-contractility, leading to left ventricular hypertrophy and fibrosis. Where culprit genes are identified, they typically relate to cardiomyocyte sarcomere structure and function. Multi-modality imaging plays a crucial role in the diagnosis, monitoring, and risk stratification of HCM, as well as in screening those at risk. Following the recent publication of the first European Society of Cardiology (ESC) cardiomyopathy guidelines, we build on previous reviews and explore the roles of electrocardiography, echocardiography, cardiac magnetic resonance (CMR), cardiac computed tomography (CT), and nuclear imaging. We examine each modality's strengths along with their limitations in turn, and discuss how they can be used in isolation, or in combination, to facilitate a personalized approach to patient care, as well as providing key information and robust safety and efficacy evidence within new areas of research.
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Project description:Aims: We aimed to assess diabetes outcomes in heart failure (HF) patients with hypertrophic cardiomyopathy (HCM). Methods: The National Inpatient Sample database was analyzed to identify records from 2005 to 2015 of patients hospitalized for HF with concomitant HCM. We examined the prevalence of diabetes in those patients, assessed the temporal trend of in-hospital mortality, ventricular fibrillation, atrial fibrillation, and cardiogenic shock and compared diabetes patients to their non-diabetes counterparts. Results: Among patients with HF, 0.26% had HCM, of whom 29.3% had diabetes. Diabetes prevalence increased from 24.8% in 2005 to 32.7% in 2015. The mean age of patients with diabetes decreased from 71 ± 13 to 67.6 ± 14.2 (p < 0.01), but the prevalence of cardiovascular risk factors significantly increased. In-hospital mortality decreased from 4.3% to 3.2% between 2005 and 2015. Interestingly, cardiogenic shock, VF, and AF followed an upward trend. Age (OR = 1.04 [1.03-1.05]), female gender (OR = 1.50 [0.72-0.88]), and cardiovascular risk factors were associated with a higher in-hospital mortality risk in diabetes. Compared to non-diabetes patients, the ones with diabetes were younger and had more comorbidities. Unexpectedly, the adjusted risks of in-hospital mortality (aOR = 0.88 [0.76-0.91]), ventricular fibrillation (aOR = 0.79 [0.71-0.88]) and atrial fibrillation (aOR 0.80 [0.76-0.85]) were lower in patients with diabetes, but not cardiogenic shock (aOR 1.01 [0.80-1.27]). However, the length of stay was higher in patients with diabetes, and so were the total charges per stay. Conclusion: In total, we observed a temporal increase in diabetes prevalence among patients with HF and HCM. However, diabetes was paradoxically associated with lower in-hospital mortality and arrhythmias.
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Project description:Non-ischemic cardiomyopathy encompasses a heterogeneous group of diseases, with a generally unfavorable long-term prognosis. Cardiac resynchronization therapy (CRT) is a useful therapeutic option for patients with symptomatic heart failure, currently recommended by all available guidelines, with outstanding benefits, especially in non-ischemic dilated cardiomyopathy. Still, in spite of clear indications based on identifying a dyssynchronous pattern on the electrocardiogram (ECG,) a great proportion of patients are non-responders. The idea that multimodality cardiac imaging can play a role in refining the selection criteria and the implant technique and help with subsequent system optimization is promising. In this regard, predictors of CRT response, such as apical rocking and septal flash have been identified. Promising new data come from studies using cardiac magnetic resonance and nuclear imaging for showcasing myocardial dyssynchrony. Still, to date, no single imaging predictor has been included in the guidelines, probably due to lack of validation in large, multicenter cohorts. This review provides an up-to-date synthesis of the latest evidence of CRT use in non-ischemic cardiomyopathy and highlights the potential additional value of multimodality imaging for improving CRT response in this population. By incorporating all these findings into our clinical practice, we can aim toward obtaining a higher proportion of responders and improve the success rate of CRT.
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Project description:Introduction: Patients with hypertrophic cardiomyopathy (HCM) are at risk for lethal ventricular arrhythmia, but the electrophysiological substrate behind this is not well-understood. We used non-invasive electrocardiographic imaging to characterize patients with HCM, including cardiac arrest survivors. Methods: HCM patients surviving ventricular fibrillation or hemodynamically unstable ventricular tachycardia (n = 17) were compared to HCM patients without a personal history of potentially lethal arrhythmia (n = 20) and a pooled control group with structurally normal hearts. Subjects underwent exercise testing by non-invasive electrocardiographic imaging to estimate epicardial electrophysiology. Results: Visual inspection of reconstructed epicardial HCM maps revealed isolated patches of late activation time (AT), prolonged activation-recovery intervals (ARIs), as well as reversal of apico-basal trends in T-wave inversion and ARI compared to controls (p < 0.005 for all). AT and ARI were compared between groups. The pooled HCM group had longer mean AT (60.1 ms vs. 52.2 ms, p < 0.001), activation dispersion (55.2 ms vs. 48.6 ms, p = 0.026), and mean ARI (227 ms vs. 217 ms, p = 0.016) than structurally normal heart controls. HCM ventricular arrhythmia survivors could be differentiated from HCM patients without a personal history of life-threatening arrhythmia by longer mean AT (63.2 ms vs. 57.4 ms, p = 0.007), steeper activation gradients (0.45 ms/mm vs. 0.36 ms/mm, p = 0.011), and longer mean ARI (234.0 ms vs. 221.4 ms, p = 0.026). A logistic regression model including whole heart mean activation time and activation recovery interval could identify ventricular arrhythmia survivors from the HCM cohort, producing a C statistic of 0.76 (95% confidence interval 0.72-0.81), with an optimal sensitivity of 78.6% and a specificity of 79.8%. Discussion: The HCM epicardial electrotype is characterized by delayed, dispersed conduction and prolonged, dispersed activation-recovery intervals. Combination of electrophysiologic measures with logistic regression can improve differentiation over single variables. Future studies could test such models prospectively for risk stratification of sudden death due to HCM.
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Project description:Background: Development of advanced heart failure (HF) symptoms is the most common adverse pathway in hypertrophic cardiomyopathy (HCM) patients. Currently, there is a limited ability to identify HCM patients at risk of HF. Objectives: In this study, we present a machine learning (ML)-based model to identify individual HCM patients who are at high risk of developing advanced HF symptoms. Methods: From a consecutive cohort of HCM patients evaluated at the Tufts HCM Institute from 2001 to 2018, we extracted a set of 64 potential risk factors measured at baseline. Only patients with New York Heart Association (NYHA) functional class I/II and LV ejection fraction (LVEF) by echocardiography >35% were included. The study cohort (n = 1,427 patients) was split into three disjoint subsets: development (50%), model selection (10%), and independent validation (40%). The least absolute shrinkage and selection operator was used to select the most influential clinical variables. An ensemble of ML classifiers, including logistic regression, was used to identify patients with high risk of developing a HF outcome. Study outcomes were defined as progression to NYHA class III/IV, drop in LVEF below 35%, septal reduction procedure, and/or heart transplantation. Results: During a mean follow-up of 4.7 ± 3.7 years, advanced HF occurred in 283 (20% out of 1,427) patients. The model features included patients' sex, NYHA class (I or II), HCM type (i.e., obstructive or not), LV wall thickness, LVEF, presence of HF symptoms (e.g., dyspnea, presyncope), comorbidities (atrial fibrillation, hypertension, mitral regurgitation, and systolic anterior motion), and type of cardiac medications. The developed risk stratification model showed strong differentiation power to identify patients at advanced HF risk in the testing dataset (c-statistics = 0.81; 95% confidence interval [CI]: 0.76, 0.86). The model allowed correct identification of high-risk patients with accuracy 74% (CI: 0.70, 0.78), sensitivity 80% (CI: 0.77, 0.83), and specificity 72% (CI: 0.68, 0.76). The model performance was comparable among different sex and age groups. Conclusions: A 5-year risk prediction of progressive HF in HCM patients can be accurately estimated using ML analysis of patients' clinical and imaging parameters. A set of 17 clinical and imaging variables were identified as the most important predictors of progressive HF in HCM.
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