The natural history of dilated cardiomyopathy

Evidence for reciprocal network interactions between injured hearts and cancer

Heart failure (HF) and cancer are responsible for 50% of all deaths in middle-aged people. These diseases are tightly linked, which is supported by recent epidemiological studies and case control studies, demonstrating that HF patients have a higher risk to develop cancer such as lung and breast cancer. For HF patients, a one-size-fits-all clinical management strategy is not effective and patient management represents a major economical and clinical burden. Anti-cancer treatments-mediated cardiotoxicity, leading to HF have been extensively studied. However, recent studies showed that even before the initiation of cancer therapy, cancer patients presented impairments in the cardiovascular functions and exercise capacity. Thus, the optimal cardioprotective and surveillance strategies should be applied to cancer patients with pre-existing HF. Recently, preclinical studies addressed the hypothesis that there is bilateral interaction between cardiac injury and cancer development. Understanding of molecular mechanisms of HF-cancer interaction can define the profiles of bilateral signaling networks, and identify the disease-specific biomarkers and possibly therapeutic targets. Here we discuss the shared pathological events, and some treatments of cancer- and HF-mediated risk incidence. Finally, we address the evidences on bilateral connection between cardiac injury (HF and early cardiac remodeling) and cancer through secreted factors (secretoms).

A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions

OBJECTIVE

Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stratification for these high-risk patients by incorporating various types of data. The aim of this study was to build an ML model to predict adverse events including all-cause deaths and heart transplantation in DCM patients with severely impaired LV systolic function.

METHODS

One hundred and eighteen patients with DCM and severely reduced LVEFs (<35%) were included. The baseline clinical characteristics, laboratory data, electrocardiographic, and cardiac magnetic resonance (CMR) features were collected. Various feature selection processes and classifiers were performed to select an ML model with the best performance. The predictive performance of tested ML models was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve using 10-fold cross-validation.

RESULTS

Twelve patients died, and 17 patients underwent heart transplantation during the median follow-up of 508 days. The ML model included systolic blood pressure, left ventricular end-systolic and end-diastolic volume indices, and late gadolinium enhancement (LGE) extents on CMR imaging, and a support vector machine was selected as a classifier. The model showed excellent performance in predicting adverse events in DCM patients with severely reduced LVEF (the AUC and accuracy values were 0.873 and 0.763, respectively).

CONCLUSIONS

This ML technique could effectively predict adverse events in DCM patients with severely reduced LVEF.

ADVANCES IN KNOWLEDGE

The ML method has superior ability in risk stratification in severe DCM patients.

Long-term outcomes of non-ischemic dilated cardiomyopathy patients with left ventricular ejection fraction ≤19% on medical therapy

Background

Patients with heart failure and reduced ejection fraction in low resource settings may not have access to devices and expensive therapeutic options. We followed up a cohort of patients with non-ischemic dilated cardiomyopathy (DCM) with very low left ventricle ejection fraction (LVEF≤19%) on low cost medical therapy alone. By selecting patients with such low LVEF, this study was restricted to patients with severe disease. We studied long-term transplant free survival of these patients.

Methods and results

The study enrolled 130 patients (83 men and 47 women) of DCM cohort with LVEF≤19% from April 2003–December 2018 on medical therapy alone. Mean age was 40.35 ± 13.9 years. Mean follow-up was 45.6 ± 39 months while median follow-up was 39 months (range: 0–176 months). Patients on devices (ICD/CRT) for heart failure management were excluded. Fifty-four patients died and three underwent transplant during the study. Median survival was 86 months (S.E. 22.38). 113 patients had follow-up till end of study. In the worst case scenario, if all 17 patients who were lost to final follow-up were assumed to be dead, the median survival was still 57 (S.E.9.28) months. Higher baseline NYHA class, recurrent heart failure hospitalizations, absence of treatment with beta-blockers, angiotensin converting enzyme inhibitors/angiotensin receptor blockers and aldosterone antagonists were predictors of death on univariate analysis whereas none of these parameters were significant on multivariate analysis.

Conclusions

Median survival of our DCM cohort with LVEF≤19% on medical therapy was over 7 years.

Emerging concepts in arrhythmogenic dilated cardiomyopathy

Dilated cardiomyopathy (DCM) represents one of the primary cardiomyopathies and may lead to heart failure and sudden death. Until recently, ventricular arrhythmias were considered to be a direct consequence of the systolic dysfunction of the left ventricle (LV) and guidelines for implantable cardioverter defibrillator implantation were established on this basis. However, the identification of heritable dilated cardiomyopathy phenotypes that presented with mildly impaired or moderate LV dysfunction, with or without chamber dilatation, and ventricular arrhythmias exceeding the degree of the underlying morphological abnormalities lead to the identification of the arrhythmogenic phenotypes and genotypes of DCM. This subset of DCM patients presents phenotypic and in many cases genotypic overlaps with left dominant arrhythmogenic cardiomyopathy (LDAC). LMNA, SCN5A, FLNC, TTN, and RBM20 are the main genes responsible for arrhythmogenic DCM. Moreover, desmosomal genes such as DSP and other non-desmosomal such as DES and PLN have been associated with both LDAC and arrhythmogenic DCM. The aim of this review is to highlight the importance of genetic profiling among DCM patients with disproportionate arrhythmic burden and the significance of the electrocardiogram, cardiac magnetic resonance, Holter monitoring, detailed family history, and other assays in order to identify red flags for arrhythmogenic DCM and proceed to an early preventive approach for sudden cardiac death. A special consideration was given to the phenotypic and genotypic overlap with LDAC. The role of myocarditis as a common disease expression of LDAC and arrhythmogenic DCM is also analyzed supporting the premise of their phenotypic overlap.