CLINICAL STATEMENTS AND GUIDELINEST he intent of this American Heart Association (AHA) scientific statement is to summarize our current understanding of dilated cardiomyopathies. There is special emphasis on recent developments in diagnostic approaches and therapies for specific cardiomyopathies. Recommendations in this document are based on published studies, published practice guidelines from the American College of Cardiology (ACC)/AHA 1 and other organizations, 2,3 and the multidisciplinary expertise of the writing group. Existing evidence in epidemiology, classification, diagnosis, and management of specific cardiomyopathies is usually derived from nonrandomized observational studies, registries, case reports, or expert opinion based on clinical experience, not large-scale randomized clinical trials or systematic reviews. Therefore, in this document, rather than using the standard ACC/AHA classification schema of recommendations and level of evidence, 4 we have included key management strategies at the end of each section and categorized our recommendations according to the level of consensus. Although the format of our recommendations might resemble the ACC/AHA classification of recommendations used in the ACC/AHA practice guidelines, because of the preponderance of expert opinion or level of evidence C evidence in our document, we elected to use different terminology to provide a distinction from the practice guidelines, in which stronger levels and quality of evidence with randomized clinical trials or meta-analyses are usually present. 4 The levels of evidence follow the AHA and ACC methods of classifying the level of certainty of the treatment effect. 4 DEfINITIoN of DILATED CArDIoMyopAThyThe term dilated cardiomyopathy (DCM) refers to a spectrum of heterogeneous myocardial disorders that are characterized by ventricular dilation and depressed myocardial performance in the absence of hypertension, valvular, congenital, or ischemic heart disease. 5 In clinical practice, the pathogenesis of heart failure (HF) has often been placed into 2 categories: ischemic and nonischemic cardiomyopathy. The term nonischemic cardiomyopathy has been interchangeably used with DCM. Although this approach might be practical, it fails to recognize that nonischemic cardiomyopathy can include cardiomyopathies caused by volume or pressure overload (such as hypertension or valvular heart disease) that are not conventionally accepted under the definition of DCM. 1,5 Again, in general practice and clinical research trials, the term ischemic cardiomyopathy is defined as cardiomyopathy caused by ischemic heart disease. Current use of ischemic cardiomyopathy terminology implies ventricular dilation and depressed myocardial contractility caused by ischemia or infarction. CLASSIfICATIoN of CArDIoMyopAThIESThe first classification on this topic categorized cardiomyopathies as heart muscle diseases with dilated (DCM), hypertrophic, restrictive, arrhythmogenic right ventricular (ARVC), or nonclassifiable cardiomyopathy in 1980. 5 Subse...

In response to the COVID-19 pandemic, US federal and state governments have implemented wide-ranging stay-at-home recommendations as a means to reduce spread of infection. As a consequence, many US healthcare systems and practices have curtailed ambulatory clinic visits-pillars of care for patients with heart failure (HF). In this context, synchronous audio/video interactions, also known as virtual visits (VVs), have emerged as an innovative and necessary alternative. This scientific statement outlines the benefits and challenges of VVs, enumerates changes in policy and reimbursement that have increased the feasibility of VVs during the COVID-19 era, describes platforms and models of care for VVs, and provides a vision for the future of VVs.

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AimsThe complete blood count (CBC) and basic metabolic profile are common, low-cost blood tests, which have previously been used to create and validate the Intermountain Risk Score (IMRS) for mortality prediction. Mortality is the most definitive clinical endpoint, but medical care is more easily applied to modify morbidity and thereby prevent death. This study tested whether IMRS is associated with clinical morbidity endpoints. Methods and resultsPatients seen for coronary angiography (n ¼ 3927) were evaluated using a design similar to a genome-wide association study. The Bonferroni correction for 102 tests required a P-value of ≤ 4.9 × 10 24 for significance. A second set of angiography patients (n ¼ 10 413) was used to validate significant findings from the first patient sample. In the first patient sample, IMRS predicted heart failure (HF) (P trend ¼ 1.6 × 10 226), coronary disease (P trend ¼ 2.6 × 10 211), myocardial infarction (MI) (P trend ¼ 3.1 × 10 225 ), atrial fibrillation (P trend ¼ 2.5 × 10 220 ), and chronic obstructive pulmonary disease (P trend ¼ 4.7 × 10 24). Even more, IMRS predicted HF readmission [hazard ratio (HR) ¼ 2.29/category, P trend ¼ 1.2 × 10 26 ), incident HF (HR ¼ 1.88/category, P trend ¼ 0.02), and incident MI (HR ¼ 1.56/category, P trend ¼ 4.7 × 10 24 ). These findings were verified in the second patient sample. ConclusionIntermountain Risk Score, a predictor of mortality, was associated with morbidity endpoints that often lead to mortality. Further research is required to fully characterize its clinical utility, but its low-cost CBC and basic metabolic profile composition may make it ideal for initial risk estimation and prevention of morbidity and mortality. An IMRS web calculator is freely available at http://intermountainhealthcare.org/IMRS. Trial Registration: Database registry of the Intermountain Heart Collaborative Study: NCT00406185

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Purpose Heart failure (HF) patients often have comorbid conditions that confound management and adversely affect prognosis. The purpose of this study was to determine whether the obesity paradox is also present in hospitalized HF patients in an integrated healthcare system. Data sources A cohort of 2707 patients with a primary diagnosis of HF was identified within an integrated, 20‐hospital healthcare system. Patients were identified by ICD‐9 codes or a left ventricular ejection fraction ≤40% dating back to 1995. Body mass index (BMI) was calculated using the first measured height and weight when hospitalized with HF. Survival rates were calculated using Kaplan Meier estimation. Hazard ratios for 3‐year mortality with 95% confidence intervals were assessed using Cox regression, controlling for age, gender, and severity of illness at time of diagnosis. Conclusions Three‐year survival rates paradoxically improved for patients with increasing BMI. Survival rates for the larger three BMI quartiles were significantly better than for the lowest quartile after adjusting for severity of illness, age, and gender. Implications for practice While obesity increases the risk of developing HF approximately twofold, reports involving stable outpatients suggest that obesity is associated with improved survival after the development of HF. This finding is paradoxical because obesity increases the risk and worsens the prognosis of other cardiovascular diseases.