Anderson-Fabry disease cardiomyopathy: an update on epidemiology, diagnostic approach, management and monitoring strategies

Anderson-Fabry disease (AFD) is an X-linked lysosomal storage disorder caused by deficient activity of the enzyme alpha-galactosidase. While AFD is recognized as a progressive multi-system disorder, infiltrative cardiomyopathy causing a number of cardiovascular manifestations is recognized as an important complication of this disease. AFD affects both men and women, although the clinical presentation typically varies by sex, with men presenting at a younger age with more neurologic and renal phenotype and women developing a later onset variant with more cardiovascular manifestations. AFD is an important cause of increased myocardial wall thickness, and advances in imaging, in particular cardiac magnetic resonance imaging and T1 mapping techniques, have improved the ability to identify this disease non-invasively. Diagnosis is confirmed by the presence of low alpha-galactosidase activity and identification of a mutation in the GLA gene. Enzyme replacement therapy remains the mainstay of disease modifying therapy, with two formulations currently approved. In addition, newer treatments such as oral chaperone therapy are now available for select patients, with a number of other investigational therapies in development. The availability of these therapies has significantly improved outcomes for AFD patients. Improved survival and the availability of multiple agents has presented new clinical dilemmas regarding disease monitoring and surveillance using clinical, imaging and laboratory biomarkers, in addition to improved approaches to managing cardiovascular risk factors and AFD complications. This review will provide an update on clinical recognition and diagnostic approaches including differentiation from other causes of increased ventricular wall thickness, in addition to modern strategies for management and follow-up

Bridging Treatment Implementation Gaps in Patients With Heart Failure: JACC Focus Seminar 2/3.

Heart failure (HF) is a leading cause of death and disability in older adults. Despite decades of high-quality evidence to support their use, guideline-directed medical therapies (GDMTs) that reduce death and disease burden in HF have been suboptimally implemented. Approaches to closing care gaps have focused largely on strategies proven to be ineffective, whilst effective interventions shown to improve GDMT uptake have not been instituted. This review synthesizes implementation interventions that increase the uptake of GDMT, discusses barriers and facilitators of implementation, summarizes conceptual frameworks in implementation science that could improve knowledge uptake, and offers suggestions for trial design that could better facilitate end-of-trial implementation. We propose an evidence-to-care conceptual model that could foster the simultaneous generation of evidence and long-term implementation. By adopting principles of implementation science, policymakers, researchers, and clinicians can help reduce the burden of HF on patients and health care systems worldwide

Predicting Risk of Cardiotoxic Effects in Breast Cancer: Are We There Yet?

People with breast cancer are at increased risk of cardiovascular disease, myocardial injury, and heart failure (HF) due to underlying risk factors, a proinflammatory milieu, and cancer therapy–related cardiovascular toxic effects (CTR-CVT).1 Of the therapies for breast cancer, human epidermal growth factor receptor-2 (ERBB2)–targeted therapies, such as trastuzumab, and anthracyclines, such as doxorubicin, have the most well-described associations with both decline in left ventricular ejection fraction (LVEF), and clinical HF.2,3 Given that cardiotoxic effects are not always reversible and can result in significant morbidity and mortality, current European Society of Cardiology guidelines recommend risk stratification before starting potentially cardiotoxic anticancer therapy in all patients with cancer (Class 1B) to identify patients who are at high risk of developing CTR-CVT.4 Several risk prediction models have been developed to estimate adverse cardiac outcomes among patients with breast cancer. However, the design, performance, and methodological rigor of the risk prediction models have not been assessed systematically

THE ASSOCIATION BETWEEN SEX, RACE, SOCIOECONOMIC STATUS, AND IN-HOSPITAL ALL-CAUSE MORTALITY AMONG PATIENTS ADMITTED FOR HEART FAILURE: A RETROSPECTIVE COHORT STUDY

BackgroundHeart failure (HF) hospitalization is associated with high mortality. We aimed to assess the role of race, sex, and socioeconomic status (SES) in the risk of in-hospital mortality among patients hospitalized for HF.MethodsWe used the National Inpatient Sample to analyze all hospitalizations for HF in the United States from 2015-2017. We extracted patient demographics and clinical characteristics. Using a multivariate Poisson regression model with interaction terms, we determined the relationship between sex, race, SES, and risk of in-hospital mortality.ResultsWe identified 857,496 HF admissions, representing 4,287,478 HF admissions after applying discharge weights. Median (IQR) age of patients was 73.4 (62.4-82.9) years, 48.7% were women, 30.0% were non-White, and 33.1% were of low SES. There were 218,580 (5.1%) deaths. In the multivariate regression model, men (RR 1.09; 95% CI 1.07, 1.11) and those of low SES (RR 1.02; 95% CI 1.00, 1.04) experienced increased risk of in-hospital mortality relative to women and those of high SES, respectively, while Black (RR 0.78; 95% CI 0.75, 0.80) and Hispanic patients (RR 0.89; 95% CI 0.86, 0.93) experienced a reduced risk of in-hospital mortality relative to White patients. There was a significant interaction between race and sex (p=0.04), and race and SES (p <0.01), but not sex and SES (p=0.95). Hispanic race was associated with reduced risk of in-hospital mortality relative to White race among low (RR 0.88; 95% CI 0.82, 0.93) but not high SES (RR 0.93; 95% CI 0.87, 1.00) patients. Black race was associated with reduced risk of in-hospital mortality relative to white race among all SES groups, although the reduction was more pronounced among low (RR 0.74; 0.70, 0.77) than high SES patients (RR 0.80; 95% CI 0.75, 0.84). Similarly, risk of in-hospital mortality was lower in Black men (RR 0.74; 95% CI 0.71, 0.78) than Black women (RR 0.79, 95% CI 0.76, 0.83) relative to White men and women, respectively.ConclusionThis retrospective cohort study confirms prior published associations between race and in-hospital mortality in HF and demonstrates the interaction between race, sex, and SES. These findings are novel and warrant further study

Applications of artificial intelligence and machine learning in heart failure

: Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur