Background Cardiac muscle hypercontractility is a key pathophysiological abnormality in hypertrophic cardiomyopathy, and a major determinant of dynamic left ventricular outflow tract (LVOT) obstruction. Available pharmacological options for hypertrophic cardiomyopathy are inadequate or poorly tolerated and are not disease-specific. We aimed to assess the efficacy and safety of mavacamten, a first-in-class cardiac myosin inhibitor, in symptomatic obstructive hypertrophic cardiomyopathy. Methods In this phase 3, randomised, double-blind, placebo-controlled trial (EXPLORER-HCM) in 68 clinical cardiovascular centres in 13 countries, patients with hypertrophic cardiomyopathy with an LVOT gradient of 50 mm Hg or greater and New York Heart Association (NYHA) class II-III symptoms were assigned (1:1) to receive mavacamten (starting at 5 mg) or placebo for 30 weeks. Visits for assessment of patient status occurred every 2-4 weeks. Serial evaluations included echocardiogram, electrocardiogram, and blood collection for laboratory tests and mavacamten plasma concentration. The primary endpoint was a 1•5 mL/kg per min or greater increase in peak oxygen consumption (pVO 2) and at least one NYHA class reduction or a 3•0 mL/kg per min or greater pVO 2 increase without NYHA class worsening. Secondary endpoints assessed changes in post-exercise LVOT gradient, pVO 2 , NYHA class, Kansas City Cardiomyopathy Questionnaire-Clinical Summary Score (KCCQ-CSS), and Hypertrophic Cardiomyopathy Symptom Questionnaire Shortness-of-Breath subscore (HCMSQ-SoB). This study is registered with ClinicalTrials.gov, NCT03470545.

In the context of the COVID-19 pandemic, many barriers to telemedicine disappeared. Virtual visits and telemonitoring strategies became routine. Evidence is accumulating regarding the safety and efficacy of virtual visits to replace in-person visits. A structured approach to virtual encounters is recommended. Telemonitoring includes patient reported remote vital sign monitoring, information from wearable devices, cardiac implantable electronic devices and invasive remote hemodynamic monitoring. The intensity of the monitoring should match the risk profile of the patient. Attention to cultural and educational barriers is important to prevent disparities in telehealth implementation.

Heart failure with reduced ejection fraction (HFrEF) is an increasing global pandemic affecting more than 30 million individuals worldwide. Importantly, HFrEF is frequently accompanied by the presence of cardiac and non-cardiac comorbidities that may greatly influence the management and prognosis of the disease. In this review article, we will focus on three important comorbidities in HFrEF; atrial fibrillation (AF), advanced renal disease, and elderly, which all have a paramount impact on progression of the disease, management strategies, and response to therapy. AF is very common in HFrEF and shares many risk factors. AF aggravates heart failure and contributes to HFrelated adverse clinical outcomes; hence it requires special consideration in HFrEF management. The kidney function is largely affected by the reduced cardiac output developed in the setting of HFrEF, and the neurohormonal feedback effects create a complex interplay that pose challenges in the management of HFrEF when renal function is significantly impaired. Cardiorenal syndrome is a challenging sequela with increased morbidity and mortality thereby reflecting the delicate and complex balance between the heart and the kidney in HFrEF and renal failure conditions. Furthermore, patients with advanced renal failure have poor prognosis in the presence of HFrEF with limited treatment options. Finally, aging and frailty are important factors that influence treatment strategies in HFrEF with greater emphasis on tolerability and safety of the various HFrEF therapies in elderly individuals.

Background Machine learning (ML) is increasingly being applied in Cardiology to predict outcomes and assist in clinical decision‐making. We sought to develop and validate an ML model for the prediction of mortality after heart transplantation (HT) in adults with congenital heart disease (ACHD). Methods The United Network for Organ Sharing (UNOS) database was queried from 2000 to 2020 for ACHD patients who underwent isolated HT. The study cohort was randomly split into derivation (70%) and validation (30%) datasets that were used to train and test a CatBoost ML model. Feature selection was performed using SHapley Additive exPlanations (SHAP). Recipient, donor, procedural, and post‐transplant characteristics were tested for their ability to predict mortality. We additionally used SHAP for explainability analysis, as well as individualized mortality risk assessment. Results The study cohort included 1033 recipients (median age 34 years, 61% male). At 1 year after HT, there were 205 deaths (19.9%). Out of a total of 49 variables, 10 were selected as highly predictive of 1‐year mortality and were used to train the ML model. Area under the curve (AUC) and predictive accuracy for the 1‐year ML model were .80 and 75.2%, respectively, and .69 and 74.2% for the 3‐year model, respectively. Based on SHAP analysis, hemodialysis of the recipient post‐HT had overall the strongest relative impact on 1‐year mortality after HΤ, followed by recipient‐estimated glomerular filtration rate, age and ischemic time. Conclusions ML models showed satisfactory predictive accuracy of mortality after HT in ACHD and allowed for individualized mortality risk assessment.

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