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Sacubitril/Valsartan (S/V) carries potential anti-remodeling properties, however long-term effects and biventricular adaptive response are poorly described. 76 HFrEF patients who underwent progressive uptitration of S/V, completed the annual scheduled follow-up. After a median follow-up of 11 (8-13) months, left ventricular (LV) reverse remodeling (RR) is defined as (1) absolute increase in LV ejection fraction (EF) ≥ 10% or LVEF ≥ 50% at follow-up and (2) decrease in indexed LV end-diastolic diameter (LVEDDi) of at least 10% or indexed LVEDDi ≤ 33 mm/m 2 , occurred in 27.6%. Non-ischemic etiology, shorter duration of HF, and absence of a history of AF were independently associated with LVRR ( p < 0.05). TAPSE and TAPSE/PASP, a non-invasive index of right ventricular (RV) coupling to the pulmonary circulation, significantly improved at follow-up (0.45 vs. 0.56, p = 0.02). 41% of patients with baseline RV dysfunction obtained favorable RV remodeling despite only a moderate correlation between RV and LV function was observed (r = 0.478, p = 0.002). Our data point to a potential long-term reverse global remodeling effect by S/V, especially in patients who start S/V at an early stage of the disease, and focus our attention on a possible direct effect of the drug in synergistic hemodynamics between RV and pulmonary circulation.

Valvular heart disease is a leading cause of cardiovascular morbidity and mortality and a major contributor of symptoms and functional disability. Knowledge of valvular heart disease epidemiology and a deep comprehension of the geographical and temporal trends are crucial for clinical advances and the formulation of effective health policy for primary and secondary prevention. This review mainly focuses on the epidemiology of primary (organic, related to the valve itself) valvular disease and its management, especially emphasizing the importance of heart valve centers in ensuring the best care of patients through a multidisciplinary team.

With stress echo (SE) 2020 study, a new standard of practice in stress imaging was developed and disseminated: the ABCDE protocol for functional testing within and beyond CAD. ABCDE protocol was the fruit of SE 2020, and is the seed of SE 2030, which is articulated in 12 projects: 1-SE in coronary artery disease (SECAD); 2-SE in diastolic heart failure (SEDIA); 3-SE in hypertrophic cardiomyopathy (SEHCA); 4-SE post-chest radiotherapy and chemotherapy (SERA); 5-Artificial intelligence SE evaluation (AI-SEE); 6-Environmental stress echocardiography and air pollution (ESTER); 7-SE in repaired Tetralogy of Fallot (SETOF); 8-SE in post-COVID-19 (SECOV); 9: Recovery by stress echo of conventionally unfit donor good hearts (RESURGE); 10-SE for mitral ischemic regurgitation (SEMIR); 11-SE in valvular heart disease (SEVA); 12-SE for coronary vasospasm (SESPASM). The study aims to recruit in the next 5 years (2021-2025) ≥10,000 patients followed for ≥5 years (up to 2030) from ≥20 quality-controlled laboratories from ≥10 countries. In this COVID-19 era of sustainable health care delivery, SE2030 will provide the evidence to finally recommend SE as the optimal and versatile imaging modality for functional testing anywhere, any time, and in any patient.

Stress echo (SE) 2030 study is an international, prospective, multicenter cohort study that will include >10,000 patients from ≥20 centers from ≥10 countries. It represents the logical and chronological continuation of the SE 2020 study, which developed, validated, and disseminated the "ABCDE protocol" of SE, more suitable than conventional SE to describe the complex vulnerabilities of the contemporary patient within and beyond coronary artery disease. SE2030 was started with a recruitment plan from 2021 to 2025 (and follow-up to 2030) with 12 subprojects (ranging from coronary artery disease to valvular and post-COVID-19 patients). With these features, the study poses particular challenges on quality control assurance, methodological harmonization, and data management. One of the significant upgrades of SE2030 compared to SE2020 was developing and implementing a Research Electronic Data Capture (REDCap)-based infrastructure for interactive and entirely web-based data management to integrate and optimize reproducible clinical research data. The purposes of our paper were: first, to describe the methodology used for quality control of imaging data, and second, to present the informatic infrastructure developed on RedCap platform for data entry, storage, and management in a large-scale multicenter study.

Background: Few studies to date have addressed global cardiovascular (CV) risk profile in a "protected" young population as that of medical school students., Objective: to assess CV traditional risk factors and global CV risk profile of Italian medical students throughout the six years of university., Methods: A cross-sectional survey accessible online via quick response (QR) code was conducted among 2700 medical students at the University of Milan, Italy. Data on baseline characteristics, traditional CV risk factors, diet, lifestyle habits, and perceived lifestyle variations were evaluated across different years of school., Results: Overall, 1183 students (mean age, 22.05 years; 729 women (61.6%)) out of 2700 completed the questionnaire (43.8% rate response). More than 16% of the students had at least 3 out of 12 CV risk factors and only 4.6% had ideal cardiovascular health as defined by the American Heart Association. Overweight, underweight, physical inactivity, sub-optimal diet, smoke history, and elevated stress were commonly reported. Awareness of own blood pressure and lipid profile increased over the academic years as well as the number of high-blood-pressure subjects, alcohol abusers, and students constantly stressed for university reasons. Moreover, a reduction in physical-activity levels over the years was reported by half of the students. Conclusion and Relevance: This study demonstrates that a "protected" population as that of young medical students can show an unsatisfactory cardiovascular risk profile and suggests that medical school itself, being demanding and stressful, may have a role in worsening of the lifestyle.

A recently developed algorithm for 3D analysis based on machine learning (ML) principles detects left ventricular (LV) mass without any human interaction. We retrospectively studied the correlation between 2D-derived linear dimensions using the ASE/EACVI-recommended formula and 3D automated, ML-based methods (Philips HeartModel) regarding LV mass quantification in unselected patients undergoing echocardiography. We included 130 patients (mean age 60 ± 18 years; 45% women). There was only discrete agreement between 2D and 3D measurements of LV mass (r = 0.662, r 2 = 0.348, p < 0.001). The automated algorithm yielded an overestimation of LV mass compared to the linear method (Bland-Altman positive bias of 13.1 g with 95% limits of the agreement at 4.5 to 21.6 g, p = 0.003, ICC 0.78 (95%CI 0.68-8.4). There was a significant proportional bias (Beta -0.22, t = -2.9) p = 0.005, the variance of the difference varied across the range of LV mass. When the published cut-offs for LV mass abnormality were used, the observed proportion of overall agreement was 77% (kappa = 0.32, p < 0.001). In consecutive patients undergoing echocardiography for any indications, LV mass assessment by 3D analysis using a novel ML-based algorithm showed systematic differences and wide limits of agreements compared with quantification by ASE/EACVI- recommended formula when the current cut-offs and partition values were applied.