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Aim: Echo‐derived haemodynamic classification, based on forward‐flow and left ventricular (LV) filling pressure (LVFP) correlates, has been proposed to phenotype patients with heart failure and reduced ejection fraction (HFrEF). To assess the prognostic relevance of baseline echocardiographically defined haemodynamic profile in ambulatory HFrEF patients before starting sacubitril/valsartan. Methods and results: In our multicentre, open‐label study, HFrEF outpatients were classified into 4 groups according to the combination of forward flow (cardiac index; CI:< or ≥2.0 L/min/m2) and early transmitral Doppler velocity/early diastolic annular velocity ratio (E/e′: ≥ or <15): Profile‐A: normal‐flow, normal‐pressure; Profile‐B: low‐flow, normal‐pressure; Profile‐C: normal‐flow, high‐pressure; Profile‐D: low‐flow, high‐pressure. Patients were started on sacubitril/valsartan and followed‐up for 12.3 months (median). Rates of the composite of death/HF‐hospitalization were assessed by multivariable Cox proportional‐hazards models. Twelve sites enrolled 727 patients (64 ± 12 year old; LVEF: 29.8 ± 6.2%). Profile‐D had more comorbidities and worse renal and LV function. Target dose of sacubitril/valsartan (97/103 mg BID) was more likely reached in Profile‐A (34%) than other profiles (B: 32%, C: 24%, D: 28%, P < 0.001). Event‐rate (per 100 patients per year) progressively increased from Profile‐A to Profile‐D (12.0%, 16.4%, 22.9%, and 35.2%, respectively, P < 0.0001). By covariate‐adjusted Cox model, profiles with low forward‐flow (B and D) remained associated with poor outcome (P < 0.01). Adding this categorization to MAGGIC‐score and natriuretic peptides, provided significant continuous net reclassification improvement (0.329; P < 0.001). Intermediate and high‐dose sacubitril/valsartan reduced the event's risk independently of haemodynamic profile. Conclusions: Echocardiographically‐derived haemodynamic classification identifies ambulatory HFrEF patients with different risk profiles. In real‐world HFrEF outpatients, sacubitril/valsartan is effective in improving outcome across different haemodynamic profiles. [ABSTRACT FROM AUTHOR]

Background: Diastolic dysfunction (DD) assessment in heart failure is still challenging. Peak atrial longitudinal strain (PALS) is strongly related to end-diastolic pressure and prognosis, but it is still not part of standard DD assessment. We tested the hypothesis that a machine learning approach would be useful to include PALS in DD classification and refine prognostic stratification. Methods: In a derivation cohort of 864 heart failure patients in sinus rhythm (age, 66.6±12 years; heart failure with reduced ejection fraction, n=541; heart failure with mildly reduced ejection fraction, n=129; heart failure with preserved ejection fraction, n=194), machine learning techniques were retrospectively applied to PALS and guideline-recommended diastolic variables. Outcome (death/heart failure rehospitalization) of the identified DD-clusters was compared with that by guidelines-based classification. To identify the best combination of variables able to classify patients in one of the identified DD-clusters, classification and regression tree analysis was applied (with DD-clusters as dependent variable and PALS plus guidelines-recommended diastolic variables as explanatory variables). The algorithm was subsequently validated in a prospective cohort of 189 heart failure outpatients (age, 65±13 years). Results: Three distinct echocardiographic DD-clusters were identified (cluster-1, n=212; cluster-2, n=376; cluster-3 DD, n=276), with modest agreement with guidelines-recommended classification (kappa=0.40; P <0.001). DD-clusters were predicted by a simple algorithm including E/A ratio, left atrial volume index, E/e′ ratio, and PALS. After 36.5±29.4 months follow-up, 318 events occurred. Compared to guideline-based classification, DD-clusters showed a better association with events in multivariable models (C-index 0.720 versus 0.733, P =0.033; net reclassification improvement 0.166 [95% CI, 0.035–0.276], P =0.013), without interaction with ejection fraction category. In the validation cohort (median follow-up: 18.5 months), cluster-based classification better predicted outcome than guideline-based classification (C-index 0.80 versus 0.78, P =0.093). Conclusions: Integrating PALS by machine learning algorithm in DD classification improves risk stratification over recommended current criteria, regardless of ejection fraction status. This proof of concept study needs further validation of the proposed algorithm to assess generalizability to other populations. [ABSTRACT FROM AUTHOR]

Anorexia nervosa (AN) is an eating disorder in which a distorted self-perception of body image and an excessive fear of gaining weight result in extreme restrictions in eating habits. AN may be divided into two types: a 'binge-eating/purging type' during which the individual regularly engages in overeating and then purging behavior, and a 'restricting type', in which she does not. AN is a serious medical problem in young people in Western societies. It is widely reported that patients with AN exhibit an enhanced mortality rate as compared with age-matched healthy subjects, which has been mainly ascribed to cardiac complications. At least one-third of all deaths in patients with anorexia nervosa are estimated to be due to cardiac causes, mainly sudden death. Cardiovascular complications of AN can be present in up to 80 % of cases, and among them alterations in cardiac electrical activity, structure and hemodynamics have been reported as causes of morbidity and mortality. The objective of this brief review is to summarize current knowledge on the main cardiovascular complications of AN, their underlying mechanisms and the possible therapeutic approaches. [ABSTRACT FROM AUTHOR]

The myocardial bridge (MB) is a common anomaly of the coronary tree, very often clinically silent. The artery typically involved is the left anterior descending in its proximal and/or middle portion. MB can cause ischaemia with various mechanisms, directly proportional to the degree of compression of the intra-myocardial tract, which impairs the coronary flow. It is a dynamic phenomenon that is affected by the adrenergic tone and is therefore often brought by physical exercise. MB, when symptomatic, often begins with angina from exertion; some patients have more severe conditions such as unstable angina or myocardial infarction. Coronary vasospasm related to MB-induced endothelial dysfunction can explain a number of cases that come to observation even with catastrophic pictures such as ventricular fibrillation caused by ischaemia. The diagnostic workup includes the non-invasive study using computed tomography angiography and the invasive study of the haemodynamic impact using pressure and Doppler guides. In symptomatic cases, drug therapy with a beta-blocker is enough to manage angina. When it fails, there is the option of coronary angioplasty or surgical treatment techniques., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Lipoprotein Lp(a) represents an independent risk factor for coronary artery disease (CAD). However, its association with CAD burden and lipid rich plaques prone to rupture in patients with acute coronary syndrome (ACS) still remains unknown. These data aim to investigate the association among serum Lipoprotein(a) (Lpa) levels, coronary atherosclerotic burden and features of culprit plaque in patients with ACS and obstructive CAD. For his reason, a total of 500 ACS patients were enrolled for the angiographic cohort and 51 ACS patients were enrolled for the optical coherence tomography (OCT) cohort. Angiographic CAD severity was assessed by Sullivan score and by Bogaty score including stenosis score and extent index, whereas OCT plaque features were evaluated at the site of the minimal lumen area and along the culprit segment. In the angiographic cohort, Lp(a) was a weak independent predictor of Sullivan score (p<0.0001), stenosis score (p<0.0001) and extent index (p<0.0001). In the OCT cohort, patients with higher Lp(a) levels (>30 md/dl) compared to patients with lower Lp(a) levels (<30 md/dl) exhibited a higher prevalence of lipidic plaque at the site of the culprit stenosis (P=0.02), a wider lipid arc (p=0.003) and a higher prevalence of thin-cap fibroatheroma (p=0.004).