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For decades, cardiologists have largely underestimated the role of the right heart in heart failure due to left heart disease. Nowadays, the importance of evaluating right ventricular (RV) structure and function in left heart failure is well documented and this concept has been emphasized in the most recent heart failure guidelines. However, several relevant questions remain unanswered such as the following: (a) which imaging technique (standard or 3D echocardiography or strain imaging or cardiac magnetic resonance) and, more, which parameters should be used to grade the severity of RV dysfunction? (b) do less widespread and less applied diagnostic tools such as cardiopulmonary stress testing and bioelectrical impedance analysis play a role in this field? (c) are there specific biochemical aspects of RV failure? (d) why notion of pathophysiology of heart and lung interaction are so well appreciated at an academic level but are not applied in the clinical setting? The present review has been prepared by the Heart Failure (HF) working group of the Italian Society of Cardiology and its main objective is to improve our understanding on RV dysfunction in heart failure., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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.

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/m 2 ) 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., (© 2022 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Background: Sacubitril/valsartan improves outcome in patients with heart failure (HF) with reduced left ventricular (LV) ejection fraction (EF, HFrEF). However, little is known about possible mechanisms underlying this favourable effect., Purpose: To assess changes in echocardiographically-derived hemodynamic profiles induced by sacubitril/valsartan and their impact on outcome., Methods: In this multicenter, open-label study, 727 HFrEF outpatients underwent comprehensive echocardiography at baseline (before starting sacubitril/valsartan) and after 12 months. Estimated LV filling pressure (E/e') and cardiac index (CI, l/min/m 2 ) were combined to determine 4 hemodynamic profiles: profile-A (normal-flow/normal-pressure); profile-B (low-flow/normal-pressure); profile-C: (normal-flow/high-pressure); profile-D: (low-flow/high-pressure). Changes among categories were recorded, and their associations with rates of the composite of death/HF-hospitalization were assessed by multivariable Cox analysis., Results: At baseline, 29% had profile-A, 15% had profile-B, 32% profile-C, and 24% profile-D. After 12 months, the hemodynamic profile improved in 53% of patients (all profile-A achievers, or profile-D patients achieving either C or B profile), while it remained unchanged in 39% patients and worsened in 9%. Prevalence of improved profile progressively increased with increasing dose of sacubitril/valsartan (P < 0.0001). After the second echocardiography, patients were followed up 12.6 ± 7.6 months: event-rate was lower in patients with improved profile (12.3%, 95%CI: 9.4-16.1) compared to patients in whom hemodynamic profile remained unchanged (29.9%, 24.0-37.3) or worsened (31.2%, 20.7-46.9, P < 0.0001). Improved hemodynamic profile was associated with favourable outcome independent of LVEF and other covariates (HR 0.65, 95%CI: 0.45-0.95, P < 0.05)., Conclusion: In HFrEF patients, the beneficial prognostic effects of sacubitril/valsartan are associated with improvement in hemodynamic conditions., (Copyright © 2022 Elsevier B.V. All rights reserved.)