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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 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.

Introduction Left atrial appendage (LAA) is a finger-like muscular extension of the left atrium (LA). It acts as blood reservoir, modulates LA pressure and elicits adaptive responses to stress. Several LAA shapes and variants have been described. We report a case of congenital absence of LAA diagnosed by transesophageal echocardiography (TEE) and confirmed by computed tomography (CT). Case report A 55-year-old male patient with history of hypertension was admitted to our hospital for an episode of atrial fibrillation (AF). TEE was performed and showed normal LA volume, absence of thrombi and spontaneous echo contrast in the LA, but, despite acquiring three-dimensional (3D) en-face views, we were unable to identify LAA. Congenital absence of LAA was suspected and multidetector cardiac CT confirmed the abnormality. Discussion Congenital absence of LAA is an extremely rare condition: only eighteen cases were described on a review of the literature published in 2020. Associations with other congenital abnormalities or with atrial fibrillation have been reported, but a causal relationship cannot be established. Multiple imaging modalities can be used to define the anatomy and function of LAA, to detect or rule out in situ thrombosis and to guide catheter-based therapies, but the most widely used technique is TEE. Among intracardiac thrombi in patients with non-valvular atrial fibrillation, more than 90% are identified in the LAA, meaning that the remaining 10% may develop in other sites of the LA. The congenital absence of LAA could be considered similar to LAA percutaneous occlusion or surgical exclusion; because of the few published randomized controlled trials, the current ESC guidelines on atrial fibrillation are unclear about the true thrombotic risk in these cases. Since LAA percutaneous occlusion is actually indicated only in cases of contraindications for long-term anticoagulant treatment, in our opinion in patients with non-valvular AF, LAA congenital absence, high stroke risk and low bleeding risk, anticoagulant therapy should be considered. Conclusion Congenital absence of LAA is an extremely rare condition and its pathophysiological role, especially with regard to antithrombotic therapy in patients with AF, is still not well defined.

Aims: Sacubitril/valsartan has changed the treatment of heart failure with reduced ejection fraction (HFrEF), due to the positive effects on morbidity and mortality, partly mediated by left ventricular (LV) reverse remodelling (LVRR). The aim of this multicenter study was to identify echocardiographic predictors of LVRR after sacubitril/valsartan administration. Methods and results: Patients with HFrEF requiring therapy with sacubitril/valsartan from 13 Italian centres were included. Echocardiographic parameters including LV global longitudinal strain (GLS) and global peak atrial longitudinal strain by speckle tracking echocardiography were measured to find the predictors of LVRR [=LV end-systolic volume reduction ≥10% and ejection fraction (LVEF) improvement ≥10% at follow-up] at 6month follow-up as the primary endpoint. Changes in symptoms [New York Heart Association (NYHA) class] and neurohormonal activations [N-terminal pro-brain natriuretic peptide (NT-proBNP)] were also evaluated as secondary endpoints; 341 patients (excluding patients with poor acoustic windows and missing data) were analysed (mean age: 65±10years; 18% female, median LVEF 30% [inter-quartile range: 25-34]). At 6month follow-up, 82 (24%) patients showed early complete response (LVRR and LVEF≥35%), 55 (16%) early incomplete response (LVRR and LVEF&nbsp