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Aims: Left ventricular (LV) blood flow is determined by intraventricular pressure gradients (IVPG). Changes in blood flow initiate remodelling and precede functional decline. Novel cardiac magnetic resonance (CMR) post-processing LV-IVPG analysis might provide a sensitive marker of LV function in dilated cardiomyopathy (DCM). Therefore, the aim of our study was to evaluate LV-IVPG patterns and their prognostic value in DCM., Methods and Results: LV-IVPGs between apex and base were measured on standard CMR cine images in DCM patients (n = 447) from the Maastricht Cardiomyopathy registry. Major adverse cardiovascular events, including heart failure hospitalisations, life-threatening arrhythmias, and sudden/cardiac death, occurred in 66 DCM patients (15%). A temporary LV-IVPG reversal during systolic-diastolic transition, leading to a prolonged transition period or slower filling, was present in 168 patients (38%). In 14%, this led to a reversal of blood flow, which predicted outcome corrected for univariable predictors [hazard ratio (HR) = 2.57, 95% confidence interval (1.01-6.51), P = 0.047]. In patients without pressure reversal (n = 279), impaired overall LV-IVPG [HR = 0.91 (0.83-0.99), P = 0.033], systolic ejection force [HR = 0.91 (0.86-0.96), P < 0.001], and E-wave decelerative force [HR = 0.83 (0.73-0.94), P = 0.003] predicted outcome, independent of known predictors (age, sex, New York Heart Association class ≥ 3, LV ejection fraction, late gadolinium enhancement, LV-longitudinal strain, left atrium (LA) volume-index, and LA-conduit strain)., Conclusion: Pressure reversal during systolic-diastolic transition was observed in one-third of DCM patients, and reversal of blood flow direction predicted worse outcome. In the absence of pressure reversal, lower systolic ejection force, E-wave decelerative force (end of passive LV filling), and overall LV-IVPG are powerful predictors of outcome, independent of clinical and imaging parameters., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aims: While electrocardiogram (ECG) characteristics have been associated with life-threatening ventricular arrhythmias (LTVA) in dilated cardiomyopathy (DCM), they typically rely on human-derived parameters. Deep neural networks (DNNs) can discover complex ECG patterns, but the interpretation is hampered by their 'black-box' characteristics. We aimed to detect DCM patients at risk of LTVA using an inherently explainable DNN., Methods and Results: In this two-phase study, we first developed a variational autoencoder DNN on more than 1 million 12-lead median beat ECGs, compressing the ECG into 21 different factors (F): FactorECG. Next, we used two cohorts with a combined total of 695 DCM patients and entered these factors in a Cox regression for the composite LTVA outcome, which was defined as sudden cardiac arrest, spontaneous sustained ventricular tachycardia, or implantable cardioverter-defibrillator treated ventricular arrhythmia. Most patients were male (n = 442, 64%) with a median age of 54 years [interquartile range (IQR) 44-62], and median left ventricular ejection fraction of 30% (IQR 23-39). A total of 115 patients (16.5%) reached the study outcome. Factors F8 (prolonged PR-interval and P-wave duration, P < 0.005), F15 (reduced P-wave height, P = 0.04), F25 (increased right bundle branch delay, P = 0.02), F27 (P-wave axis P < 0.005), and F32 (reduced QRS-T voltages P = 0.03) were significantly associated with LTVA., Conclusion: Inherently explainable DNNs can detect patients at risk of LTVA which is mainly driven by P-wave abnormalities., Competing Interests: Conflicts of interest: None declared., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aims: This study assessed the prognostic implications of mechanical atrial dysfunction in heart failure with preserved ejection fraction (HFpEF) patients with different stages of atrial fibrillation (AF) in detail., Methods and Results: HFpEF patients (n = 258) systemically underwent an extensive clinical characterization, including 24-h Holter monitoring and speckle-tracking echocardiography. Patients were categorized according to rhythm and stages of AF: 112 with no history of AF (no AF), 56 with paroxysmal AF (PAF), and 90 with sustained (persistent/permanent) AF (SAF). A progressive decrease in mechanical atrial function was seen: left atrial reservoir strain (LASr) 30.5 ± 10.5% (no AF), 22.3 ± 10.5% (PAF), and 13.9 ± 7.8% (SAF), P < 0.001. Independent predictors for lower LASr values were AF, absence of chronic obstructive pulmonary disease, higher N-terminal-pro hormone B-type natriuretic peptide, left atrial volume index, and relative wall thickness, lower left ventricular global longitudinal strain, and echocardiographic signs of elevated left ventricular filling pressure. LASr was an independent predictor of adverse outcome (hazard ratio per 1% decrease =1.049, 95% confidence interval 1.014-1.085, P = 0.006), whereas AF was not when the multivariable model included LASr. Moreover, LASr mediated the adverse outcome associated with AF in HFpEF (P = 0.008)., Conclusion: Mechanical atrial dysfunction has a possible greater prognostic role in HFpEF compared to AF status alone. Mechanical atrial dysfunction is a predictor of adverse outcome independently of AF presence or stage, and may be an underlying mechanism (mediator) for the worse outcome associated with AF in HFpEF. This may suggest mechanical atrial dysfunction plays a crucial role in disease progression in HFpEF patients with AF, and possibly also in HFpEF patients without AF., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aims: The dilated cardiomyopathy (DCM) phenotype is the result of combined genetic and acquired triggers. Until now, clinical decision-making in DCM has mainly been based on ejection fraction (EF) and NYHA classification, not considering the DCM heterogenicity. The present study aimed to identify patient subgroups by phenotypic clustering integrating aetiologies, comorbidities, and cardiac function along cardiac transcript levels, to unveil pathophysiological differences between DCM subgroups., Methods and Results: We included 795 consecutive DCM patients from the Maastricht Cardiomyopathy Registry who underwent in-depth phenotyping, comprising extensive clinical data on aetiology and comorbodities, imaging and endomyocardial biopsies. Four mutually exclusive and clinically distinct phenogroups (PG) were identified based upon unsupervised hierarchical clustering of principal components: [PG1] mild systolic dysfunction, [PG2] auto-immune, [PG3] genetic and arrhythmias, and [PG4] severe systolic dysfunction. RNA-sequencing of cardiac samples (n = 91) revealed a distinct underlying molecular profile per PG: pro-inflammatory (PG2, auto-immune), pro-fibrotic (PG3; arrhythmia), and metabolic (PG4, low EF) gene expression. Furthermore, event-free survival differed among the four phenogroups, also when corrected for well-known clinical predictors. Decision tree modelling identified four clinical parameters (auto-immune disease, EF, atrial fibrillation, and kidney function) by which every DCM patient from two independent DCM cohorts could be placed in one of the four phenogroups with corresponding outcome (n = 789; Spain, n = 352 and Italy, n = 437), showing a feasible applicability of the phenogrouping., Conclusion: The present study identified four different DCM phenogroups associated with significant differences in clinical presentation, underlying molecular profiles and outcome, paving the way for a more personalized treatment approach., (© The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Cardiology.)