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Abstract Aims The HFA‐PEFF score was developed to optimize diagnosis and to aid in early recognition of heart failure (HF) with preserved ejection fraction (HFpEF) in patients who present with HF‐like symptoms. Recognizing early‐HFpEF phenogroups is essential to better understand progression towards overt HFpEF and pave the way for early intervention and treatment. Whether the HFA‐PEFF domain scores can identify ‘early‐HFpEF’ phenogroups remains unknown. The aims of this pilot study are to (i) identify distinct phenogroups by cluster analysis of HFA‐PEFF domain scores in subjects that present with HF‐like symptoms and (ii) study whether these phenogroups may be associated with distinct blood proteome profiles. Methods and results Subjects referred to the Cardiology Centers of the Netherlands, location Utrecht, with non‐acute possibly cardiac‐related symptoms (such as dyspnoea or fatigue) were prospectively enrolled in the HELPFul cohort (N = 507) and were included in the current analysis. Inclusion criteria for this study were (i) age ≥ 45 years and (ii) a left ventricular ejection fraction (LVEF) ≥ 50%, in the absence of a history of HF, coronary artery disease, congenital heart disease, or any previous cardiac interventions. Multinominal‐based clustering with latent class model using the HFA‐PEFF domain scores (functional, structural, and biomarker scores) as input was used to detect distinct phenotypic clusters. For each bootstrapping run, the 92 Olink proteins were analysed for their association with the identified phenogroups. Four distinct phenogroups were identified in the current analysis (validated by bootstrapping 1000×): (i) no left ventricular diastolic dysfunction (no LVDD, N = 102); (ii) LVDD with functional left ventricular (LV) abnormalities (N = 204); (iii) LVDD with functional and structural LV abnormalities (N = 204); and (iv) LVDD with functional and structural LV abnormalities and elevated BNP (N = 107). The HFA‐PEFF total score risk categories significantly differed between the phenogroups (P

Abstract Aims Heart failure (HF) represents a clinical syndrome resulting from different aetiologies and degrees of heart diseases. Among these, a key role is played by primary heart muscle disease (cardiomyopathies), which are the combination of multifactorial environmental insults in the presence or absence of a known genetic predisposition. The aim of the Maastricht Cardiomyopathy registry (mCMP‐registry; NCT04976348) is to improve (early) diagnosis, risk stratification, and management of cardiomyopathy phenotypes beyond the limits of left ventricular ejection fraction (LVEF). Methods and results The mCMP‐registry is an investigator‐initiated prospective registry including patient characteristics, diagnostic measurements performed as part of routine clinical care, treatment information, sequential biobanking, quality of life and economic impact assessment, and regular follow‐up. All subjects aged ≥16 years referred to the cardiology department of the Maastricht University Medical Center (MUMC+) for HF‐like symptoms or cardiac screening for cardiomyopathies are eligible for inclusion, irrespective of phenotype or underlying causes. Informed consented subjects will be followed up for 15 years. Two central approaches will be used to answer the research questions related to the aims of this registry: (i) a data‐driven approach to predict clinical outcome and response to therapy and to identify clusters of patients who share underlying pathophysiological processes; and (ii) a hypothesis‐driven approach in which clinical parameters are tested for their (incremental) diagnostic, prognostic, or therapeutic value. The study allows other centres to easily join this initiative, which will further boost research within this field. Conclusions The broad inclusion criteria, systematic routine clinical care data‐collection, extensive study‐related data‐collection, sequential biobanking, and multi‐disciplinary approach gives the mCMP‐registry a unique opportunity to improve diagnosis, risk stratification, and management of HF and (early) cardiomyopathy phenotypes beyond the LVEF limits.

Abstract Aims Whether and how iron deficiency (ID) impacts patients with heart failure (HF) with preserved ejection fraction (HFpEF) remain unclear. The aim of our study was to investigate the impact of ID on functional status, exercise capacity, and prognosis in HFpEF. Methods and results The study population consisted of 300 HFpEF patients. ID was defined as serum ferritin

Abstract Aims Global longitudinal strain (GLS) has become an alternative to left ventricular ejection fraction (LVEF) to determine systolic function of the heart. The absence of cut‐off values is one of the limitations preventing full clinical implementation. The aim of this study is to determine a cut‐off value of GLS for an increased risk of adverse events in individuals with a normal LVEF. Methods and results Echocardiographic images of 502 subjects (52% female, mean age 48 ± 15) with an LVEF ≥ 55% were analysed using speckle tracking‐based GLS. The primary endpoint was cardiovascular death or cardiac hospitalization. The analysis of Cox models with splines was performed to visualize the effect of GLS on outcome. A cut‐off value was suggested by determining the optimal specificity and sensitivity. The median GLS was −22.2% (inter‐quartile range −20.0 to −24.9%). In total, 35 subjects (7%) had a cardiac hospitalization and/or died because of cardiovascular disease during a follow‐up of 40 (5–80) months. There was a linear correlation between the risk for adverse events and GLS value. Subjects with a normal LVEF and a GLS between −22.9% and −20.9% had a mildly increased risk (hazard ratio 1.01–2.0) for cardiac hospitalization or cardiovascular mortality, and the risk was doubled for subjects with a GLS of −20.9% and higher. The optimal specificity and sensitivity were determined at a GLS value of −20.0% (hazard ratio 2.49; 95% confidence interval: 1.71–3.61). Conclusions There is a strong correlation between cardiac adverse events and GLS values in subjects with a normal LVEF. In our single‐centre study, −20.0% was determined as a cut‐off value to identify subjects at risk. A next step should be to integrate GLS values in a multi‐parametric model.