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Aims
There is an ongoing discussion whether the categorization of patients with heart failure according to left ventricular ejection fraction (LVEF) is scientifically justified and clinically relevant. Major efforts are directed towards the identification of appropriate cut-off values to correctly allocate heart failure-specific pharmacotherapy. Alternatively, an LVEF continuum without definite subgroups is discussed. This study aimed to evaluate the natural distribution of LVEF in patients presenting with acutely decompensated heart failure and to identify potential subgroups of LVEF in male and female patients.
Methods and results
We identified 470 patients (mean age 75 ± 11 years, n = 137 female) hospitalized for acute heart failure in whom LVEF could be quantified by Simpson's method in an in-hospital echocardiogram. Non-parametric modelling revealed a bimodal shape of the LVEF distribution. Parametric modelling identified two clusters suggesting two LVEF peaks with mean (variance) of 61% (9%) and 31% (10%), respectively. Sub-differentiation by sex revealed a sex-specific bimodal clustering of LVEF. The respective threshold differentiating between ‘high’ and ‘low’ LVEF was 45% in men and 52% in women.
Conclusions
In patients presenting with acute heart failure, LVEF clustered in two subgroups and exhibited profound sex-specific distributional differences. These findings might enrich the scientific process to identify distinct subgroups of heart failure patients, which might each benefit from respectively tailored (pharmaco)therapies.
Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.