Suchergebnisse

Aims Left ventricular ejection fraction (EF) is required to categorize heart failure (HF) [i.e. HF with preserved (HFpEF), mid-range (HFmrEF), and reduced (HFrEF) EF] but is often not captured in population-based cohorts or non-HF registries. The aim was to create an algorithm that identifies EF subphenotypes for research purposes. Methods and results We included 42 061 HF patients from the Swedish Heart Failure Registry. As primary analysis, we performed two logistic regression models including 22 variables to predict (i) EF >= vs. = vs. = 50% and 0.76 (95% CI 0.75-0.76) for EF >= 40%. Similar results were achieved for HFrEF and HFpEF in the multinomial model, but the C-statistic for HFmrEF was lower: 0.63 (95% CI 0.63-0.64). The external validation showed similar discriminative ability to the development cohort. Conclusions Routine clinical characteristics could potentially be used to identify different EF subphenotypes in databases where EF is not readily available. Accuracy was good for the prediction of HFpEF and HFrEF but lower for HFmrEF. The proposed algorithm enables more effective research on HF in the big data setting. ; Funding Agencies|Swedish National Board of Health and Welfare; Swedish Association of Local Authorities and Regions; Swedish Society of Cardiology; Swedish Heart-Lung FoundationSwedish Heart-Lung Foundation; Servier, the NetherlandsNetherlands Government; EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart [116074]; Swedish Research CouncilSwedish Research Council [2013-23897-104604-23, 523-2014-2336]; Swedish Heart Lung FoundationSwedish Heart-Lung Foundation [20150557, 20170841]; Stockholm County CouncilStockholm County Council [20140220, 20170112]; UCL Hospitals NIHR Biomedical Research Centre; Dutch Heart Foundation, a part of Facts and Figures

Aims We aimed to derive and validate clinically useful clusters of patients with heart failure with preserved ejection fraction (HFpEF; left ventricular ejection fraction >= 50%). Methods and results We derived a cluster model from 6909 HFpEF patients from the Swedish Heart Failure Registry (SwedeHF) and externally validated this in 2153 patients from the Chronic Heart Failure ESC-guideline based Cardiology practice Quality project (CHECK-HF) registry. In SwedeHF, the median age was 80 [interquartile range 72-86] years, 52% of patients were female and most frequent comorbidities were hypertension (82%), atrial fibrillation (68%), and ischaemic heart disease (48%). Latent class analysis identified five distinct clusters: cluster 1 (10% of patients) were young patients with a low comorbidity burden and the highest proportion of implantable devices; cluster 2 (30%) patients had atrial fibrillation, hypertension without diabetes; cluster 3 (25%) patients were the oldest with many cardiovascular comorbidities and hypertension; cluster 4 (15%) patients had obesity, diabetes and hypertension; and cluster 5 (20%) patients were older with ischaemic heart disease, hypertension and renal failure and were most frequently prescribed diuretics. The clusters were reproduced in the CHECK-HF cohort. Patients in cluster 1 had the best prognosis, while patients in clusters 3 and 5 had the worst age- and sex-adjusted prognosis. Conclusions Five distinct clusters of HFpEF patients were identified that differed in clinical characteristics, heart failure drug therapy and prognosis. These results confirm the heterogeneity of HFpEF and form a basis for tailoring trial design to individualized drug therapy in HFpEF patients. ; Funding Agencies|Swedish National Board of Health and Welfare; Swedish Association of Local Authorities and Regions; Swedish Society of Cardiology; Swedish Heart-Lung FoundationSwedish Heart-Lung Foundation; Servier, the NetherlandsNetherlands Government; EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart grant [116074]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2013-23897-104604-23, 523-2014-2336]; Swedish Heart Lung FoundationSwedish Heart-Lung Foundation [20150557, 20170841]; Stockholm County CouncilStockholm County Council [20140220, 20170112]; UCL Hospitals NIHR Biomedical Research Centre; Dutch Heart Foundation, as part of Facts and Figures