Dataset Information

A registry-based algorithm to predict ejection fraction in patients with heart failure.

ABSTRACT: 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. <50% and (ii) EF? vs. <40%. In the secondary analysis, we performed a multivariable multinomial analysis with 22 variables to create a model for all three separate EF subphenotypes: HFrEF vs. HFmrEF vs. HFpEF. The models were validated in the database from the CHECK-HF study, a cross-sectional survey of 10 627 patients from the Netherlands. The C-statistic (discrimination) was 0.78 [95% confidence interval (CI) 0.77-0.78] for EF ?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.

SUBMITTER: Uijl A

PROVIDER: S-EPMC7524089 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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A registry-based algorithm to predict ejection fraction in patients with heart failure.

Uijl Alicia A Lund Lars H LH Vaartjes Ilonca I Brugts Jasper J JJ Linssen Gerard C GC Asselbergs Folkert W FW Hoes Arno W AW Dahlström Ulf U Koudstaal Stefan S Savarese Gianluigi G

ESC heart failure 20200617 5

<h4>Aims</h4>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.<h4>Methods and results</h4>We included 42 061 HF patients from the Swedish Heart Failure Registry. As primary analysis, we performed two logistic regression m ...[more]

PMID: 32548911

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Project description:AimsThis study aimed to investigate the prognostic value of dynamic changes in left ventricular ejection fraction (EF) for cardiovascular (CV) outcomes in an all-comer heart failure (HF) population with reduced EF (HFrEF, EF < 40%). We sought to identify independent factors related to improvement in EF and to identify risk factors for increased risk of CV events in the subgroups of improved EF (iEF) and non-improved EF (niEF), respecively.Methods and resultsThis is a retrospective sub-analysis from the REDEAL HF trial, which included consecutive patients with chronic HF who were hospitalized from July 2009 to December 2017. Baseline and follow-up echocardiography data (interval ≥12 months) of 573 consecutive patients with HFrEF were analysed. iEF was defined as absolute improvement in EF ≥ 10% and follow-up EF over 40%. The primary endpoint was defined as a composite endpoint of cardiovascular (CV) death, CV hospitalization, or appropriate implantable cardioverter-defibrillator (ICD) therapy for ventricular arrhythmia. EF improved in 37.2% of patients with HFrEF during follow-up (median period of 17 months). iEF was independently associated with shorter HF duration (>4 vs. ≤4 years, odd ratio [OR] = 0.477, 95% CI 0.305-0.745), no coronary artery disease (CAD vs. no CAD, OR = 0.583, 95% CI 0.396-0.858), and no ICD implantation (ICD vs. no ICD, OR = 0.341, 95% CI 0.228-0.511). Compared with niEF, iEF was significantly and independently associated with lower all-cause mortality (22.1% vs. 31.1%, P = 0.019; hazard ratio [HR] = 0.674, 95% CI 0.469-0.968), lower CV mortality (8.9% vs. 16.1%, P = 0.015; HR = 0.539, 95% CI 0.317-0.916), and lower CV events risk (27.2% vs. 49.2%, P < 0.001; HR 0.519, 95% CI 0.381-0.708), after adjustment for age, sex, duration of HF, and other clinical risk factors. Hypertension (HR = 2.452, P = 0.032) and elevated N-terminal prohormone of brain natriuretic peptide (NT-proBNP >1153 pg/mL, HR = 4.372, P < 0.001) were identified as independent risk factors for CV events in the iEF subgroup. ICD implantation (HR = 1.533, P = 0.011), elevated NT-proBNP (HR = 1.626, P = 0.018), increased left atrial volume index (HR = 1.461, P = 0.021), reduced lateral mitral annular plane systolic excursion (HR = 1.478, P = 0.025), and reduced tricuspid plane systolic excursion (HR = 1.491, P = 0.039) were identified as risk factors for CV events in the niEF subgroup.ConclusionsImprovement in EF is independently related to the longer survival and lower CV related mortality and hospitalization rate of HFrEF. Elevated baseline NT-proBNP is identified as the strongest prognostic factor associated with increased CV events risk in HFrEF patients both with and without improved EF, regardless of age, sex, duration of HF, and other clinical risk factors.

Project description:AimsGuidelines for management of patients with heart failure with mid-range ejection fraction [HFmrEF; left ventricular EF (LVEF) 41-49%] do not exist. Disagreement exists whether HFmrEF should be considered a distinct group. The aim of this study is to examine characteristics of patients with HFmrEF with HF with reduced EF (HFrEF; LVEF ≤ 40%) or preserved EF (HFpEF; LVEF ≥ 50%).Methods and resultsWe examined data collected in the American College of Cardiology's National Cardiovascular Data Registry (NCDR) Practice Innovation and Clinical Excellence (PINNACLE) Registry® for first HF patient visits between 1 May 2008 and 30 June 2016. Analysis was performed using ANOVA F-tests (or Kruskal-Wallis tests for non-normally distributed variables) for continuous parameters and χ2 tests for nominal covariates at the first diagnosed HF visit. Given the NCDR PINNACLE Registry® is a US-based registry, we opted to define HFmrEF as per the US guidelines, which define HFmrEF as LVEF 41-49% in contrast to European guidelines, which define HFmrEF as LVEF 40-49%. Among 1 103 386 patients with available data, 36.1% (N = 398 228) had HFrEF, 7.5% (N = 82 292) had HFmrEF, and 56.5% (N = 622 866) had HFpEF. Compared with patients with HFrEF or HFpEF, patients with HFmrEF had more prevalent coronary and peripheral artery disease and more history of myocardial infarction, percutaneous coronary intervention, or coronary artery bypass surgery (all P < 0.001). Patients with HFmrEF were also more likely to have atrial fibrillation/flutter, diabetes, and chronic kidney disease and to have a history of tobacco use (both P < 0.001). Among those with EF assessment prior to this analysis, only 4.8% (N = 1032) previously had HFrEF that improved to HFmrEF; 32.9% (N = 7072) had HFpEF previously and progressed to HFmrEF. Those patients who transitioned from HFpEF to HFmrEF had considerably more complex profiles and were less aggressively managed compared with those who remained with HFmrEF (all P < 0.001).ConclusionsIn this large descriptive analysis, patients with HFmrEF had an atherothrombotic phenotype distinct from other forms of HF. Interventions aimed at treating coronary ischaemia and addressing prevalent risk factors may play a particularly important role in the management of patients with HFmrEF.

Dataset Information

A registry-based algorithm to predict ejection fraction in patients with heart failure.

Publications

A registry-based algorithm to predict ejection fraction in patients with heart failure.

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