Estimated Population Prevalence of Heart Failure with Reduced Ejection Fraction in Spain, According to DAPA-HF Study Criteria.
Anna CampsJuan Francisco DelgadoNúria FarreHelena Tizón-MarcosJesus Alvarez-GarciaJuan CincaIrene R DéganoJaume MarrugatPublished in: Journal of clinical medicine (2020)
Heart failure (HF) is one of the main causes of morbidity, mortality, and high healthcare costs. Dapagliflozin, a sodium-glucose cotransporter-2 (SGLT2) inhibitor, reduced cardiovascular mortality and hospitalization for HF compared to placebo in patients with chronic HF, and reduced ejection fraction (EF) in the Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure (DAPA-HF) study. Our aim was to estimate the number of patients with DAPA-HF characteristics in Spain. Our literature review identified epidemiological studies whose objective was to quantify the prevalence of HF and its comorbidities in Spain. We estimated the prevalence of HF with reduced EF, of New York Heart Association (NYHA) functional class II-IV, and with a glomerular filtration rate (GFR) ≥ 30 mL/min/1.73 m². In this population, we analysed the prevalence of diabetes using data from the REDINSCOR (Spanish Network for Heart Failure) registry. Our estimations indicate there are 594,684 patients ≥45 years old with HF in Spain (2.6% of this population age group), of which 52.4%, 84.0%, and 93.9% have reduced EF, are NYHA II-IV, and have a GFR ≥ 30 mL/min/1.73 m², respectively. By our calculations, approximately 245,789 Spanish patients would meet the DAPA-HF patient profile, and therefore could benefit from the protective cardiovascular effects of dapagliflozin.
Keyphrases
- acute heart failure
- heart failure
- risk factors
- healthcare
- newly diagnosed
- ejection fraction
- left ventricular
- prognostic factors
- atrial fibrillation
- type diabetes
- case report
- cardiovascular events
- randomized controlled trial
- clinical trial
- patient reported outcomes
- coronary artery disease
- big data
- cardiac resynchronization therapy
- adipose tissue
- deep learning
- metabolic syndrome
- open label
- patient reported
- double blind
- data analysis