Impact of SGLT2 Inhibitors on Very Elderly Population with Heart Failure with Reduce Ejection Fraction: Real Life Data.
Jorge Balaguer GermánMarcelino Cortés GarcíaCarlos Rodríguez LópezJose María Romero OteroJose Antonio Esteban ChapelAntonio José Bollas BecerraLuis Nieto-RocaMikel Taibo UrquíaAna María Pello LázaroJosé Tuñón FernándezPublished in: Biomedicines (2024)
(1) Background: The validation of new lines of therapy for the elderly is required due to the progressive ageing of the world population and scarce evidence in elderly patients with HF with reduced ejection fraction (HFrEF). The purpose of our study is to analyze the effect of SGLT2 inhibitors (SGLT2i) in this subgroup of patients. (2) Methods: A single-center, real-world observational study was performed. We consecutively enrolled all patients aged ≥ 75 years diagnosed with HFrEF and for treatment with SGLT2i, and considered the theoretical indications. (3) Results: A total of 364 patients were recruited, with a mean age of 84.1 years. At inclusion, the mean LVEF was 29.8%. Median follow-up was 33 months, and there were 122 deaths. A total of 55 patients were under SGLT2i treatment. A multivariate Cox logistic regression test for all-cause mortality was performed, and only SGLT2i (HR 0.39 [0.19-0.82]) and glomerular filtration rate (HR 0.98 [0.98-0.99]) proved to be protective factors. In parallel, we conducted a propensity-score-matched analysis, where a significant reduction in all-cause mortality was associated with the use of SGLT2i treatment (HR 0.39, [0.16-0.97]). (4) Conclusions: Treatment with SGLT2i in elderly patients with HFrEF was associated with a lower rate of all-cause mortality. Our data show that SGLT2i therapy could improve prognosis in the elderly with HFrEF in a real-world study.
Keyphrases
- ejection fraction
- end stage renal disease
- heart failure
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- aortic stenosis
- middle aged
- randomized controlled trial
- stem cells
- clinical trial
- multiple sclerosis
- deep learning
- coronary artery disease
- big data
- smoking cessation
- patient reported
- open label
- phase iii