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ARNI in HFrEF—One-Centre Experience in the Era before the 2021 ESC HF Recommendations

Author

Listed:
  • Rafał Niemiec

    (Upper Silesian Medical Centre, First Department of Cardiology, School of Medicine in Katowice, Medical University of Silesia, 40-055 Katowice, Poland
    These authors contributed equally to this work.)

  • Irmina Morawska

    (Upper Silesian Medical Centre, First Department of Cardiology, School of Medicine in Katowice, Medical University of Silesia, 40-055 Katowice, Poland)

  • Maria Stec

    (Upper Silesian Medical Centre, Students’ Scientific Society of the First Department of Cardiology, School of Medicine in Katowice, Medical University of Silesia, 40-055 Katowice, Poland)

  • Wiktoria Kuczmik

    (Upper Silesian Medical Centre, Students’ Scientific Society of the First Department of Cardiology, School of Medicine in Katowice, Medical University of Silesia, 40-055 Katowice, Poland)

  • Andrzej S. Swinarew

    (Faculty of Computer Science and Material Science, Institute of Material Science, University of Silesia in Katowice, 40-055 Katowice, Poland
    Institute of Sport Sciences, The Jerzy Kukuczka Academy of Physical Education, 40-065 Katowice, Poland)

  • Arkadiusz Stanula

    (Institute of Sport Sciences, The Jerzy Kukuczka Academy of Physical Education, 40-065 Katowice, Poland)

  • Katarzyna Mizia-Stec

    (Upper Silesian Medical Centre, First Department of Cardiology, School of Medicine in Katowice, Medical University of Silesia, 40-055 Katowice, Poland
    These authors contributed equally to this work.)

Abstract

Background: Sacubitril/valsartan, an angiotensin receptor–neprilysin inhibitor (ARNI), has demonstrated a survival benefit and reduces heart failure hospitalization in patients with heart failure with reduced left ventricular ejection fraction (HFrEF); however, our experience in this field is limited. This study aimed to summarize a real clinical practice of the use of ARNI in HFrEF patients hospitalized due to HFrEF in the era before the 2021 ESC HF recommendations, as well as assess their clinical outcome with regard to ARNI administration. Methods and Materials: Overall, 613 patients with HFrEF hospitalized in 2018–2020 were enrolled into a retrospective one-centre cross-sectional analysis. The study population was categorized into patients receiving (82/13.4%) and not-receiving (531/82.6%) ARNI. Clinical outcomes defined as rehospitalization, number of rehospitalizations, time to the first rehospitalization and death from any cause were analysed in the 1–2 year follow-up in the ARNI and non-ARNI groups, matched as to age and LVEF. Results: Clinical characteristics revealed the following differences between ARNI and non-ARNI groups: A higher percentage of cardiovascular implantable electronic devices (CIED) ( p = 0.014) and defibrillators with cardiac resynchronization therapy (CRT-D) ( p = 0.038), higher frequency of atrial fibrillation ( p = 0.002) and history of stroke ( p = 0.024) were in the ARNI group. The percentage of patients with HFrEF NYHA III/IV presented an increasing trend to be higher in the ARNI (64.1%) as compared to the non-ARNI group (51.5%, p = 0.154). Incidence of rehospitalization, number of rehospitalizations and time to the first rehospitalization were comparable between the groups. There were no differences between the numbers of deaths of any cause in the ARNI (28%) and non-ARNI (28%) groups. The independent negative predictor of death in the whole population of ARNI and non-ARNI groups was the coexistence of coronary artery disease (CAD) (beta= −0.924, HR 0.806, p = 0.011). Conclusions: Our current positive experience in ARNI therapy is limited to extremely severe patients with HFrEF. Regardless of the more advanced HF and HF comorbidities, the patients treated with ARNI presented similar mortality and rehospitalizations as the patients treated by standard therapy.

Suggested Citation

  • Rafał Niemiec & Irmina Morawska & Maria Stec & Wiktoria Kuczmik & Andrzej S. Swinarew & Arkadiusz Stanula & Katarzyna Mizia-Stec, 2022. "ARNI in HFrEF—One-Centre Experience in the Era before the 2021 ESC HF Recommendations," IJERPH, MDPI, vol. 19(4), pages 1-12, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2089-:d:748274
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    References listed on IDEAS

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    1. Stephen F Weng & Jenna Reps & Joe Kai & Jonathan M Garibaldi & Nadeem Qureshi, 2017. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
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