IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i4p2089-d748274.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/4/2089/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/4/2089/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mirza Rizwan Sajid & Bader A. Almehmadi & Waqas Sami & Mansour K. Alzahrani & Noryanti Muhammad & Christophe Chesneau & Asif Hanif & Arshad Ali Khan & Ahmad Shahbaz, 2021. "Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches," IJERPH, MDPI, vol. 18(23), pages 1-16, November.
    2. Salvatore Tedesco & Martina Andrulli & Markus Åkerlund Larsson & Daniel Kelly & Antti Alamäki & Suzanne Timmons & John Barton & Joan Condell & Brendan O’Flynn & Anna Nordström, 2021. "Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults," IJERPH, MDPI, vol. 18(23), pages 1-18, December.
    3. Ajay Dev & Sanjay Kumar Malik, 2021. "Artificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data: ABC-DNN for Prediction of Stroke," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 12(5), pages 67-83, September.
    4. Feihan Lu & Yao Zheng & Harrington Cleveland & Chris Burton & David Madigan, 2018. "Bayesian hierarchical vector autoregressive models for patient-level predictive modeling," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-27, December.
    5. Shinya Suzuki & Takeshi Yamashita & Tsuyoshi Sakama & Takuto Arita & Naoharu Yagi & Takayuki Otsuka & Hiroaki Semba & Hiroto Kano & Shunsuke Matsuno & Yuko Kato & Tokuhisa Uejima & Yuji Oikawa & Minor, 2019. "Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-14, September.
    6. Ying Wang & Zhicheng Du & Wayne R. Lawrence & Yun Huang & Yu Deng & Yuantao Hao, 2019. "Predicting Hepatitis B Virus Infection Based on Health Examination Data of Community Population," IJERPH, MDPI, vol. 16(23), pages 1-13, December.
    7. Shelda Sajeev & Stephanie Champion & Alline Beleigoli & Derek Chew & Richard L. Reed & Dianna J. Magliano & Jonathan E. Shaw & Roger L. Milne & Sarah Appleton & Tiffany K. Gill & Anthony Maeder, 2021. "Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
    8. Emily J MacKay & Michael D Stubna & Corey Chivers & Michael E Draugelis & William J Hanson & Nimesh D Desai & Peter W Groeneveld, 2021. "Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-14, June.
    9. Woo Suk Hong & Adrian Daniel Haimovich & R Andrew Taylor, 2018. "Predicting hospital admission at emergency department triage using machine learning," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-13, July.
    10. Adrian Richter & Julia Truthmann & Jean-François Chenot & Carsten Oliver Schmidt, 2021. "Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data—An Interpretable Machine Learning Approach," IJERPH, MDPI, vol. 18(22), pages 1-14, November.
    11. Gian Luca Di Tanna & Heidi Wirtz & Karen L Burrows & Gary Globe, 2020. "Evaluating risk prediction models for adults with heart failure: A systematic literature review," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-23, January.
    12. Dohyun Kim & Sungmin You & Soonwon So & Jongshill Lee & Sunhyun Yook & Dong Pyo Jang & In Young Kim & Eunkyoung Park & Kyeongwon Cho & Won Chul Cha & Dong Wook Shin & Baek Hwan Cho & Hoon-Ki Park, 2018. "A data-driven artificial intelligence model for remote triage in the prehospital environment," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-14, October.
    13. Hoa Thi Nguyen & Claudia M. Denkinger & Stephan Brenner & Lisa Koeppel & Lucia Brugnara & Robin Burk & Michael Knop & Till Bärnighausen & Andreas Deckert & Manuela De Allegri, 2023. "Cost and cost-effectiveness of four different SARS-CoV-2 active surveillance strategies: evidence from a randomised control trial in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(9), pages 1545-1559, December.
    14. Sharan Srinivas, 2020. "A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
    15. Syed Waseem Abbas Sherazi & Jang-Whan Bae & Jong Yun Lee, 2021. "A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary ," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    16. Stephen F Weng & Luis Vaz & Nadeem Qureshi & Joe Kai, 2019. "Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-22, March.
    17. Alexander Engels & Katrin C Reber & Ivonne Lindlbauer & Kilian Rapp & Gisela Büchele & Jochen Klenk & Andreas Meid & Clemens Becker & Hans-Helmut König, 2020. "Osteoporotic hip fracture prediction from risk factors available in administrative claims data – A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-14, May.
    18. Cynthia Rudin & Berk Ustun, 2018. "Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice," Interfaces, INFORMS, vol. 48(5), pages 449-466, October.
    19. Pablo Gonzalez Ginestet & Ales Kotalik & David M. Vock & Julian Wolfson & Erin E. Gabriel, 2021. "Stacked inverse probability of censoring weighted bagging: A case study in the InfCareHIV Register," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 51-65, January.
    20. Eunji Koh & Younghoon Kim, 2022. "Risk Association of Liver Cancer and Hepatitis B with Tree Ensemble and Lifestyle Features," IJERPH, MDPI, vol. 19(22), pages 1-16, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2089-:d:748274. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.