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Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study

INTRODUCTION: Implementation of GDMT for HFrEF remains low. We assessed the feasibility of a virtual GDMT Team for optimization of GDMT during hospitalization for non-CV conditions. HYPOTHESIS: A GDMT Team will improve GDMT optimization compared with usual care. METHODS: Consecutive hospitalized pat...

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Autores principales: Bhatt, Ankeet S., Varshney, Anubodh, Moscone, Alea, Cunningham, Jonathan, Jering, Karola, Sinnenberg, Lauren, Nekoui, Mahan, Buckley, Leo, Cook, Brian, Dempsey, Jillian, Kelly, Julie, Knowles, Danielle, Lupi, Kenneth, Malloy, Rhynn, Matta, Lina, Rhoten, Megan, Hinchey, Emily, McElrath, Erin, Alobaidly, Maryam, Amato, Mary, Ulbricht, Catherine, Ting, Clara, Bernier, Thomas, Choudhry, Niteesh, Adler, Dale S., Vaduganathan, Muthiah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527179/
http://dx.doi.org/10.1016/j.cardfail.2020.09.467
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author Bhatt, Ankeet S.
Varshney, Anubodh
Moscone, Alea
Cunningham, Jonathan
Jering, Karola
Sinnenberg, Lauren
Nekoui, Mahan
Buckley, Leo
Cook, Brian
Dempsey, Jillian
Kelly, Julie
Knowles, Danielle
Lupi, Kenneth
Malloy, Rhynn
Matta, Lina
Rhoten, Megan
Hinchey, Emily
McElrath, Erin
Alobaidly, Maryam
Amato, Mary
Ulbricht, Catherine
Ting, Clara
Bernier, Thomas
Choudhry, Niteesh
Adler, Dale S.
Vaduganathan, Muthiah
author_facet Bhatt, Ankeet S.
Varshney, Anubodh
Moscone, Alea
Cunningham, Jonathan
Jering, Karola
Sinnenberg, Lauren
Nekoui, Mahan
Buckley, Leo
Cook, Brian
Dempsey, Jillian
Kelly, Julie
Knowles, Danielle
Lupi, Kenneth
Malloy, Rhynn
Matta, Lina
Rhoten, Megan
Hinchey, Emily
McElrath, Erin
Alobaidly, Maryam
Amato, Mary
Ulbricht, Catherine
Ting, Clara
Bernier, Thomas
Choudhry, Niteesh
Adler, Dale S.
Vaduganathan, Muthiah
author_sort Bhatt, Ankeet S.
collection PubMed
description INTRODUCTION: Implementation of GDMT for HFrEF remains low. We assessed the feasibility of a virtual GDMT Team for optimization of GDMT during hospitalization for non-CV conditions. HYPOTHESIS: A GDMT Team will improve GDMT optimization compared with usual care. METHODS: Consecutive hospitalized patients with HFrEF≤40% were prospectively identified. Patients with critical illness, cardiology consult, de-novo HF, COVID-19 & SBP ≤90mmHg were excluded. February 3 to March 1, 2020 served as a pre-intervention period during which patients were screened, but did not receive GDMT Team interventions. From March 2 to June 21, 2020, a pharmacist-physician team provided up to 1 suggestion daily for GDMT optimization (evidence-based ß-blockers, ACEi/ARB/ARNI, & MRA) to treating teams based on an evidence-based algorithm. The primary outcome of a composite GDMT optimization score, the net of positive therapeutic changes (+1 for new initiations/uptitrations) & negative therapeutic changes (-1 for discontinuations/downtitrations) during hospitalization, was compared between the pre- vs. post-intervention periods. Multivariable linear regression models were built adjusting associations for clinical factors. Safety outcomes requiring intervention or GDMT downtitration were identified. RESULTS: Of 187 encounters, 84 (45%) met eligibility criteria: 28 pre-intervention, 56 post-intervention. Mean age was 68±11 yrs, 70% men, and 61% White. Of 88 GDMT Team suggestions, 49 (56%) were followed by discharge. During the intervention, cumulative COVID-19 hospitalizations rose from 0 to 11085 in MA. Mean GDMT optimization score was -0.14 (95% CI: -0.58 to +0.30) pre-intervention & +0.64 (95% CI: +0.35 to +0.93) post-intervention (P=0.004). In a model inclusive of demographics, comorbidities, vital signs, potassium levels, eGFR, & LVEF, the intervention was the only factor associated with higher GDMT optimization score (β coeff 0.89; P=0.008). Safety events included 1 instance each of AKI, hyperkalemia, bradycardia, & hypotension. CONCLUSION: Admission for non-CV conditions is a feasible setting for GDMT optimization. A virtual GDMT Team was associated with improved GDMT; this implementation strategy warrants testing in a prospective RCT.
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spelling pubmed-75271792020-10-01 Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study Bhatt, Ankeet S. Varshney, Anubodh Moscone, Alea Cunningham, Jonathan Jering, Karola Sinnenberg, Lauren Nekoui, Mahan Buckley, Leo Cook, Brian Dempsey, Jillian Kelly, Julie Knowles, Danielle Lupi, Kenneth Malloy, Rhynn Matta, Lina Rhoten, Megan Hinchey, Emily McElrath, Erin Alobaidly, Maryam Amato, Mary Ulbricht, Catherine Ting, Clara Bernier, Thomas Choudhry, Niteesh Adler, Dale S. Vaduganathan, Muthiah J Card Fail Lbct-002 INTRODUCTION: Implementation of GDMT for HFrEF remains low. We assessed the feasibility of a virtual GDMT Team for optimization of GDMT during hospitalization for non-CV conditions. HYPOTHESIS: A GDMT Team will improve GDMT optimization compared with usual care. METHODS: Consecutive hospitalized patients with HFrEF≤40% were prospectively identified. Patients with critical illness, cardiology consult, de-novo HF, COVID-19 & SBP ≤90mmHg were excluded. February 3 to March 1, 2020 served as a pre-intervention period during which patients were screened, but did not receive GDMT Team interventions. From March 2 to June 21, 2020, a pharmacist-physician team provided up to 1 suggestion daily for GDMT optimization (evidence-based ß-blockers, ACEi/ARB/ARNI, & MRA) to treating teams based on an evidence-based algorithm. The primary outcome of a composite GDMT optimization score, the net of positive therapeutic changes (+1 for new initiations/uptitrations) & negative therapeutic changes (-1 for discontinuations/downtitrations) during hospitalization, was compared between the pre- vs. post-intervention periods. Multivariable linear regression models were built adjusting associations for clinical factors. Safety outcomes requiring intervention or GDMT downtitration were identified. RESULTS: Of 187 encounters, 84 (45%) met eligibility criteria: 28 pre-intervention, 56 post-intervention. Mean age was 68±11 yrs, 70% men, and 61% White. Of 88 GDMT Team suggestions, 49 (56%) were followed by discharge. During the intervention, cumulative COVID-19 hospitalizations rose from 0 to 11085 in MA. Mean GDMT optimization score was -0.14 (95% CI: -0.58 to +0.30) pre-intervention & +0.64 (95% CI: +0.35 to +0.93) post-intervention (P=0.004). In a model inclusive of demographics, comorbidities, vital signs, potassium levels, eGFR, & LVEF, the intervention was the only factor associated with higher GDMT optimization score (β coeff 0.89; P=0.008). Safety events included 1 instance each of AKI, hyperkalemia, bradycardia, & hypotension. CONCLUSION: Admission for non-CV conditions is a feasible setting for GDMT optimization. A virtual GDMT Team was associated with improved GDMT; this implementation strategy warrants testing in a prospective RCT. Published by Elsevier Inc. 2020-10 2020-09-30 /pmc/articles/PMC7527179/ http://dx.doi.org/10.1016/j.cardfail.2020.09.467 Text en Copyright © 2020 Published by Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Lbct-002
Bhatt, Ankeet S.
Varshney, Anubodh
Moscone, Alea
Cunningham, Jonathan
Jering, Karola
Sinnenberg, Lauren
Nekoui, Mahan
Buckley, Leo
Cook, Brian
Dempsey, Jillian
Kelly, Julie
Knowles, Danielle
Lupi, Kenneth
Malloy, Rhynn
Matta, Lina
Rhoten, Megan
Hinchey, Emily
McElrath, Erin
Alobaidly, Maryam
Amato, Mary
Ulbricht, Catherine
Ting, Clara
Bernier, Thomas
Choudhry, Niteesh
Adler, Dale S.
Vaduganathan, Muthiah
Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study
title Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study
title_full Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study
title_fullStr Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study
title_full_unstemmed Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study
title_short Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study
title_sort feasibility of virtual optimization of guideline directed medical therapy in hospitalized patients with hfref during the covid-19 pandemic: the implement-hf pilot study
topic Lbct-002
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527179/
http://dx.doi.org/10.1016/j.cardfail.2020.09.467
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