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10.1016/j.cardfail.2020.09.467 http://scihub22266oqcxt.onion/10.1016/j.cardfail.2020.09.467 C7527179!7527179!C7527179     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 300.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 300.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Card+Fail 2020 ; 26 (10): S162-3 Nephropedia Template TP {{tp|p=C7527179|t=2020. Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study |pdf=|usr=}}{{C7527179}} gab.com Text Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study JO: J Card Fail http://www.kidney.de/pget.php?&tw=1&pmid=C7527179 #FOAvip 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 (? 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. Twit Text FOAVip Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study ????J Card Fail http://www.kidney.de/pget.php?&tw=1&pmid=C7527179 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=C7527179 #FOAvip English Wikipedia <ref>{{Cite pmid|C7527179}}</ref> Feasibility of Virtual Optimization of Guideline Directed Medical Therapy in Hospitalized Patients with HFrEF During the Covid-19 Pandemic: The IMPLEMENT-HF Pilot Study #MMPMIDC7527179 Bhatt AS; Varshney A; Moscone A; Cunningham J; Jering K; Sinnenberg L; Nekoui M; Buckley L; Cook B; Dempsey J; Kelly J; Knowles D; Lupi K; Malloy R; Matta L; Rhoten M; Hinchey E; McElrath E; Alobaidly M; Amato M; Ulbricht C; Ting C; Bernier T; Choudhry N; Adler DS; Vaduganathan M J Card Fail 2020[Oct]; 26 (10): S162-3 PMIDC7527179show ga 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 (? 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. äFeasibility of Virtual Optimization of Guideline Directed Medical Ther(epmc).pdf DeepDyve Pubget Overpricing