Cluster-Randomized Trial Comparing Ambulatory Decision Support Tools to Improve Heart Failure Care
BACKGROUND:Mineralocorticoid receptor antagonists (MRA) are under-prescribed for patients with heart failure with reduced ejection fraction (HFrEF). OBJECTIVE:To compare effectiveness of two automated, electronic health record (EHR)-embedded tools vs. usual care on MRA prescribing in eligible patients with HFrEF. METHODS:BETTER CARE-HF (Building Electronic Tools To Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) was a three-arm, pragmatic, cluster-randomized trial comparing the effectiveness of an alert during individual patient encounters vs. a message about multiple patients between encounters vs. usual care on MRA prescribing. We included adult patients with HFrEF, no active MRA prescription, no contraindication to MRA, and an outpatient cardiologist in a large health system. Patients were cluster-randomized by cardiologist (60 per arm). RESULTS:The study included 2,211 patients (alert: 755, message: 812, usual care [control]: 644), with average age 72.2 years, average EF 33%, who were predominantly male (71.4%) and White (68.9%). New MRA prescribing occurred in 29.6% of patients in the alert arm, 15.6% in the message arm, and 11.7% in the control arm. The alert more than doubled MRA prescribing compared to control (RR: 2.53, 95% CI: 1.77-3.62, p<0.0001), and improved MRA prescribing compared to the message (RR: 1.67, 95% CI: 1.21-2.29, p=0.002). The number of patients with alert needed to result in an additional MRA prescription was 5.6. CONCLUSIONS:An automated, patient-specific, EHR-embedded alert increased MRA prescribing compared to both a message and usual care. Our findings highlight the potential for EHR-embedded tools to substantially increase prescription of life-saving therapies for HFrEF. (NCT05275920).
Association of perioral dermatitis with facial mask usage during the COVID-19 pandemic: A retrospective study
Design and pilot implementation for the BETTER CARE-HF trial: A pragmatic cluster-randomized controlled trial comparing two targeted approaches to ambulatory clinical decision support for cardiologists
BACKGROUND:Beart failure with reduced ejection fraction (HFrEF) is a leading cause of morbidity and mortality. However, shortfalls in prescribing of proven therapies, particularly mineralocorticoid receptor antagonist (MRA) therapy, account for several thousand preventable deaths per year nationwide. Electronic clinical decision support (CDS) is a potential low-cost and scalable solution to improve prescribing of therapies. However, the optimal timing and format of CDS tools is unknown. METHODS AND RESULTS/RESULTS:We developed two targeted CDS tools to inform cardiologists of gaps in MRA therapy for patients with HFrEF and without contraindication to MRA therapy: (1) an alert that notifies cardiologists at the time of patient visit, and (2) an automated electronic message that allows for review between visits. We designed these tools using an established CDS framework and findings from semistructured interviews with cardiologists. We then pilot tested both CDS tools (n = 596 patients) and further enhanced them based on additional semistructured interviews (n = 11 cardiologists). The message was modified to reduce the number of patients listed, include future visits, and list date of next visit. The alert was modified to improve noticeability, reduce extraneous information on guidelines, and include key information on contraindications. CONCLUSIONS:The BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) trial aims to compare the effectiveness of the alert vs. the automated message vs. usual care on the primary outcome of MRA prescribing. To our knowledge, no study has directly compared the efficacy of these two different types of electronic CDS interventions. If effective, our findings can be rapidly disseminated to improve morbidity and mortality for patients with HFrEF, and can also inform the development of future CDS interventions for other disease states. (Trial registration: Clinicaltrials.gov NCT05275920).
The impact of COVID-19 monoclonal antibodies on clinical outcomes: A retrospective cohort study
DISCLAIMER/CONCLUSIONS:In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE/OBJECTIVE:Despite progress in the treatment of coronavirus disease 2019 (COVID-19), including the development of monoclonal antibodies (mAbs), more clinical data to support the use of mAbs in outpatients with COVID-19 is needed. This study is designed to determine the impact of bamlanivimab, bamlanivimab/etesevimab, or casirivimab/imdevimab on clinical outcomes within 30 days of COVID-19 diagnosis. METHODS:A retrospective cohort study was conducted at a single academic medical center with 3 campuses in Manhattan, Brooklyn, and Long Island, NY. Patients 12 years of age or older who tested positive for COVID-19 or were treated with a COVID-19-specific therapy, including COVID-19 mAb therapies, at the study site between November 24, 2020, and May 15, 2021, were included. The primary outcomes included rates of emergency department (ED) visit, inpatient admission, intensive care unit (ICU) admission, or death within 30 days from the date of COVID-19 diagnosis. RESULTS:A total of 1,344 mAb-treated patients were propensity matched to 1,344 patients with COVID-19 patients who were not treated with mAb therapy. Within 30 days of diagnosis, among the patients who received mAb therapy, 101 (7.5%) presented to the ED and 79 (5.9%) were admitted. Among the patients who did not receive mAb therapy, 165 (12.3%) presented to the ED and 156 (11.6%) were admitted (relative risk [RR], 0.61 [95% CI, 0.50-0.75] and 0.51 [95% CI, 0.40-0.64], respectively). Four mAb patients (0.3%) and 2.64 control patients (0.2%) were admitted to the ICU (RR, 01.51; 95% CI, 0.45-5.09). Six mAb-treated patients (0.4%) and 3.37 controls (0.3%) died and/or were admitted to hospice (RR, 1.61; 95% CI, 0.54-4.83). mAb therapy in ambulatory patients with COVID-19 decreases the risk of ED presentation and hospital admission within 30 days of diagnosis.
Missed opportunities in medical therapy for patients with heart failure in an electronically-identified cohort
BACKGROUND:National registries reveal significant gaps in medical therapy for patients with heart failure and reduced ejection fraction (HFrEF), but may not accurately (or fully) characterize the population eligible for therapy. OBJECTIVE:We developed an automated, electronic health record-based algorithm to identify HFrEF patients eligible for evidence-based therapy, and extracted treatment data to assess gaps in therapy in a large, diverse health system. METHODS:In this cross-sectional study of all NYU Langone Health outpatients with EFâ€‰â‰¤â€‰40% on echocardiogram and an outpatient visit from 3/1/2019 to 2/29/2020, we assessed prescription of the following therapies: beta-blocker (BB), angiotensin converting enzyme inhibitor (ACE-I)/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), and mineralocorticoid receptor antagonist (MRA). Our algorithm accounted for contraindications such as medication allergy, bradycardia, hypotension, renal dysfunction, and hyperkalemia. RESULTS:We electronically identified 2732 patients meeting inclusion criteria. Among those eligible for each medication class, 84.8% and 79.7% were appropriately prescribed BB and ACE-I/ARB/ARNI, respectively, while only 23.9% and 22.7% were appropriately prescribed MRA and ARNI, respectively. In adjusted models, younger age, cardiology visit and lower EF were associated with increased prescribing of medications. Private insurance and Medicaid were associated with increased prescribing of ARNI (ORâ€‰=â€‰1.40, 95% CIâ€‰=â€‰1.02-2.00; and ORâ€‰=â€‰1.70, 95% CIâ€‰=â€‰1.07-2.67). CONCLUSIONS:We observed substantial shortfalls in prescribing of MRA and ARNI therapy to ambulatory HFrEF patients. Subspecialty care setting, and Medicaid insurance were associated with higher rates of ARNI prescribing. Further studies are warranted to prospectively evaluate provider- and policy-level interventions to improve prescribing of these evidence-based therapies.
Telemedicine and Home Pregnancy Testing for iPLEDGE: A Survey of Clinician Perspectives [Letter]
Assessing Patient Satisfaction with Live-Interactive Teledermatology Visits During the COVID-19 Pandemic: A Survey Study
Development of an Electronic Trigger to Identify Delayed Follow-up HbA1c Testing for Patients with Uncontrolled Diabetes
Challenges for dermatologists during the COVID-19 pandemic: A qualitative study
Burnout is increasing in all fields of medicine, including dermatology. The coronavirus disease 2019 (COVID-19) pandemic presented new and additional challenges for dermatologists.
KIR-HLA Interactions Lack Clinical Utility in Matched Unrelated Donor Transplantation for AML: An Analysis of the CIBMTR and DRST Registries [Meeting Abstract]
[Formula presented] Background: The interaction between donor killer immunoglobulin-like receptor (KIR) and recipient HLA has been postulated to enhance the graft-versus-leukemia effect in allogeneic hematopoietic cell transplantation (HCT) for acute myeloid leukemia (AML). Historically, analyses of individual interactions between single KIR and their respective HLA ligands have yielded conflicting findings, and the clinical importance of these interactions in the matched unrelated donor (MUD) setting remains controversial. Here, we applied a systematic approach, studying both a wide range of KIR and class I HLA interactions at the single-receptor level as well as the most prevalent KIR genotypes in a large cohort of AML patients undergoing MUD transplantation.
Method(s): We included adult AML patients in complete remission transplanted from an 8/8-HLA MUD between 2010 and 2016 and reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). Donor-KIR and respective recipient-HLA ligand interactions were assessed in multivariable Cox proportional hazard models for standard transplantation outcomes. To account for the compound effect of simultaneous KIR/HLA interactions, we applied a combinatorial approach to identify aggregate KIR genotypes based on combinations of individual KIR genes. The most frequently observed donor-KIR genotypes, in combination with recipient ligands, were evaluated for association with relapse using multivariable regression. Those associated (p < 0.01) with relapse risk were evaluated for differential relapse in a DRST (German stem-cell registry)/Collaborative Biobank cohort of donors/patients with similar inclusion criteria.
Result(s): A total of 2,036 transplantations from the CIBMTR were included. Most patients were treated in first complete remission (78%) and received myeloablative conditioning (59%). We first studied eight known interactions between donor KIR and their respective HLA ligands (Figure A). Only donor-KIR-2DL2+/recipient-HLA-C1+ was associated with reduced relapse (compared to donor-KIR-2DL2-/recipient-HLA-C1+, hazard ratio [HR] 0.80 [95% confidence interval 0.67-0.94], p=0.008). However, no difference was found when comparing HLA-C group pairs among KIR-2DL2+ recipients, suggesting this finding is confounded by co-occurrence of other receptors. There are hundreds of possible KIR gene combinations (i.e. genotypes), which are typically clustered into two primary haplotypes, A and B. To study the cumulative effect of donor KIR, we investigated nine prevalent KIR genotypes (Figure B) and identified three significantly associated with relapse risk. (1) Donor KIR genotype 5 in all recipients irrespective of their HLA (Figure C, n = 138/2,036) and (2) genotype 3 in HLA-Bw4/x recipients (Figure D, n = 51/1,198) had significantly decreased relapse risk (HR 0.53 [0.37-0.78], p=0.002 and 0.34 [0.15-0.75], p=0.008, respectively). (3) KIR genotype 2 was associated with greater relapse in HLA-C1-homozygous recipients (Figure E, n = 87/836, HR 1.62 [1.14-2.30], p=0.007). These findings were not confirmed in the external European dataset (n = 796, Figure 1C-E); however, this cohort differed in ways that might affect the importance of KIRs, such as the higher frequency of reduced intensity conditioning (74% vs. 41%) and in-vivo T-cell depletion (79% vs. 37%).
Conclusion(s): Our systematic investigation in two large AML cohorts receiving MUD allogenic HCT did not validate any association between individual KIR-HLA interactions and clinical outcomes. A combinatorial approach identified combinations potentially protective against relapse, however these could not be confirmed in a second dataset. Overall, our findings do not support KIR-informed donor selection using the approaches outlined here. [Formula presented] Disclosures: Shouval: Medexus: Consultancy. Kroeger: AOP Pharma: Honoraria; Gilead/Kite: Honoraria; Riemser: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Jazz: Honoraria, Research Funding; Sanofi: Honoraria; Neovii: Honoraria, Research Funding; Novartis: Honoraria. Horowitz: Daiicho Sankyo: Research Funding; Allovir: Consultancy; Miltenyi Biotech: Research Funding; Medac: Research Funding; Kite/Gilead: Research Funding; Genentech: Research Funding; Jazz Pharmaceuticals: Research Funding; Janssen: Research Funding; Kiadis: Research Funding; CSL Behring: Research Funding; Gamida Cell: Research Funding; bluebird bio: Research Funding; Bristol-Myers Squibb: Research Funding; Amgen: Research Funding; Astellas: Research Funding; Chimerix: Research Funding; GlaxoSmithKline: Research Funding; Novartis: Research Funding; Magenta: Consultancy, Research Funding; Actinium: Research Funding; Mesoblast: Research Funding; Omeros: Research Funding; Orca Biosystems: Research Funding; Pfizer, Inc: Research Funding; Pharmacyclics: Research Funding; Regeneron: Research Funding; Sanofi: Research Funding; Seattle Genetics: Research Funding; Shire: Research Funding; Sobi: Research Funding; Stemcyte: Research Funding; Takeda: Research Funding; Tscan: Research Funding; Vertex: Research Funding; Vor Biopharma: Research Funding; Xenikos: Research Funding. Malmberg: Merck: Research Funding; Vycellix: Consultancy; Fate Therapeutics: Consultancy, Research Funding. Miller: Sanofi: Membership on an entity's Board of Directors or advisory committees; Magenta: Membership on an entity's Board of Directors or advisory committees; ONK Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Vycellix: Consultancy; GT Biopharma: Consultancy, Patents & Royalties, Research Funding; Fate Therapeutics, Inc: Consultancy, Patents & Royalties, Research Funding; Wugen: Membership on an entity's Board of Directors or advisory committees. Mohty: Sanofi: Honoraria, Research Funding; Pfizer: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Jazz: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Gilead: Honoraria; Celgene: Honoraria, Research Funding; Bristol Myers Squibb: Honoraria; Astellas: Honoraria; Amgen: Honoraria; Adaptive Biotechnologies: Honoraria. Romee: Crispr Therapeutics: Research Funding; Glycostem: Membership on an entity's Board of Directors or advisory committees. Schetelig: Roche: Honoraria, Other: lecture fees; Novartis: Honoraria, Other: lecture fees; BMS: Honoraria, Other: lecture fees; Abbvie: Honoraria, Other: lecture fees; AstraZeneca: Honoraria, Other: lecture fees; Gilead: Honoraria, Other: lecture fees; Janssen: Honoraria, Other: lecture fees. Weisdorf: Fate Therapeutics: Research Funding; Incyte: Research Funding. Koreth: Biolojic Design: Other: Scientific Advisory Board; Mallinckrodt: Other: Scientific Advisory Board; Cugene: Other: Scientific Advisory Board; Moderna: Consultancy; Amgen: Consultancy; EMD Serono/Merck: Consultancy; Gentibio Inc.: Consultancy; Miltenyi Biotec: Research Funding; BMS: Research Funding; Clinigen Labs: Research Funding; Regeneron: Research Funding; Equillium: Research Funding.