Randomized Evaluation of a Remote Management Program to Improve Guideline-Directed Medical Therapy: The DRIVE Trial.

BACKGROUND: Several SGLT2i (sodium-glucose transport protein 2 inhibitors) and GLP1-RA (glucagon-like peptide-1 receptor agonists) reduce cardiovascular events and improve kidney outcomes in patients with type 2 diabetes; however, utilization remains low despite guideline recommendations. METHODS: A randomized, remote implementation trial in the Mass General Brigham network enrolled patients with type 2 diabetes with increased cardiovascular or kidney risk. Patients eligible for, but not prescribed, SGLT2i or GLP1-RA were randomly assigned to simultaneous virtual patient education with concurrent prescription of SGLT2i or GLP1-RA (ie, Simultaneous) or 2 months of virtual education followed by medication prescription (ie, Education-First) delivered by a multidisciplinary team driven by nonlicensed navigators and clinical pharmacists who prescribed SGLT2i or GLP1-RA using a standardized treatment algorithm. The primary outcome was the proportion of patients with prescriptions for either SGLT2i or GLP1-RA by 6 months. RESULTS: Between March 2021 and December 2022, 200 patients were randomized. The mean age was 66.5 years; 36.5% were female, and 22.0% were non-White. Overall, 30.0% had cardiovascular disease, 5.0% had cerebrovascular disease, and 1.5% had both. Mean estimated glomerular filtration rate was 77.9 mL/(min‧1.73 m2), and mean urine/albumin creatinine ratio was 88.6 mg/g. After 2 months, 69 of 200 (34.5%) patients received a new prescription for either SGLT2i or GLP1-RA: 53.4% of patients in the Simultaneous arm and 8.3% of patients in the Education-First arm (P<0.001). After 6 months, 128 of 200 (64.0%) received a new prescription: 69.8% of patients in the Simultaneous arm and 56.0% of patients in Education-First (P<0.001). Patient self-report of taking SGLT2i or GLP1-RA within 6 months of trial entry was similarly greater in the Simultaneous versus Education-First arm (69 of 116 [59.5%] versus 37 of 84 [44.0%]; P<0.001) Median time to first prescription was 24 (interquartile range [IQR], 13-50) versus 85 days (IQR, 65-106), respectively (P<0.001). CONCLUSIONS: In this randomized trial, a remote, team-based program identifies patients with type 2 diabetes and high cardiovascular or kidney risk, provides virtual education, prescribes SGLT2i or GLP1-RA, and improves guideline-directed medical therapy. These findings support greater utilization of virtual team-based approaches to optimize chronic disease management. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06046560.

Retrieval Augmented Generation Enabled Generative Pre-Trained Transformer 4 (GPT-4) Performance for Clinical Trial Screening.

Background: Subject screening is a key aspect of all clinical trials; however, traditionally, it is a labor-intensive and error-prone task, demanding significant time and resources. With the advent of large language models (LLMs) and related technologies, a paradigm shift in natural language processing capabilities offers a promising avenue for increasing both quality and efficiency of screening efforts. This study aimed to test the Retrieval-Augmented Generation (RAG) process enabled Generative Pretrained Transformer Version 4 (GPT-4) to accurately identify and report on inclusion and exclusion criteria for a clinical trial. Methods: The Co-Operative Program for Implementation of Optimal Therapy in Heart Failure (COPILOT-HF) trial aims to recruit patients with symptomatic heart failure. As part of the screening process, a list of potentially eligible patients is created through an electronic health record (EHR) query. Currently, structured data in the EHR can only be used to determine 5 out of 6 inclusion and 5 out of 17 exclusion criteria. Trained, but non-licensed, study staff complete manual chart review to determine patient eligibility and record their assessment of the inclusion and exclusion criteria. We obtained the structured assessments completed by the study staff and clinical notes for the past two years and developed a workflow of clinical note-based question answering system powered by RAG architecture and GPT-4 that we named RECTIFIER (RAG-Enabled Clinical Trial Infrastructure for Inclusion Exclusion Review). We used notes from 100 patients as a development dataset, 282 patients as a validation dataset, and 1894 patients as a test set. An expert clinician completed a blinded review of patients' charts to answer the eligibility questions and determine the "gold standard" answers. We calculated the sensitivity, specificity, accuracy, and Matthews correlation coefficient (MCC) for each question and screening method. We also performed bootstrapping to calculate the confidence intervals for each statistic. Results: Both RECTIFIER and study staff answers closely aligned with the expert clinician answers across criteria with accuracy ranging between 97.9% and 100% (MCC 0.837 and 1) for RECTIFIER and 91.7% and 100% (MCC 0.644 and 1) for study staff. RECTIFIER performed better than study staff to determine the inclusion criteria of "symptomatic heart failure" with an accuracy of 97.9% vs 91.7% and an MCC of 0.924 vs 0.721, respectively. Overall, the sensitivity and specificity of determining eligibility for the RECTIFIER was 92.3% (CI) and 93.9% (CI), and study staff was 90.1% (CI) and 83.6% (CI), respectively. Conclusion: GPT-4 based solutions have the potential to improve efficiency and reduce costs in clinical trial screening. When incorporating new tools such as RECTIFIER, it is important to consider the potential hazards of automating the screening process and set up appropriate mitigation strategies such as final clinician review before patient engagement.

Methods, rationale, and design for a remote pharmacist and navigator-driven disease management program to improve guideline-directed medical therapy in patients with type 2 diabetes at elevated cardiovascular and/or kidney risk.

AIM: Describe the rationale for and design of Diabetes Remote Intervention to improVe use of Evidence-based medications (DRIVE), a remote medication management program designed to initiate and titrate guideline-directed medical therapy (GDMT) in patients with type 2 diabetes (T2D) at elevated cardiovascular (CV) and/or kidney risk by leveraging non-physician providers. METHODS: An electronic health record based algorithm is used to identify patients with T2D and either established atherosclerotic CV disease (ASCVD), high risk for ASCVD, chronic kidney disease, and/or heart failure within our health system. Patients are invited to participate and randomly assigned to either simultaneous education and medication management, or a period of education prior to medication management. Patient navigators (trained, non-licensed staff) are the primary points of contact while a pharmacist or nurse practitioner reviews and authorizes each medication initiation and titration under an institution-approved collaborative drug therapy management protocol with supervision from a cardiologist and/or endocrinologist. Patient engagement is managed through software to support communication, automation, workflow, and standardization. CONCLUSION: We are testing a remote, navigator-driven, pharmacist-led, and physician-overseen management strategy to optimize GDMT for T2D as a population-level strategy to close the gap between guidelines and clinical practice for patients with T2D at elevated CV and/or kidney risk.

Implementation of a Scalable Online Weight Management Programme in Clinical Settings: Protocol for the PROPS 2.0 Programme (Partnerships for Reducing Overweight and Obesity with Patient-Centered Strategies 2.0).

INTRODUCTION: There is an urgent need for scalable strategies for treating overweight and obesity in clinical settings. PROPS 2.0 (Partnerships for Reducing Overweight and Obesity with Patient-Centered Strategies 2.0) aims to adapt and implement the combined intervention from the PROPS Study at scale, in a diverse cross-section of patients and providers. METHODS AND ANALYSIS: We are implementing PROPS 2.0 across a variety of clinics at Brigham and Women's Hospital, targeting enrolment of 5000 patients. Providers can refer patients or patients can self-refer. Eligible patients must be ≥20 years old and have a body mass index (BMI) of ≥30 kg/m2 or a BMI of 25-29.9 kg/m2 plus another cardiovascular risk factor or obesity-related condition. After enrolment, patients register for the RestoreHealth online programme/app (HealthFleet Inc.) and participate for 12 months. Patients can engage with the programme and receive personalized feedback from a coach. Patient navigators help to enrol patients, enter updates in the electronic health record, and refer patients to additional resources. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework is guiding the evaluation. ETHICS AND DISSEMINATION: The Mass General Brigham Human Research Committee approved this protocol. An implementation guide will be created and disseminated, to help other sites adopt the intervention in the future. TRIAL REGISTRATION NUMBER: NCT0555925.

Postoperative outcomes, predictors and trends of mortality and morbidity in patients undergoing hip fracture surgery with underlying aortic stenosis: a nationwide inpatient sample analysis.

BACKGROUND: Hip fractures frequently necessitate hospitalization, especially among patients aged 75 and above who might concurrently suffer from aortic stenosis (AS). This study focuses on postoperative outcomes, potential determinants of morbidity and mortality, as well as evolving trends in patients with AS undergoing surgical repair of hip fractures. METHODS: A retrospective analysis of the Nationwide Inpatient Sample from 2008 to 2019 was conducted. Hip fracture cases were identified, and a subgroup with AS was isolated using the ICD-9 and ICD-10 diagnostic codes. We compared baseline characteristics, postoperative in-hospital outcomes and trends in mortality and morbidity between patients with and without AS. RESULTS: From the dataset, 2,834,919 patients with hip fracture were identified on weighted analysis. Of these, 94,270 (3.3%) were found to have concurrent AS. The AS cohort was characterized by higher mean age and elevated burden of cardiovascular comorbidities, such as coronary artery disease, peripheral vascular disease, pulmonary hypertension, congestive heart failure and cardiac arrhythmias. Postoperative mortality following hip fracture surgery was greater in the AS groups compared to non-AS group (3.3% vs 1.57%, p < 0.001). Risk factors such as congestive heart failure (OR, 2.3[CI, 2.1-2.6]), age above 85 years (OR, 3.2[CI, 2.2-4.7]), cardiac arrhythmias (OR, 2.4[CI, 2.2-2.6]), end-stage renal disease (OR, 3.4[CI, 2.7-4.1]), malnutrition (OR, 2.3[CI, 2.1-2.7]) and AS (OR, 1.2[CI, 1.08-1.5] were associated with increased adjusted odds of postoperative mortality. AS was linked to higher adjusted odds of postoperative mortality (OR, 1.2 [CI, 1.1-1.5]) and complications such as acute myocardial infarction (OR, 1.2 [CI, 1.01-1.4]), cardiogenic shock (OR, 2.0[CI, 1.4-2.9]) and acute renal failure (OR, 1.1[CI, 1.02-1.2]). While hospital stay duration was comparable in both groups (average 5 days), the AS group incurred higher costs (mean $50,673 vs $44,607). The presence of acute heart failure in patients with AS and hip fracture significantly increased mortality, hospital stay, and cost. A notable decline in postoperative in-hospital mortality was observed in both groups from 2008-2019 though the rate of major in-hospital complications rose. CONCLUSION: AS significantly influences postoperative in-hospital mortality and complication rates in hip fracture patients. While a reduction in postoperative mortality was observed in both AS and non-AS cohorts, the incidence of major in-hospital complications increased across both groups.

Remote Cardiovascular Hypertension Program Enhanced Blood Pressure Control During the COVID-19 Pandemic.

Background The COVID-19 pandemic disrupted traditional health care; one fallout was a drastic decrease in blood pressure (BP) assessment. We analyzed the pandemic's impact on our existing remote hypertension management program's effectiveness and adaptability. Methods and Results This retrospective observational analysis evaluated BP control in an entirely remote management program before and during the pandemic. A team of pharmacists, nurse practitioners, physicians, and nonlicensed navigators used an evidence-based clinical algorithm to optimize hypertensive treatment. The algorithm was adapted during the pandemic to simplify BP control. Overall, 1256 patients (605 enrolled in the 6 months before the pandemic shutdown in March 2020 and 651 in the 6 months after) were a median age of 63 years old, 57% female, and 38.2% non-White. Among enrolled patients with sustained hypertension, 51.1% reached BP goals. Within this group, rates of achieving goal BP improved to 94.6% during the pandemic from 75.8% prepandemic (P<0.0001). Mean baseline home BP was 141.7/81.9 mm Hg during the pandemic and 139.8/82.2 prepandemic, and fell ≈16/9 mm Hg in both periods (P<0.0001). Maintenance during the pandemic was achieved earlier (median 11.8 versus 19.6 weeks, P<0.0001), with more frequent monthly calls (8.2 versus 3.1, P<0.0001) and more monthly home BP recordings per patient (32.4 versus 18.9, P<0.0001), compared with the prepandemic period. Conclusions A remote clinical management program was successfully adapted and delivered significant improvements in BP control and increased home BP monitoring despite a nationally observed disruption of traditional hypertension care. Such programs have the potential to transform hypertension management and care delivery.

Results of a Remotely Delivered Hypertension and Lipid Program in More Than 10 000 Patients Across a Diverse Health Care Network.

Importance: Blood pressure (BP) and cholesterol control remain challenging. Remote care can deliver more effective care outside of traditional clinician-patient settings but scaling and ensuring access to care among diverse populations remains elusive. Objective: To implement and evaluate a remote hypertension and cholesterol management program across a diverse health care network. Design, Setting, and Participants: Between January 2018 and July 2021, 20 454 patients in a large integrated health network were screened; 18 444 were approached, and 10 803 were enrolled in a comprehensive remote hypertension and cholesterol program (3658 patients with hypertension, 8103 patients with cholesterol, and 958 patients with both). A total of 1266 patients requested education only without medication titration. Enrolled patients received education, home BP device integration, and medication titration. Nonlicensed navigators and pharmacists, supported by cardiovascular clinicians, coordinated care using standardized algorithms, task management and automation software, and omnichannel communication. BP and laboratory test results were actively monitored. Main Outcomes and Measures: Changes in BP and low-density lipoprotein cholesterol (LDL-C). Results: The mean (SD) age among 10 803 patients was 65 (11.4) years; 6009 participants (56%) were female; 1321 (12%) identified as Black, 1190 (11%) as Hispanic, 7758 (72%) as White, and 1727 (16%) as another or multiple races (including American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, unknown, other, and declined to respond; consolidated owing to small numbers); and 142 (11%) reported a preferred language other than English. A total of 424 482 BP readings and 139 263 laboratory reports were collected. In the hypertension program, the mean (SD) office BP prior to enrollment was 150/83 (18/10) mm Hg, and the mean (SD) home BP was 145/83 (20/12) mm Hg. For those engaged in remote medication management, the mean (SD) clinic BP 6 and 12 months after enrollment decreased by 8.7/3.8 (21.4/12.4) and 9.7/5.2 (22.2/12.6) mm Hg, respectively. In the education-only cohort, BP changed by a mean (SD) -1.5/-0.7 (23.0/11.1) and by +0.2/-1.9 (30.3/11.2) mm Hg, respectively (P < .001 for between cohort difference). In the lipids program, patients in remote medication management experienced a reduction in LDL-C by a mean (SD) 35.4 (43.1) and 37.5 (43.9) mg/dL at 6 and 12 months, respectively, while the education-only cohort experienced a mean (SD) reduction in LDL-C of 9.3 (34.3) and 10.2 (35.5) mg/dL at 6 and 12 months, respectively (P < .001). Similar rates of enrollment and reductions in BP and lipids were observed across different racial, ethnic, and primary language groups. Conclusions and Relevance: The results of this study indicate that a standardized remote BP and cholesterol management program may help optimize guideline-directed therapy at scale, reduce cardiovascular risk, and minimize the need for in-person visits among diverse populations.

I2b2-etl: Python application for importing electronic health data into the informatics for integrating biology and the bedside platform.

MOTIVATION: The i2b2 platform is used at major academic health institutions and research consortia for querying for electronic health data. However, a major obstacle for wider utilization of the platform is the complexity of data loading that entails a steep curve of learning the platform's complex data schemas. To address this problem, we have developed the i2b2-etl package that simplifies the data loading process, which will facilitate wider deployment and utilization of the platform. RESULTS: We have implemented i2b2-etl as a Python application that imports ontology and patient data using simplified input file schemas and provides inbuilt record number de-identification and data validation. We describe a real-world deployment of i2b2-etl for a population-management initiative at MassGeneral Brigham. AVAILABILITY AND IMPLEMENTATION: i2b2-etl is a free, open-source application implemented in Python available under the Mozilla 2 license. The application can be downloaded as compiled docker images. A live demo is available at https://i2b2clinical.org/demo-i2b2etl/ (username: demo, password: Etl@2021). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis.

Analysis of health data typically requires development of queries using structured query language (SQL) by a data-analyst. As the SQL queries are manually created, they are prone to errors. In addition, accurate implementation of the queries depends on effective communication with clinical experts, that further makes the analysis error prone. As a potential resolution, we explore an alternative approach wherein a graphical interface that automatically generates the SQL queries is used to perform the analysis. The latter allows clinical experts to directly perform complex queries on the data, despite their unfamiliarity with SQL syntax. The interface provides an intuitive understanding of the query logic which makes the analysis transparent and comprehensible to the clinical study-staff, thereby enhancing the transparency and validity of the analysis. This study demonstrates the feasibility of using a user-friendly interface that automatically generate SQL for analysis of health data. It outlines challenges that will be useful for designing user-friendly tools to improve transparency and reproducibility of data analysis.

Workflow Automation for a Virtual Hypertension Management Program.

OBJECTIVES: Hypertension is a modifiable risk factor for numerous comorbidities and treating hypertension can greatly improve health outcomes. We sought to increase the efficiency of a virtual hypertension management program through workflow automation processes. METHODS: We developed a customer relationship management (CRM) solution at our institution for the purpose of improving processes and workflow for a virtual hypertension management program and describe here the development, implementation, and initial experience of this CRM system. RESULTS: Notable system features include task automation, patient data capture, multi-channel communication, integration with our electronic health record (EHR), and device integration (for blood pressure cuffs). In the five stages of our program (intake and eligibility screening, enrollment, device configuration/setup, medication titration, and maintenance), we describe some of the key process improvements and workflow automations that are enabled using our CRM platform, like automatic reminders to capture blood pressure data and present these data to our clinical team when ready for clinical decision making. We also describe key limitations of CRM, like balancing out-of-the-box functionality with development flexibility. Among our first group of referred patients, 76% (39/51) preferred email as their communication method, 26/51 (51%) were able to enroll electronically, and 63% of those enrolled (32/51) were able to transmit blood pressure data without phone support. CONCLUSION: A CRM platform could improve clinical processes through multiple pathways, including workflow automation, multi-channel communication, and device integration. Future work will examine the operational improvements of this health information technology solution as well as assess clinical outcomes.

Outcomes of a smartphone-based application with live health-coaching post-percutaneous coronary intervention.

BACKGROUND: The interval between inpatient hospitalization for symptomatic coronary artery disease (CAD) and post-discharge office consultation is a vulnerable period for adverse events. METHODS: Content was customized on a smartphone app-based platform for hospitalized patients receiving percutaneous coronary intervention (PCI) which included education, tracking, reminders and live health coaches. We conducted a single-arm open-label pilot study of the app at two academic medical centers in a single health system, with subjects enrolled 02/2018-05/2019 and 1:3 propensity-matched historical controls from 01/2015-12/2017. To evaluate feasibility and efficacy, we assessed 30-day hospital readmission (primary), outpatient cardiovascular follow-up, and cardiac rehabilitation (CR) enrollment as recorded in the health system. Outcomes were assessed by Cox Proportional Hazards model. FINDINGS: 118 of 324 eligible (36·4%) 21-85 year-old patients who underwent PCI for symptomatic CAD who owned a smartphone or tablet enrolled. Mean age was 62.5 (9·7) years, 87 (73·7%) were male, 40 of 118 (33·9%) had type 2 diabetes mellitus, 68 (57·6%) enrolled underwent PCI for MI and 59 (50·0%) had previously known CAD; demographics were similar among matched historical controls. No significant difference existed in all-cause readmission within 30 days (8·5% app vs 9·6% control, ARR -1.1% absolute difference, 95% CI -7·1-4·8, p = 0·699) or 90 days (16·1% app vs 19·5% control, p = 0.394). Rates of both 90-day CR enrollment (HR 1·99, 95% CI 1·30-3·06) and 1-month cardiovascular follow up (HR 1·83, 95% CI 1·43-2·34) were greater with the app. Weekly engagement at 30- and 90-days, as measured by percentage of weeks with at least one day of completion of tasks, was mean (SD) 73·5% (33·9%) and 63·5% (40·3%). Spearman correlation analyses indicated similar engagement across age, sex, and cardiovascular risk factors. INTERPRETATIONS: A post-PCI smartphone app with live health coaches yielded similarly high engagement across demographics and safely increased attendance in cardiac rehabilitation. Larger prospective randomized controlled trials are necessary to test whether this app improves cardiovascular outcomes following PCI. FUNDING: National Institutes of Health, Boston Scientific. CLINICAL TRIAL REGISTRATION: NCT03416920 (https://clinicaltrials.gov/ct2/show/NCT03416920).

Progression of Myocardial Fibrosis in Nonischemic DCM and Association With Mortality and Heart Failure Outcomes.

OBJECTIVES: The purpose of this study was to assess whether the presence and extent of fibrosis changes over time in patients with nonischemic, dilated cardiomyopathy (DCM) receiving optimal medical therapy and the implications of any such changes on left ventricular ejection fraction (LVEF) and clinical outcomes. BACKGROUND: Myocardial fibrosis on cardiovascular magnetic resonance (CMR) imaging has emerged as important risk marker in patients with DCM. METHODS: In total, 85 patients (age 56 ± 15 years, 45% women) with DCM underwent serial CMR (median interval 1.5 years) for assessment of LVEF and fibrosis. The primary outcome was all-cause mortality; the secondary outcome was a composite of heart failure hospitalization, aborted sudden cardiac death, left ventricular (LV) assist device implantation, or heart transplant. RESULTS: On CMR-1, fibrosis (median 0.0 [interquartile range: 0% to 2.6%]) of LV mass was noted in 34 (40%) patients. On CMR-2, regression of fibrosis was not seen in any patient. Fibrosis findings were stable in 70 (82%) patients. Fibrosis progression (increase >1.8% of LV mass or new fibrosis) was seen in 15 patients (18%); 46% of these patients had no fibrosis on CMR-1. Although fibrosis progression was on aggregate associated with adverse LV remodeling and decreasing LVEF (40 ± 7% to 34 ± 10%; p < 0.01), in 60% of these cases the change in LVEF was minimal (<5%). Fibrosis progression was associated with increased hazards for all-cause mortality (hazard ratio: 3.4 [95% confidence interval: 1.5 to 7.9]; p < 0.01) and heart failure-related complications (hazard ratio: 3.5 [95% confidence interval: 1.5 to 8.1]; p < 0.01) after adjustment for clinical covariates including LVEF. CONCLUSIONS: Once myocardial replacement fibrosis in DCM is present on CMR, it does not regress in size or resolve over time. Progressive fibrosis is often associated with minimal change in LVEF and identifies a high-risk cohort.

Remote Optimization of Guideline-Directed Medical Therapy in Patients With Heart Failure With Reduced Ejection Fraction.

Importance: Optimal treatment of heart failure with reduced ejection fraction (HFrEF) is scripted by treatment guidelines, but many eligible patients do not receive guideline-directed medical therapy (GDMT) in clinical practice. Objective: To determine whether a remote, algorithm-driven, navigator-administered medication optimization program could enhance implementation of GDMT in HFrEF. Design, Setting, and Participants: In this case-control study, a population-based sample of patients with HFrEF was offered participation in a quality improvement program directed at GDMT optimization. Treating clinicians in a tertiary academic medical center who were caring for patients with heart failure and an ejection fraction of 40% or less (identified through an electronic health record-based search) were approached for permission to adjust medical therapy according to a sequential titration algorithm modeled on the current American College of Cardiology/American Heart Association heart failure guidelines. Navigators contacted participants by telephone to direct medication adjustment and conduct longitudinal surveillance of laboratory tests, blood pressure, and symptoms under supervision of a pharmacist, nurse practitioner, and heart failure cardiologist. Patients and clinicians declining to participate served as a control group. Exposures: Navigator-led remote optimization of GDMT compared with usual care. Main Outcomes and Measures: Proportion of patients receiving GDMT in the intervention and control groups at 3 months. Results: Of 1028 eligible patients (mean [SD] values: age, 68 [14] years; ejection fraction, 32% [8%]; and systolic blood pressure, 122 [18] mm Hg; 305 women (30.0%); 892 individuals [86.8%] in New York Heart Association class I and II), 197 (19.2%) participated in the medication optimization program, and 831 (80.8%) continued with usual care as directed by their treating clinicians (585 [56.9%] general cardiologists; 443 [43.1%] heart failure specialists). At 3 months, patients participating in the remote intervention experienced significant increases from baseline in use of renin-angiotensin system antagonists (138 [70.1%] to 170 [86.3%]; P < .001) and ß-blockers (152 [77.2%] to 181 [91.9%]; P < .001) but not mineralocorticoid receptor antagonists (51 [25.9%] to 60 [30.5%]; P = .14). Doses for each category of GDMT also increased from baseline in the intervention group. Among the usual-care group, there were no changes from baseline in the proportion of patients receiving GDMT or the dose of GDMT in any category. Conclusions and Relevance: Remote titration of GDMT by navigators using encoded algorithms may represent an efficient, population-level strategy for rapidly closing the gap between guidelines and clinical practice in patients with HFrEF.

Rationale and design of a navigator-driven remote optimization of guideline-directed medical therapy in patients with heart failure with reduced ejection fraction.

Although optimal pharmacological therapy for heart failure with reduced ejection fraction (HFrEF) is carefully scripted by treatment guidelines, many eligible patients are not treated with guideline-directed medical therapy (GDMT) in clinical practice. We designed a strategy for remote optimization of GDMT on a population scale in patients with HFrEF leveraging nonphysician providers. An electronic health record-based algorithm was used to identify a cohort of patients with a diagnosis of heart failure (HF) and ejection fraction (EF) ≤ 40% receiving longitudinal follow-up at our center. Those with end-stage HF requiring inotropic support, mechanical circulatory support, or transplantation and those enrolled in hospice or palliative care were excluded. Treating providers were approached for consent to adjust medical therapy according to a sequential, stepped titration algorithm modeled on the current American College of Cardiology (ACC)/American Heart Association (AHA) HF Guidelines within a collaborative care agreement. The program was approved by the institutional review board at Brigham and Women's Hospital with a waiver of written informed consent. All patients provided verbal consent to participate. A navigator then facilitated medication adjustments by telephone and conducted longitudinal surveillance of laboratories, blood pressure, and symptoms. Each titration step was reviewed by a pharmacist with supervision as needed from a nurse practitioner and HF cardiologist. Patients were discharged from the program to their primary cardiologist after achievement of an optimal or maximally tolerated regimen. A navigator-led remote management strategy for optimization of GDMT may represent a scalable population-level strategy for closing the gap between guidelines and clinical practice in patients with HFrEF.

Is an Admission for Decompensated Heart Failure Inevitable?

Given the high prevalence of heart failure (HF) and the profound impact on morbid, mortality, and health care costs, strategies to improve outcomes and reduce cost have become progressively more attractive. Reducing HF hospitalizations as a study outcome has gained traction in recent years. The basic hypothesis of these investigations is that HF hospitalizations are preventable and harmful. This article examines advancements in pharmacotherapy, medical devices, and health care delivery techniques targeting reductions in HF hospitalizations and evaluates the role and implications of hospitalization in the natural history of HF.