Quality of Care and Outcomes Among Patients Hospitalized for Heart Failure in Rural vs Urban US Hospitals: The Get With The Guidelines-Heart Failure Registry

Advancing Guideline-Directed Medical Therapy in Heart Failure: Overcoming Challenges and Maximizing Benefits

The delayed titration of guideline-directed drug therapy (GDMT) is a complex event influenced by multiple factors that often result in poor prognosis for patients with heart failure (HF). Individualized adjustments in GDMT titration may be necessary based on patient characteristics, and every clinician is responsible for promptly initiating GDMT and titrating it appropriately within the patient's tolerance range. This review examines the current challenges in GDMT implementation and scrutinizes titration considerations within distinct subsets of HF patients, with the overarching goal of enhancing the adoption and effectiveness of GDMT. The authors also underscore the significance of establishing a novel management strategy that integrates cardiologists, nurse practitioners, pharmacists, and patients as a unified team that can contribute to the improved promotion and implementation of GDMT.

A Deep Learning Approach for Automated Extraction of Functional Status and New York Heart Association Class for Heart Failure Patients During Clinical Encounters

Introduction

Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials. We developed and validated a deep learning-based natural language processing (NLP) strategy to extract functional status assessments from unstructured clinical notes.

Methods

We identified 26,577 HF patients across outpatient services at Yale New Haven Hospital (YNHH), Greenwich Hospital (GH), and Northeast Medical Group (NMG) (mean age 76.1 years; 52.0% women). We used expert annotated notes from YNHH for model development/internal testing and from GH and NMG for external validation. The primary outcomes were NLP models to detect (a) explicit New York Heart Association (NYHA) classification, (b) HF symptoms during activity or rest, and (c) functional status assessment frequency.

Results

Among 3,000 expert-annotated notes, 13.6% mentioned NYHA class, and 26.5% described HF symptoms. The model to detect NYHA classes achieved a class-weighted AUROC of 0.99 (95% CI: 0.98-1.00) at YNHH, 0.98 (0.96-1.00) at NMG, and 0.98 (0.92-1.00) at GH. The activity-related HF symptom model achieved an AUROC of 0.94 (0.89-0.98) at YNHH, 0.94 (0.91-0.97) at NMG, and 0.95 (0.92-0.99) at GH. Deploying the NYHA model among 166,655 unannotated notes from YNHH identified 21,528 (12.9%) with NYHA mentions and 17,642 encounters (10.5%) classifiable into functional status groups based on activity-related symptoms.

Conclusions

We developed and validated an NLP approach to extract NYHA classification and activity-related HF symptoms from clinical notes, enhancing the ability to track optimal care and identify trial-eligible patients.

Palliative Care Across the Spectrum of Heart Failure

Persons with heart failure (HF) often suffer from poor symptom control, decreased quality of life, and poor communication with their health care providers. These needs are particularly acute in advanced HF, a leading cause of death in the United States. Palliative care, when offered alongside HF disease management, offers improved symptom control, quality of life, communication, and caregiver satisfaction as well as reduced caregiver anxiety. The dynamic nature of the clinical trajectory of HF presents distinct symptom patterns, changing functional status, and uncertainty, which requires an adaptive, dynamic model of palliative care delivery. Due to a limited specialty-trained palliative care workforce, patients and their caregivers often cannot access these benefits, especially in the community. To meet these needs, new models are required that are better informed by high-quality data, engage a range of health care providers in primary palliative care principles, and have clear triggers for specialty palliative care engagement, with specific palliative interventions tailored to patient's illness trajectory and changing needs.

Prioritizing the primary prevention of heart failure: Measuring, modifying and monitoring risk

With the rising incidence of heart failure (HF) and increasing burden of morbidity, mortality, and healthcare expenditures, primary prevention of HF targeting individuals in at-risk HF (Stage A) and pre-HF (Stage B) Stages has become increasingly important with the goal to decrease progression to symptomatic (Stage C) HF. Identification of risk based on traditional risk factors (e.g., cardiovascular health which can be assessed with the American Heart Association's Life's Essential 8 framework), adverse social determinants of health, inherited risk of cardiomyopathies, and identification of risk-enhancing factors, such as patients with viral disease, exposure to cardiotoxic chemotherapy, and history of adverse pregnancy outcomes should be the first step in evaluation for HF risk. Next, use of guideline-endorsed risk prediction tools such as Pooled Cohort Equations to Prevent Heart Failure provide quantification of absolute risk of HF based in traditional risk factors. Risk reduction through counseling on traditional risk factors is a core focus of implementation of prevention and may include the use of novel therapeutics that target specific pathways to reduce risk of HF, such as mineralocorticoid receptor agonists (e.g., fineronone), angiotensin-receptor/neprolysin inhibitors, and sodium glucose co-transporter-2 inhibitors. These interventions may be limited in at-risk populations who experience adverse social determinants and/or individuals who reside in rural areas. Thus, strategies like telemedicine may improve access to preventive care. Gaps in the current knowledge base for risk-based prevention of HF are highlighted to outline future research that may target approaches for risk assessment and risk-based prevention with the use of artificial intelligence, genomics-enhanced strategies, and pragmatic trials to develop a guideline-directed medical therapy approach to reduce risk among individuals with Stage A and Stage B HF.