J Gen Intern Med
May 2025
Background: Clinical guidelines recommend medications from four drug classes, collectively referred to as quadruple therapy, to improve outcomes for patients with heart failure with reduced ejection fraction (HFrEF). Wide gaps in uptake of these therapies persist across a range of settings. In this qualitative study, we identified determinants (i.
View Article and Find Full Text PDFmedRxiv
October 2024
Background: Clinical guidelines recommend medications from four drug classes, collectively referred to as quadruple therapy, to improve outcomes for patients with heart failure with reduced ejection fraction (HFrEF). Wide gaps in uptake of these therapies persist across a range of settings. In this qualitative study, we identified determinants (i.
View Article and Find Full Text PDFBackground: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.
Objective: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.