Journal of the American Heart Association · 2026
Barriers to Optimization of Medical Therapy and the Role of Checklist-Based Decision Support in Heart Failure.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Montelaro Brett M, Woods Edward, Speight Candace D, Udeshi Eisha P, Mitchell Andrea R, Burke Michael A, Diamond Jamie, Morris Alanna A, Obi Engels N, Stevenson Andra S, Tiedeken Molly, Dickert Neal W
Аннотация
BACKGROUND: Guideline-directed medical therapy (GDMT) reduces morbidity and mortality in heart failure with reduced ejection fraction, yet it remains underused. Patient-activation interventions, including checklist-based decision support, have shown improvement in GDMT optimization, and iterative refinement of such tools may enhance their impact. We analyzed recorded clinician-patient encounters, in which a checklist-based activation tool was used, to identify barriers to GDMT optimization and opportunities to enhance decision support.
METHODS: This was a secondary analysis of transcript data from the POCKET-COST-HF (Integrating Cost Into Shared Decision-Making for Heart Failure With Reduced Ejection Fraction) trial, which implemented a checklist-based tool focused on prescription price transparency. Patients and clinicians at 2 academic health centers received adapted versions of the EPIC-HF (Electronically Delivered Patient-Activation Tool for Intensification of Medications for Chronic Heart Failure With Reduced Ejection Fraction) checklist outlining approved medications and target doses for HFrEF. Encounters were audio recorded and transcribed and underwent content analysis to identify barriers and opportunities for checklist improvement.
RESULTS: Encounters between 247 patients with heart failure with reduced ejection fraction (mean age 62.9, 29.5% female, 26.3% Black) and 39 clinicians were included. Baseline GDMT use was high (95% beta blockers, 81% angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors, 63% mineralocorticoid receptor antagonists, and 43% SGLT2 [sodium-glucose cotransporter 2] inhibitors). The checklist was referenced in 48.6% of encounters, and GDMT optimization discussed in 75.7%. Barriers to optimization were identified in 61.5% of encounters, the most common (74.3%) being medical (eg, hypotension). Nonmedical barriers included medication cost (29.6%) and resistance from patients or clinicians (13.8%), often reflecting clinical inertia or desire to minimize medications.
CONCLUSIONS: Nonmedical barriers to GDMT optimization may be addressed by decision-support tools such as checklists. These data suggest that attention to cost, clinical inertia, and desire to minimize medications should be prioritized in future iterations.
METHODS: This was a secondary analysis of transcript data from the POCKET-COST-HF (Integrating Cost Into Shared Decision-Making for Heart Failure With Reduced Ejection Fraction) trial, which implemented a checklist-based tool focused on prescription price transparency. Patients and clinicians at 2 academic health centers received adapted versions of the EPIC-HF (Electronically Delivered Patient-Activation Tool for Intensification of Medications for Chronic Heart Failure With Reduced Ejection Fraction) checklist outlining approved medications and target doses for HFrEF. Encounters were audio recorded and transcribed and underwent content analysis to identify barriers and opportunities for checklist improvement.
RESULTS: Encounters between 247 patients with heart failure with reduced ejection fraction (mean age 62.9, 29.5% female, 26.3% Black) and 39 clinicians were included. Baseline GDMT use was high (95% beta blockers, 81% angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors, 63% mineralocorticoid receptor antagonists, and 43% SGLT2 [sodium-glucose cotransporter 2] inhibitors). The checklist was referenced in 48.6% of encounters, and GDMT optimization discussed in 75.7%. Barriers to optimization were identified in 61.5% of encounters, the most common (74.3%) being medical (eg, hypotension). Nonmedical barriers included medication cost (29.6%) and resistance from patients or clinicians (13.8%), often reflecting clinical inertia or desire to minimize medications.
CONCLUSIONS: Nonmedical barriers to GDMT optimization may be addressed by decision-support tools such as checklists. These data suggest that attention to cost, clinical inertia, and desire to minimize medications should be prioritized in future iterations.