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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
A Computable Algorithm for Medication Optimization in Heart Failure With Reduced Ejection Fraction
Michael P Dorsch1,2, Aaron Sifuentes3, David J Cordwin1
1Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.
Insights
A new algorithm can optimize guideline-directed medical therapy (GDMT) for heart failure patients. Higher medication optimization scores (MOS) were linked to better clinical outcomes, suggesting potential for improved heart failure care.
Area of Science:
- Cardiology
- Medical Informatics
- Clinical Trials
Background:
- Optimizing guideline-directed medical therapy (GDMT) is crucial for improving outcomes in patients with heart failure with reduced ejection fraction (HFrEF).
- Suboptimal GDMT is a common challenge in clinical practice, even within structured clinical trials.
Purpose of the Study:
- To evaluate a novel computable algorithm designed to recommend GDMT for HFrEF.
- To assess if the algorithm's recommendations align with actual medication adjustments and if its output correlates with patient outcomes.
Main Methods:
- Utilized clinical trial data from GUIDE-IT and HF-ACTION studies.
- Applied a computable medication optimization algorithm to generate GDMT recommendations and a medication optimization score (MOS).
- Compared algorithm-based recommendations to actual medication changes and analyzed the association between MOS and clinical endpoints using Cox proportional-hazards models.
Main Results:
- The algorithm identified significant opportunities for initiating and titrating GDMT (ACEI/ARB, beta-blockers, MRAs) that were underutilized in the trials.
- A higher baseline MOS was significantly associated with a reduced risk of cardiovascular death or heart failure hospitalization in GUIDE-IT (HR: 0.41) and all-cause death/hospitalization in HF-ACTION (HR: 0.61).
- The algorithm demonstrated accuracy in identifying patients who could benefit from GDMT optimization.
Conclusions:
- The computable algorithm effectively identifies patients requiring GDMT optimization in HFrEF.
- The algorithm-generated MOS is a predictor of improved clinical outcomes, highlighting its potential clinical utility.
- Implementation of this algorithm in clinical settings could address suboptimal GDMT and improve care for HFrEF patients.
Background:
Guideline-directed medical therapy (GDMT) optimization can improve outcomes in heart failure with reduced ejection fraction.
Objectives:
The objective of this study was to determine if a novel computable algorithm appropriately recommended GDMT.
Methods:
Clinical trial data from the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure) and HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) trials were evaluated with a computable medication optimization algorithm that outputs GDMT recommendations and a medication optimization score (MOS). Algorithm-based recommendations were compared to medication changes. A Cox proportional-hazards model was used to estimate the associations between MOS and the composite primary end point for both trials.
Results:
The algorithm recommended initiation of angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, beta-blockers, and mineralocorticoid receptor antagonists in 52.8%, 34.9%, and 68.1% of GUIDE-IT visits, respectively, when not prescribed the drug. Initiation only occurred in 20.8%, 56.9%, and 15.8% of subsequent visits. The algorithm also identified dose titration in 48.8% of visits for angiotensin-converting enzyme inhibitor/angiotensin receptor blockers and 39.4% of visits for beta-blockers. Those increases only occurred in 24.3% and 36.8% of subsequent visits. A higher baseline MOS was associated with a lower risk of cardiovascular death or heart failure hospitalization (HR: 0.41; 95% CI: 0.21-0.80; P = 0.009) in GUIDE-IT and all-cause death and hospitalization (HR: 0.61; 95% CI: 0.44-0.84; P = 0.003) in HF-ACTION.
Conclusions:
The algorithm accurately identified patients for GDMT optimization. Even in a clinical trial with robust protocols, GDMT could have been further optimized in a meaningful number of visits. The algorithm-generated MOS was associated with a lower risk of clinical outcomes. Implementation into clinical care may identify and address suboptimal GDMT in patients with heart failure with reduced ejection fraction.
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