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Computable Algorithm for Medication Optimization in HFrEF
Quick Takes
- The researchers were able to demonstrate the accuracy and safety of the use of a computable algorithm to initiate and titrate GDMT in patients with HFrEF.
- The computable algorithm was able to identify subjects in the trials that were on GDMT as well as those who were on subtherapeutic doses of medication and made appropriate recommendations.
- Even though patients were seen in the clinical trials for medication titration, there was a significant number of patients who were eligible for further up-titration of medications.
Study Questions:
Can an application programming interface appropriately recommend guideline-directed medical therapy (GDMT) and determine if the information about medication optimization was associated with clinical outcomes?
Methods:
A retrospective analysis from the database of two clinical trials (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]) was conducted to determine if a computable medication optimization algorithm created for heart failure with reduced ejection fraction (HFrEF) could determine GDMT and recommend optimization of medications. Data included medication, blood pressure, heart rate, potassium, and serum creatinine. The algorithm was created based on American College of Cardiology/American Heart Association/Heart Failure Society of America guidelines and validated by HF physicians and cardiology pharmacists and was coded in an executable computer format. The algorithm also provided a medication optimization score (MOS). The MOS reflected the extent of medication optimization with 0% being the least optimized and 100% the most optimized. A score close to 100% reflected the number of GDMT medications and higher medication dosing. Continuous variables were computed using a t-test when normally distributed and a Wilcoxon rank test when the variable was not normally distributed. Categorical variables were analyzed using a chi-square test or Fisher’s exact test where appropriate. A Cox proportional-hazards model was used to estimate the association between MOS with clinical variables.
Results:
The algorithm recommended initiation of angiotensin-converting enzyme inhibitors/angiotensin-receptor blockers (ACEi/ARBs), beta-blockers, and mineralocorticoid receptor antagonists (MRAs) 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 ACEi/ARBs 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 HF hospitalization (hazard ratio [HR], 0.41; 95% confidence interval [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. The algorithm-generated MOS was associated with a lower risk of clinical outcomes. The results were consistent between both GUIDE-IT and HF-ACTION data sets. Even though the goal of both trials was to optimize GDMT, the algorithm was able to identify patients who could have benefited from up-titration or additional medications.
Perspective:
Even with robust clinical trial protocols, there was significant room for improvement in optimizing medications. Using a computer-generated algorithm for patients with HFrEF, there is an opportunity to improve patient outcomes. Considering many patients with HFrEF are on suboptimal medications or doses, this is a tool that could help practitioners feel confident in adding medications or increasing the doses of medications at frequent follow-up visits.
Clinical Topics: Heart Failure and Cardiomyopathies, Prevention, Valvular Heart Disease, Acute Heart Failure
Keywords: Adrenergic beta-Antagonists, Algorithms, Angiotensin Receptor Antagonists, Angiotensin-Converting Enzyme Inhibitors, Blood Pressure, Creatinine, Heart Failure, Heart Valve Diseases, Mineralocorticoid Receptor Antagonists, Myocardial Ischemia, Patient Care Team, Potassium, Secondary Prevention, Stroke Volume
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