Heart failure & cardiomyopathies
A Computable Algorithm for Medication Optimization in Heart Failure With Reduced Ejection Fraction
Authors' conclusion
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.
Study context
- Population
- Patients with HFrEF in the GUIDE-IT and HF-ACTION trials
- Comparison
- Computable algorithm guideline-directed medical therapy (GDMT) recommendations vs actual medication changes
- Primary endpoint
- Composite primary end point of each trial (association with medication optimization score (MOS))
Primary result
As reported in the abstract
Association of higher baseline MOS with composite primary end point
Higher baseline MOSHazard ratio (95% CI)P value
GUIDE-IT: CV death or HF hospitalization
0.41(0.21–0.80)
P = 0.009HF-ACTION: all-cause death and hospitalization
0.61(0.44–0.84)
P = 0.003Further findings
GDMT initiation recommended by algorithm vs initiated at subsequent visit (GUIDE-IT visits when drug not prescribed)
- ACEi/ARB: recommended
- 52.8%
- Beta-blockers: recommended
- 34.9%
- MRA: recommended
- 68.1%
- ACEi/ARB: initiated
- 20.8%
- Beta-blockers: initiated
- 56.9%
- MRA: initiated
- 15.8%
Dose titration identified by algorithm vs dose increase at subsequent visit
- ACEi/ARB: titration identified
- 48.8%
- Beta-blockers: titration identified
- 39.4%
- ACEi/ARB: increased
- 24.3%
- Beta-blockers: increased
- 36.8%
Original publication
A Computable Algorithm for Medication Optimization in Heart Failure With Reduced Ejection Fraction. JACC Adv. Published 12 April 2023. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →