Differences in provider approach to initiating and titrating guideline directed medical therapy in heart failure with reduced ejection fraction.

BACKGROUND: Despite the strong evidence supporting guideline-directed medical therapy (GDMT) in patients with heart failure with reduced ejection fraction (HFrEF), prescription rates in clinical practice are still lacking. METHODS: A survey containing 20 clinical vignettes of patients with HFrEF was answered by a national sample of 127 cardiologists and 68 internal/family medicine physicians. Each vignette had 4-5 options for adjusting GDMT and the option to make no medication changes. Survey respondents could only select one option. For analysis, responses were dichotomized to the answer of interest. RESULTS: Cardiologists were more likely to make GDMT changes than general medicine physicians (91.8% vs. 82.0%; OR 1.84 [1.07-3.19]; p = 0.020). Cardiologists were more likely to initiate beta-blockers (46.3% vs. 32.0%; OR 2.38 [1.18-4.81], p = 0.016), angiotensin receptor blocker/neprilysin inhibitor (ARNI) (63.8% vs. 48.1%; OR 1.76 [1.01-3.09], p = 0.047), and hydralazine and isosorbide dinitrate (HYD/ISDN) (38.2% vs. 23.7%; OR 2.47 [1.48-4.12], p < 0.001) compared to general medicine physicians. No differences were found in initiating angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (ACEi/ARBs), initiating mineralocorticoid receptor antagonist (MRA), sodium-glucose transporter protein 2 (SGLT2) inhibitors, digoxin, or ivabradine. CONCLUSIONS: Our results demonstrate cardiologists were more likely to adjust GDMT than general medicine physicians. Future focus on improving GDMT prescribing should target providers other than cardiologists to improve care in patients with HFrEF.

A Computable Algorithm for Medication Optimization in Heart Failure With Reduced Ejection Fraction.

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.