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.

Fewer patients receive recommendations for pharmacotherapy in primary prevention using the 2018 atherosclerotic cardiovascular disease risk estimator.

Shortly after the introduction of the 2013 original Pooled Cohort Equation (PCE), an overestimation of risk was suggested. As such, the updated 2018 PCE was developed to more accurately assess atherosclerotic cardiovascular disease (ASCVD) risk in the population. Hence, this study aims to compare drug prescribing recommendations in a large, real-world patient population, depending on which PCE is used to estimate 10-year ASCVD risk. This retrospective cohort study identified 20,843 patients aged between 40 and 75 years with no previous ASCVD. The 10-year ASCVD risk score was assessed by using both PCE. Patients were assigned to the four risk categories according to the 2018 ACC/AHA guideline. The percentage of patients qualifying for guideline-recommended primary prevention with statins and/or anti-hypertensives were compared between both PCE. Risk reclassification occurred in 26.7% of patients overall (n = 5571), of which 98.1% (n = 5466) were assigned to lower risk categories with the updated PCE. Non-diabetic (14.0%) patients no longer met the threshold for recommending statins as primary prevention with the updated PCE. Likewise, 13.8% of patients with stage I hypertension no longer met the threshold for recommending antihypertensive drugs with the updated PCE. In conclusion, risk reclassification occurred among 26.7% of patients overall, mostly due to lower risk categories assigned by the updated PCE. Up to 14.0% of patients no longer met the threshold for recommending statin therapy and/or antihypertensive drugs by using the updated PCE. These findings suggest that using the updated PCE could translate into fewer patients receiving pharmacotherapy for ASCVD primary prevention.

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.

Patient self-assessment of urine dipsticks to estimate sodium intake in patients with hypertension.

BACKGROUND & AIMS: Dietary sodium restriction is recommended by current guidelines to reduce blood pressure and decrease the risk of cardiovascular disease. Current methods to assess sodium intake such as dietary questionnaires and 24-h urine collection are cumbersome, and the results are not readily available to patients. In this analysis, we evaluated using chloride and creatinine dipsticks as a convenient method to monitor sodium intake, in addition to patients' ability to practice this method independently. METHODS: This is a post-hoc analysis of a previously published trial, LowSalt4Life, that measured change in sodium consumption over 8 weeks to evaluate the effect of a just-in-time adaptive mobile application intervention. Participants were instructed on how to complete and interpret Quantab® chloride and Multistix® PRO 10 LS creatinine dipstick measurements at home and upload a picture of their results. A pharmacy student interpreted the chloride dipsticks, and intraclass correlation coefficients (ICC) were calculated to assess interrater reliability between the participant and pharmacy student. Predicted 24-h sodium values were calculated by the Kawasaki and Mann methods and compared to actual 24-h sodium excretion. RESULTS: There was a strong interobserver correlation between interpretations of the chloride dipsticks, with the ICC values 0.90, 0.97, 0.99, and 0.98 at weeks 2, 4, 6, and 8, respectively. There was a moderate correlation between the dipstick predicted 24-h sodium excretion and actual 24-h sodium excretion at baseline (r = 0.506; P < 0.001), and a weak correlation at week 8 (r = 0.187; P = 0.217). When corrected creatinine values were used, the dipstick predicted 24-h sodium excretion correlated with the actual 24-h sodium excretion at baseline and week 8 (r = 0.512; P < 0.001 and r = 0.451; P = 0.002). CONCLUSIONS: Our analysis suggests that chloride and creatinine dipsticks have the potential to predict total daily excretion of sodium. This method provides patients with an easy, convenient, and accurate method to assess sodium excretion at home. Further research is needed to identify the optimal timing of performing the dipstick analysis as well as ways to improve the creatinine measurement of the urine samples. TRIAL REGISTRATION: ClinicalTrials.gov NCT03099343; https://clinicaltrials.gov/ct2/show/NCT03099343.

The Role of sGC Stimulators and Activators in Heart Failure With Reduced Ejection Fraction.

Over the past decade, soluble guanylate cyclase (sGC) activators and stimulators have been developed and studied to improve outcomes in patients with heart failure with reduced ejection fraction (HFrEF). The sGC enzyme plays an important role in the nitric oxide (NO)-sGC-cyclic guanosine monophosphate (cGMP) pathway, that has been largely untargeted by current guideline directed medical therapy (GDMT) for HFrEF. Disruption of the NO-sCG-cGMP pathway can be widely observed in patients with HFrEF leading to endothelial dysfunction. The disruption is caused by an oxidized state resulting in low bioavailability of NO and cGMP. The increase in reactive oxygen species can also result in an oxidized, and subsequently heme free, sGC enzyme that NO is unable to activate, furthering the endothelial dysfunction. The novel sGC stimulators enhance the sensitivity of sGC to NO, and independently stimulate sGC, while the sGC activators target the oxidized and heme free sGC to stimulate cGMP production. This review will discuss the pathophysiologic basis for sGC stimulator and activator use in HFrEF, review the pre-clinical and clinical data, and propose a place in the HFrEF armamentarium for this novel pharmacotherapeutic class.

Comparative performance of the two pooled cohort equations for predicting atherosclerotic cardiovascular disease.

BACKGROUND AND AIMS: Multivariable algorithms have been developed to predict the risk of atherosclerotic cardiovascular disease (ASCVD) to identify high-risk patients. Shortly after the introduction of the AHA/ACC Pooled Cohort Equations (PCE), a systematic overestimation of risk was identified. As such, a revised PCE was proposed to more accurately assess ASCVD risk. This study aims to compare the accuracy of both PCE in predicting ASCVD risk within a large, real-world patient sample in the US. METHODS: This retrospective cohort study identified 20,843 patients aged between 40 and 75 years with no previous ASCVD in an academic healthcare system. Model fit, calibration, and discrimination were compared between PCE using Bayesian Information Criterion (BIC), Hosmer-Lemeshow test, area under the ROC curves (AUC), Brier score, and precision-recall analysis. In addition, we examined race and sex subgroups for effect modification. RESULTS: Both PCE showed poor calibration (Hosmer-Lemeshow χ2 > 20; p < 0.05) and discrimination (AUC<0.7). The lack of improvement in discrimination of the revised PCE (AUC: 0.677 vs 0.679; p = 0.357) was confirmed with the AUC precision-recall curves (AUCPR: 0.0717 vs 0.0698). In contrast, the AHA/ACC PCE showed a strong positive risk prediction (ΔBIC>10) compared to the revised PCE, although calibration curves had overlapped. CONCLUSIONS: In this single center analysis, both PCE had poor calibration and discrimination of ASCVD risk in a large, real-world patient sample followed up for over 2 years. There was no evidence of improvement in the accuracy of the revised PCE in assessing the risk of ASCVD in relation to the AHA/ACC PCE.