Heart Failure With Mid-range Ejection Fraction: Every Coin Has Two Sides

The impact of heart failure therapy in patients with mildly reduced ejection fraction: a network meta-analysis

BACKGROUND

Recent heart failure (HF) guidelines have re-classified HF patients with left ventricular ejection fraction (LVEF) between 41% and 49% as HF with mildly reduced ejection fraction (HFmrEF). HFmrEF treatment is often considered a grey zone as no randomized controlled trials (RCTs) were conducted exclusively on these patients.

AIMS

A network meta-analysis (NMA) was performed to compare treatment effect of mineralocorticoid receptor antagonists (MRA), angiotensin receptor neprilysin inhibitor (ARNi), angiotensin receptor blockers (ARB), angiotensin-converting-enzyme inhibitors (ACEi), sodium-glucose cotransporter-2 inhibitors (SGLT2i), and beta-blockers (BB) in HFmrEF cardiovascular (CV) outcomes.

METHODS AND RESULTS

RCTs sub-analyses evaluating the efficacy of pharmacological treatment in HFmrEF patients were searched. Hazard ratios (HRs) and their variance were extracted from each RCT for (i) composite of CV death or HF hospitalizations, (ii) CV death, and (iii) HF hospitalizations. A random-effects NMA was performed to compare and assess the treatment efficiency. Six RCTs with subgroup analysis according to participants' ejection fraction, a patient-level pooled meta-analysis of two RCTs, and an individual patient-level analysis of eleven BB RCTs were included, totalling 7966 patients. To our primary endpoint, SGLT2i vs. placebo was the only comparison with significant results, with a 19% risk reduction in the composite of CV death or HF hospitalizations [HR 0.81, 95% confidence interval (CI) 0.67-0.98]. In HF hospitalizations, the impact of the pharmacological therapies was more notorious, and ARNi reduced in 40% the risk of HF hospitalizations (HR 0.60, 95% CI 0.39-0.92), SGLT2i in 26% (HR 0.74, 95% CI 0.59-0.93) and renin-angiotensin system inhibition (RASi) with ARB and ACEi in 28% (HR 0.72, 95% CI 0.53-0.98). Although BBs were globally less beneficial, they were the only class that supported a reduced risk of CV death (HR vs. placebo: 0.48, 95% CI 0.24-0.95). We did not observe a statistically significant difference in any comparison between active treatments. There was a sound reduction with ARNi on the primary endpoint (HR vs. BB: 0.81, 95% CI 0.47-1.41; HR vs. MRA 0.94, 95% CI 0.53-1.66) and on HF hospitalizations (HR vs. RASi 0.83, 95% CI 0.62-1.11; HR vs. SGLT2i 0.80, 95% CI 0.50-1.30).

CONCLUSIONS

In addition to SGLT2i, pharmacological treatment recommended for HF with reduced LVEF, namely, ARNi, MRA, and BB, can also be effective in HFmrEF. This NMA did not show significant superiority over any pharmacological class.

Budget Impact Analysis of Utilization of Sacubitril/Valsartan for the Treatment of Heart Failure With Reduced Ejection Fraction in the Philippines

OBJECTIVES

This study aimed to estimate the financial and economic impact of sacubitril/valsartan compared with enalapril for the treatment and prevention of hospitalization/rehospitalization because of heart failure with reduced ejection fraction (HFrEF).

METHODS

The budget impact analysis was guided by the Philippine Reference Case and ISPOR's Principles of Good Practice for Budget Impact Analysis. A government-funded healthcare payer perspective and a societal perspective were considered. Data collection was guided by the pathways of disease progression and care. Collection of costing data followed a bottom-up approach. The model was based on a Markov model used in a study in Thailand.

RESULTS

Over the next 5 years, there will be 17 625 less hospitalizations (∼5.1% less than enalapril arm) and 7968 less cardiovascular-related deaths (∼7.0% less than enalapril arm). In 5 years, the total cost of treating patients with HFrEF with sacubitril/valsartan at current market coverage and annual growth conditions is ₱15.430 billion, which is ₱11.077 billion higher than fully treating with enalapril only. The total required additional investment with treatment of sacubitril/valsartan compared with the full enalapril arm are ₱407 million (at 30-day coverage), ₱800 million (at 60-day coverage), and ₱1.181 billion (at 90-day coverage). If hospitalizations costs alone are considered, only the 30-day coverage is cost-saving. If a societal perspective is considered, all options are cost-saving where at least ₱4.003 billion is saved by the economy.

CONCLUSION

The initial investment required to treat patients with HFrEF with sacubitril/valsartan is high; nevertheless, the year-on-year cost deficit shrinks in favor of investing in sacubitril/valsartan treatment.

[Treatment of patients with heart failure and preserved ejection fraction: reliance on clinical phenotypes]

The article discusses the problem of improving the effectiveness of treatment of heart failure with preserved left ventricular ejection fraction (HFpEF). The relative "failure" of early studies with renin-angiotensin-aldosterone system inhibitors was largely due to the lack of understanding that patients with HFpEF represent a heterogeneous group with various etiological factors and pathogenetic mechanisms of the disease. Therefore, the so-called personalized approach should be used in the treatment of these patients. This approach is based on the identification of clearly defined disease phenotypes, each characterized by a set of demographic, pathogenetic, and clinical characteristics. Based on the literature and own experience, the authors consider four main phenotypes of HFpEF: 1) phenotype with brain natriuretic peptide "deficiency" syndrome associated with moderate/severe left ventricular hypertrophy; 2) cardiometabolic phenotype; 3) phenotype with mixed pulmonary hypertension and right ventricular failure; and 4) cardiac amyloidosis phenotype. In the treatment of patients with phenotype 1, it seems preferable to use the valsartan + sacubitril (possibly in combination with spironolactone) combination treatment; with phenotype 2, the empagliflozin treatment is the best; with phenotype 3, the phosphodiesterase type 5 inhibitor sildenafil; and with phenotype 4, transthyretin stabilizers. Certain features of different phenotypes overlap and may change as the disease progresses. Nevertheless, the isolation of these phenotypes is advisable to prioritize the choice of drug therapy. Thus, the diuretic treatment (preferably torasemide) should be considered in the presence of congestion, regardless of the HFpEF phenotype; the valsartan + sacubitril and spironolactone treatment is appropriate not only in the shortage of brain natriuretic peptide but also in the presence of concentric left ventricular hypertrophy (except for the amyloidosis phenotype); and the treatment with empagliflozin and statins may be considered in all situations where pro-inflammatory mechanisms are involved.

Risk factors of short-term, intermediate-term, and long-term cardiac events in patients hospitalized for HFmrEF

AIMS

Clinical data on the prognostic determinants over varying periods within the same cohort of heart failure with mid-range or mildly reduced ejection fraction (HFmrEF) remain scarce. This study aimed to identify the short-term, intermediate-term, and long-term risk factors of adverse cardiovascular (CV) outcomes in patients hospitalized for HFmrEF.

METHODS AND RESULTS

This retrospective study included 1691 consecutive HFmrEF patients admitted to our hospital between January 2015 and August 2020. Baseline data including clinical characteristics, laboratory and cardiac imaging examinations were obtained. Patients completed at least 1 year clinical follow-up after discharge by telephone interview, clinical visit, or community visit. The primary endpoint was defined as a composite of CV death or rehospitalization for heart failure (CV events) at 3, 12, and 33 months after the diagnosis of HFmrEF. Mean age of the whole cohort was 69 (61-77) years and 64.8% were male. The median clinical follow-up was 33 (20-50) months. CV events were 17.5%, 28.2%, and 57.8% at 3, 12, and 33 months after discharge, respectively. Independent risk factors for CV events were uric acid >382 μmol/L, creatinine >100 μmol/L, N-terminal pro-B type natriuretic peptide (NT-proBNP) > 3368 pg/mL and haemoglobin <120 g/L for men and <110 g/L for women at 3 and 12 months. Pulmonary artery systolic pressure >35 mmHg and the ratio of early transmitral flow velocity to early mitral annular velocity >18 served as independent risk factors for CV events at 12 months. At 33 months, uric acid > 382 μmol/L, NT-proBNP >3368 pg/mL, and pulmonary artery systolic pressure >35 mmHg were the independent risk factors of CV events.

CONCLUSIONS

Higher uric acid, creatinine, NT-proBNP, and lower haemoglobin levels at baseline are valuable serum biomarkers for risk stratification of short-term and long-term CV outcomes of HFmrEF patients. Future studies are needed to verify if intensive heart failure therapy for identified high-risk HFmrEF patients based on these four serum biomarkers could improve their short-term and long-term CV outcomes or not.

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF. Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges. Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories. Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98. Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.