The Potential of Clinical Phenotyping of Heart Failure With Imaging Biomarkers for Guiding Therapies: A Focused Update

Computational investigation of the role of ventricular remodelling in HFpEF: The key to phenotype dissection

Recent clinical studies have reported that heart failure with preserved ejection fraction (HFpEF) can be divided into two phenotypes based on the range of ejection fraction (EF), namely HFpEF with higher EF and HFpEF with lower EF. These phenotypes exhibit distinct left ventricle (LV) remodelling patterns and dynamics. However, the influence of LV remodelling on various LV functional indices and the underlying mechanics for these two phenotypes are not well understood. To address these issues, this study employs a coupled finite element analysis (FEA) framework to analyse the impact of various ventricular remodelling patterns, specifically concentric remodelling (CR), concentric hypertrophy (CH), and eccentric hypertrophy (EH), with and without LV wall thickening on LV functional indices. Further, the geometries with a moderate level of remodelling from each pattern are subjected to fibre stiffening and contractile impairment to examine their effect in replicating the different features of HFpEF. The results show that with severe CR, LV could exhibit the characteristics of HFpEF with higher EF, as observed in recent clinical studies. Controlled fibre stiffening can simultaneously increase the end-diastolic pressure (EDP) and reduce the peak longitudinal strain (ell) without significant reduction in EF, facilitating the moderate CR geometries to fit into this phenotype. Similarly, fibre stiffening can assist the CH and 'EH with wall thickening' cases to replicate HFpEF with lower EF. These findings suggest that potential treatment for these two phenotypes should target the bio-origins of their distinct ventricular remodelling patterns and the extent of myocardial stiffening.

Association between diabetic nephropathy and left ventricular longitudinal myocardial function in type 1 diabetes mellitus patients with preserved ejection fraction

Left ventricular (LV) longitudinal myocardial dysfunction can be observed even in type 2 diabetes mellitus (DM) (T2DM) patients with preserved LV ejection fraction (LVEF), and is considered the earliest marker of DM-related cardiac dysfunction. Furthermore, diabetic nephropathy (DN), a common complication in DM, is strongly associated with LV longitudinal myocardial function in T2DM patients, but its association with type 1 DM (T1DM) has not been fully investigated. We studied 125 asymptomatic T1DM patients with preserved LVEF, and 75 age-, gender-, LVEF-matched non-diabetic healthy controls. Two-dimensional speckle-tracking strain LV was used to assess longitudinal myocardial function as global longitudinal strain (GLS). GLS of T1DM patients was significantly lower than that of normal controls (19.7 ± 3.6% vs. 20.6 ± 1.8%, P = 0.049). GLS of T1DM patients with DN was significantly lower that of T1DM patients without DN (17.3 ± 3.7% vs. 20.2 ± 3.5%, P < 0.001), but that of T1DM patients without DN was similar compared to normal controls (20.6 ± 1.8% vs. 20.2 ± 3.5%, P = 0.37). Moreover, multiple regression analysis identified DN the independent determinant parameters for GLS of T1DM patients also correlated significantly with duration of T1DM. Impaired LV longitudinal myocardial function was observed in asymptomatic T1DM patients with preserved LVEF, and DN was associated with LV longitudinal myocardial dysfunction. These findings are clinically useful for better management of T1DM patients to prevent impending development of cardiovascular disease.

Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction

OBJECTIVES

The authors explored a deep neural network (DeepNN) model that integrates multidimensional echocardiographic data to identify distinct patient subgroups with heart failure with preserved ejection fraction (HFpEF).

BACKGROUND

The clinical algorithms for phenotyping the severity of diastolic dysfunction in HFpEF remain imprecise.

METHODS

The authors developed a DeepNN model to predict high- and low-risk phenogroups in a derivation cohort (n = 1,242). Model performance was first validated in 2 external cohorts to identify elevated left ventricular filling pressure (n = 84) and assess its prognostic value (n = 219) in patients with varying degrees of systolic and diastolic dysfunction. In 3 National Heart, Lung, and Blood Institute-funded HFpEF trials, the clinical significance of the model was further validated by assessing the relationships of the phenogroups with adverse clinical outcomes (TOPCAT [Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function] trial, n = 518), cardiac biomarkers, and exercise parameters (NEAT-HFpEF [Nitrate's Effect on Activity Tolerance in Heart Failure With Preserved Ejection Fraction] and RELAX-HF [Evaluating the Effectiveness of Sildenafil at Improving Health Outcomes and Exercise Ability in People With Diastolic Heart Failure] pooled cohort, n = 346).

RESULTS

The DeepNN model showed higher area under the receiver-operating characteristic curve than 2016 American Society of Echocardiography guideline grades for predicting elevated left ventricular filling pressure (0.88 vs. 0.67; p = 0.01). The high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization and/or death, even after adjusting for global left ventricular and atrial longitudinal strain (hazard ratio [HR]: 3.96; 95% confidence interval [CI]: 1.24 to 12.67; p = 0.021). Similarly, in the TOPCAT cohort, the high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization or cardiac death (HR: 1.92; 95% CI: 1.16 to 3.22; p = 0.01) and higher event-free survival with spironolactone therapy (HR: 0.65; 95% CI: 0.46 to 0.90; p = 0.01). In the pooled RELAX-HF/NEAT-HFpEF cohort, the high-risk (vs. low-risk) phenogroup had a higher burden of chronic myocardial injury (p < 0.001), neurohormonal activation (p < 0.001), and lower exercise capacity (p = 0.001).

CONCLUSIONS

This publicly available DeepNN classifier can characterize the severity of diastolic dysfunction and identify a specific subgroup of patients with HFpEF who have elevated left ventricular filling pressures, biomarkers of myocardial injury and stress, and adverse events and those who are more likely to respond to spironolactone.

How to diagnose heart failure with preserved ejection fraction: the HFA-PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC)

Making a firm diagnosis of chronic heart failure with preserved ejection fraction (HFpEF) remains a challenge. We recommend a new stepwise diagnostic process, the 'HFA-PEFF diagnostic algorithm'. Step 1 (P=Pre-test assessment) is typically performed in the ambulatory setting and includes assessment for HF symptoms and signs, typical clinical demographics (obesity, hypertension, diabetes mellitus, elderly, atrial fibrillation), and diagnostic laboratory tests, electrocardiogram, and echocardiography. In the absence of overt non-cardiac causes of breathlessness, HFpEF can be suspected if there is a normal left ventricular ejection fraction, no significant heart valve disease or cardiac ischaemia, and at least one typical risk factor. Elevated natriuretic peptides support, but normal levels do not exclude a diagnosis of HFpEF. The second step (E: Echocardiography and Natriuretic Peptide Score) requires comprehensive echocardiography and is typically performed by a cardiologist. Measures include mitral annular early diastolic velocity (e'), left ventricular (LV) filling pressure estimated using E/e', left atrial volume index, LV mass index, LV relative wall thickness, tricuspid regurgitation velocity, LV global longitudinal systolic strain, and serum natriuretic peptide levels. Major (2 points) and Minor (1 point) criteria were defined from these measures. A score ≥5 points implies definite HFpEF; ≤1 point makes HFpEF unlikely. An intermediate score (2-4 points) implies diagnostic uncertainty, in which case Step 3 (F1: Functional testing) is recommended with echocardiographic or invasive haemodynamic exercise stress tests. Step 4 (F2: Final aetiology) is recommended to establish a possible specific cause of HFpEF or alternative explanations. Further research is needed for a better classification of HFpEF.

Prognostic value of natriuretic peptides and restrictive filling pattern before surgical ventricular restoration

OBJECTIVE

Both increased natriuretic peptide levels and restrictive filling pattern (RFP) are important risk predictors in patients with heart failure. The aim of this study was to examine the role of the combined use of natriuretic peptide and RFP for the prognostic stratification of patients with ischemic cardiomyopathy undergoing surgical ventricular restoration in the Biomarker Plus study.

METHODS

A total of 186 patients (aged 64 ± 10 years) underwent echocardiographic study and N-terminal pro-B-type natriuretic peptide assay at baseline (before surgical ventricular restoration). Patients were divided into 4 groups depending on baseline diastolic filling pattern (RFP/no RFP) and N-terminal pro-B-type natriuretic peptide level (less than or greater than or equal to the upper tertile value of 2003 ŋg/L). RFP was defined as E/A ratio ≥2. All-cause death or heart failure hospitalizations within 36-month follow-up were analyzed.

RESULTS

Despite similar ejection fraction, volumes, and mass, the 4 groups presented distinct clinical and structural pattern of presurgical ventricular restoration ventricular remodeling and significantly different clinical outcome after surgical unloading. During follow-up, 67 patients died or were hospitalized for heart failure (36%). High N-terminal pro-B-type natriuretic peptide levels and RFP, considered individually, were significantly associated with outcome (P < .0001). The combination of both was associated with the highest adjusted hazard of adverse events (hazard ratio, 3.63; 95% CI, 1.73-7.6; P < .0001).

CONCLUSIONS

The simultaneous use of 2 markers, 1 biological and 1 echocardiographic, may allow better prognostic stratification and characterization of the distinct structural and clinical phenotypes in a population of patients with ischemic cardiomyopathy undergoing surgical ventricular restoration. This approach could be useful in the decision-making process to guide treatment choices in patients with ischemic cardiomyopathy.

Multimodality imaging in chronic heart failure

The prevalence of heart failure (HF) is approximately 1-2% of the adult population in developed countries, rising to  ≥ 10% among people over 70. The common symptoms of HF include shortness of breath, ankle swelling and fatigue, determined by a reduced cardiac output. Multimodality imaging is crucial to define HF etiology, determine prognosis and guiding tailored treatments. Echocardiography is the most widely used imaging modality and maintains a pivotal role in the initial diagnostic work-up and in the follow-up of HF patients. Cardiac magnetic resonance (CMR) may support the morpho-functional assessment provided by echocardiography when the acoustic window is limited or a gold standard evaluation is required. Furthermore, CMR is frequently used due to the unmatched capability to characterize myocardial structure. Coronary computed tomography angiography has become the non-invasive imaging of choice to diagnose or rule-out coronary artery disease, acquiring remarkable importance in the management of HF patients. Moreover, emerging capabilities of CT-based tissue characterization may be useful, especially when CMR is contraindicated. Finally, chest CT may contribute to precisely define the framework of HF patients, revealing new insight about cardiopulmonary pathophysiological interactions with potential high prognostic value.

A Network-Based "Phenomics" Approach for Discovering Patient Subtypes From High-Throughput Cardiac Imaging Data

OBJECTIVES

The authors present a method that focuses on cohort matching algorithms for performing patient-to-patient comparisons along multiple echocardiographic parameters for predicting meaningful patient subgroups.

BACKGROUND

Recent efforts in collecting multiomics data open numerous opportunities for comprehensive integration of highly heterogenous data to classify a patient's cardiovascular state, eventually leading to tailored therapies.

METHODS

A total of 42 echocardiography features, including 2-dimensional and Doppler measurements, left ventricular (LV) and atrial speckle-tracking, and vector flow mapping data, were obtained in 297 patients. A similarity network was developed to delineate distinct patient phenotypes, and then neural network models were trained for discriminating the phenotypic presentations.

RESULTS

The patient similarity model identified 4 clusters (I to IV), with patients in each cluster showed distinctive clinical presentations based on American College of Cardiology/American Heart Association heart failure stage and the occurrence of short-term major adverse cardiac and cerebrovascular events. Compared with other clusters, cluster IV had a higher prevalence of stage C or D heart failure (78%; p < 0.001), New York Heart Association functional classes III or IV (61%; p < 0.001), and a higher incidence of major adverse cardiac and cerebrovascular events (p < 0.001). The neural network model showed robust prediction of patient clusters, with area under the receiver-operating characteristic curve ranging from 0.82 to 0.99 for the independent hold-out validation set.

CONCLUSIONS

Automated computational methods for phenotyping can be an effective strategy to fuse multidimensional parameters of LV structure and function. It can identify distinct cardiac phenogroups in terms of clinical characteristics, cardiac structure and function, hemodynamics, and outcomes.

How to diagnose heart failure with preserved ejection fraction: the HFA-PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC)

Making a firm diagnosis of chronic heart failure with preserved ejection fraction (HFpEF) remains a challenge. We recommend a new stepwise diagnostic process, the 'HFA-PEFF diagnostic algorithm'. Step 1 (P=Pre-test assessment) is typically performed in the ambulatory setting and includes assessment for heart failure symptoms and signs, typical clinical demographics (obesity, hypertension, diabetes mellitus, elderly, atrial fibrillation), and diagnostic laboratory tests, electrocardiogram, and echocardiography. In the absence of overt non-cardiac causes of breathlessness, HFpEF can be suspected if there is a normal left ventricular (LV) ejection fraction, no significant heart valve disease or cardiac ischaemia, and at least one typical risk factor. Elevated natriuretic peptides support, but normal levels do not exclude a diagnosis of HFpEF. The second step (E: Echocardiography and Natriuretic Peptide Score) requires comprehensive echocardiography and is typically performed by a cardiologist. Measures include mitral annular early diastolic velocity (e'), LV filling pressure estimated using E/e', left atrial volume index, LV mass index, LV relative wall thickness, tricuspid regurgitation velocity, LV global longitudinal systolic strain, and serum natriuretic peptide levels. Major (2 points) and Minor (1 point) criteria were defined from these measures. A score ≥5 points implies definite HFpEF; ≤1 point makes HFpEF unlikely. An intermediate score (2-4 points) implies diagnostic uncertainty, in which case Step 3 (F₁ : Functional testing) is recommended with echocardiographic or invasive haemodynamic exercise stress tests. Step 4 (F₂ : Final aetiology) is recommended to establish a possible specific cause of HFpEF or alternative explanations. Further research is needed for a better classification of HFpEF.

Left Ventricular Longitudinal Strain as a Marker for Point of No Return in Hypertensive Heart Failure Treatment

BACKGROUND

There are currently no therapies that can improve prognosis in cases of heart failure (HF) with preserved ejection fraction (EF). We hypothesized that there is a point of no return after which no response to treatment is noted and that for the prevention of hypertensive HF this point may be determined by left ventricle (LV) strain, in the prevention of hypertensive HF. Here an angiotensin-converting enzyme inhibitor (ACE-I) was initiated based on myocardial strain imaging and its effects were determined in an animal model.

METHODS

Thirty-two male Dahl salt-sensitive rats, age 6 weeks, were divided into six experimental groups and compared with low-salt (n = 8) and high-salt control groups (n = 8). In the early treatment group, ACE-I was administered from the age of 6 weeks (n = 4); in the longitudinal strain (LS) group, at 10-12 weeks when LS impairment was >-21% (n = 4); in the circumferential strain (CS) group, at 16-18 weeks when CS impairment was >-18% (n = 4); and in the EF group, at 20 weeks when EF was <55% (n = 4). Subsequently, all rats were sacrificed at 23 weeks age, the LV and lung weight were measured, and pathologic analyses were performed.

RESULTS

At 23 weeks of age, the lung and LV weights increased in the high-salt control, EF, and CS groups, whereas the lung and LV weights in the LS and early groups were similar to those in the low-salt control group. The percentage of area of subendocardial fibrosis was >6% in the high-salt control, EF, and CS groups and <3% in the LS, early, and low-salt groups. Serial echocardiography demonstrated LS improvement in the LS group; however, the CS and EF groups showed no differences.

CONCLUSIONS

Heart failure-related lung congestion was prevented when ACE-I was administered soon after LS impairment, accompanied by suppression of cardiac hypertrophy and fibrosis, thereby suggesting that the point of no return of myocardial remodeling due to hypertension was present after LS but before CS impairment.

Established and Emerging Roles of Biomarkers in Heart Failure

Heart failure (HF) is a complex syndrome with an enormous societal burden in terms of cost, morbidity, and mortality. Natriuretic peptide testing is now widely used to support diagnosis, prognostication, and management of patients with HF and are incorporated into HF clinical practice guidelines. Beyond the natriuretic peptides, novel biomarkers may supplement traditional clinical and laboratory testing to improve understanding of the complex disease process of HF and possibly to personalize care for those affected through better individual phenotyping. In this review, we will discuss natriuretic peptides and the more novel biomarkers by dividing them into categories based on the major pathophysiologic pathways they represent. Given the complex physiology in HF, it is reasonable to expect that the future of biomarker testing lies in the application of multimarker testing panels, precision medicine to improve HF care delivery, and the use of biomarkers in proteomics and metabolomics to further improve HF care.

The Cardiorenal Axis: Myocardial Perfusion, Metabolism, and Innervation

PURPOSE OF THE REVIEW

Cardiorenal syndrome (CRS), defined as concomitant heart and kidney disease, has been a focus of attention for nearly a decade. As more patients survive severe acute and chronic heart and kidney diseases, CRS has emerged as an "epidemic" of modern medicine. Significant advances have been made in unraveling the complex mechanisms that underlie CRS based on classification of the condition into five pathophysiologic subtypes. In types 1 and 2, acute or chronic heart disease results in renal dysfunction, while in types 3 and 4, acute or chronic kidney diseases are the inciting factors for heart disease. Type 5 CRS is defined as concomitant heart and kidney dysfunction as part of a systemic condition such as sepsis or autoimmune disease.

RECENT FINDINGS

There are ongoing efforts to better define subtypes of CRS based on historical information, clinical manifestations, laboratory data (including biomarkers), and imaging characteristics. Systematic evaluation of CRS by advanced cardiac imaging, however, has been limited in scope and mostly focused on type 4 CRS. This is in part related to lack of clinical trials applying advanced cardiac imaging in the acute setting and exclusion of patients with significant renal disease from studies of such techniques in chronic HF. Advanced cardiac nuclear imaging is well poised for assessment of the pathophysiology of CRS by offering a myriad of molecular probes without the need for nephrotoxic contrast agents. In this review, we examine the current or potential future application of advanced cardiac imaging to evaluation of myocardial perfusion, metabolism, and innervation in patients with CRS.

Overlapping Phenotypes and Degree of Ventricular Dilatation Are Associated with Severity of Systolic Impairment and Late Gadolinium Enhancement in Non-Ischemic Cardiomyopathies

Background

Dilatation and other infrastructural rearrangements of the left ventricle are connected with poor prognosis. The aim of our study was to analyze the overlapping phenotypes and dilatation of the ventricle on impairment of systolic function and existence of late gadolinium enhancement (LGE).

Material/Methods

Consecutive sample of cases with dilated left ventricle due to non-ischemic cardiomyopathy and healthy controls were included from our cardiac magnetic resonance imaging (CMR) database for a period of 3 years (n=1551 exams).

Results

The study included 127 patients; 30 (23.6%) with dilated cardiomyopathy (DCM); 30 (23.6%) with left ventricular non-compaction (LVNC); 13 (10.2%) with hypertrophic cardiomyopathy (HCM), and 50 (39.4%) controls. Overlapping phenotypes were found in 48 (37.8%) of the studied cases. Odds for impairment of systolic function in connection with overlapping phenotypes were estimated at 7.8 (95%-CI: 3.4–17.6), (p<0.001). There were significant differences in geometric parameters for patients with overlapping phenotypes vs. controls, as follows: left ventricle end-diastolic dimension(LVEDD)=6.6±0.8 vs. 5.6±1.0 cm (p<0.001); left ventricular ejection fraction (LVEF)=39.3±14.0 vs. 52.1±16.1 (p<0.001); and existence of LGE 36 (75.0%) vs. 21 (26.6%), (p<0.001), respectively. Overlapping phenotypes correlated with LVEDD (Spearman’s-Rho-CC)=0.521, p<0.001; LVEF (Rho-CC)=−0.447, p<0.001 and LGE (Rho-CC)=0.472, p<0.001.

Conclusions

This study found there are many patients with overlapping phenotypes among NICMPs with dilated left ventricles. Overlapping phenotype was associated with greater LVEDD, lesser systolic function, and commonly existing LGE, which all impose increased cardiovascular risk. Linear midventricular LGE stripe was the most powerfully connected with loss of systolic function.