Noninvasive Cardiac Imaging and the Prediction of Heart Failure Progression in Preclinical Stage A/B Subjects

Prioritizing the primary prevention of heart failure: Measuring, modifying and monitoring risk

With the rising incidence of heart failure (HF) and increasing burden of morbidity, mortality, and healthcare expenditures, primary prevention of HF targeting individuals in at-risk HF (Stage A) and pre-HF (Stage B) Stages has become increasingly important with the goal to decrease progression to symptomatic (Stage C) HF. Identification of risk based on traditional risk factors (e.g., cardiovascular health which can be assessed with the American Heart Association's Life's Essential 8 framework), adverse social determinants of health, inherited risk of cardiomyopathies, and identification of risk-enhancing factors, such as patients with viral disease, exposure to cardiotoxic chemotherapy, and history of adverse pregnancy outcomes should be the first step in evaluation for HF risk. Next, use of guideline-endorsed risk prediction tools such as Pooled Cohort Equations to Prevent Heart Failure provide quantification of absolute risk of HF based in traditional risk factors. Risk reduction through counseling on traditional risk factors is a core focus of implementation of prevention and may include the use of novel therapeutics that target specific pathways to reduce risk of HF, such as mineralocorticoid receptor agonists (e.g., fineronone), angiotensin-receptor/neprolysin inhibitors, and sodium glucose co-transporter-2 inhibitors. These interventions may be limited in at-risk populations who experience adverse social determinants and/or individuals who reside in rural areas. Thus, strategies like telemedicine may improve access to preventive care. Gaps in the current knowledge base for risk-based prevention of HF are highlighted to outline future research that may target approaches for risk assessment and risk-based prevention with the use of artificial intelligence, genomics-enhanced strategies, and pragmatic trials to develop a guideline-directed medical therapy approach to reduce risk among individuals with Stage A and Stage B HF.

Rationale and Design of Heart Failure Prevalence and Evolution of Heart Failure in Diabetes Mellitus Type II Patients at High Risk (HF-LanDMark Study)

(1) Background: Patients with diabetes mellitus (DM) are at increased risk for heart failure (HF). Accurate data regarding the prevalence of HF stages among diabetics in Greece are scarce. (2) Aim: The present study will examine the prevalence and evolution of HF stages among patients with type II DM (T2DM) diagnosed in the past 10 years, with no previous history of HF and at high CV risk, in Greece, as well as will explore the potential determinants of the development of symptomatic HF in these patients. (3) Methods: Through a non-interventional, epidemiological, single-country, multi-center, prospective cohort study design, a sample of 300 consecutive patients will be enrolled in 11 cardiology departments that are HF centers of excellence. Patients will be either self-referred or referred by primary or secondary care physicians and will be followed for up to 24 months. Demographic, clinical, echocardiography, electrocardiography, cardiac biomarkers (troponin, NT-proBNP) and health-related quality of life questionnaire data will be recorded as well as clinical events, including mortality, HF hospitalizations and HF-related healthcare resource utilization. The primary outcomes are the proportion of patients diagnosed with symptomatic HF (ACC/AHA Stage C) at enrolment in the overall study population and the proportions of patients with HF stages A, B and C, as well as by NYHA functional classification in the overall study population. (4) Conclusions: The HF-LanDMark study is the first epidemiological study that will assess the prevalence of HF among T2DM patients in Greece that could potentially enhance prompt therapeutic interventions shown to delay the development of HF in the T2DM patient population (HF-LanDMark, Clinical Trials.gov number, NCT04482283).

Interlaboratory variation for NT-proBNP among Swedish laboratories in an external quality program 2011-2021

OBJECTIVES

NT-proBNP is frequently used for ruling out heart failure. Different cut-offs are used depending on the clinical context, e.g. an acute or chronic condition. Medical decision limits have been suggested at 125 and 300 ng/L or 400 ng/L in international guidelines. However, there is limited standardization between NT-proBNP methods and using the same blood sample might cause different treatment of patients.

METHODS

Data from the external quality assessment program for NT-proBNP from Equalis, Sweden, were extracted for the period 2011-2021, and categorized according to manufacturer. Manufacturer median NT-proBNP values were compared to total median values. CV% was calculated for each manufacturer and in comparison to different levels of NT-proBNP.

RESULTS

Roche was the most common method, and its median results were closest to the median consensus results. When looking at the total CV at NT-proBNP levels in the range of 0-500 ng/L, the total CV varied from 4 to 27%. During 2019-2021, Siemens (Immulite, Centaur, Atellica) yielded results 16-20% above the consensus median depending on sample level. Similarly, Abbott was 5-7% above, while Roche and Siemens Stratus were 1% respectively 6-10% below the consensus median.

CONCLUSIONS

The introduction of new manufacturers and methods in 2017 have caused the agreement between manufacturers to decline. This highlights the need for a common calibrator and reference materials, particularly since medical decision limits in guidelines, e.g. European Society of Cardiology 2021, which are mostly based on Roche methods, do not take these method differences into account.

Aortic valve sclerosis and subclinical LV dysfunction in the general population with normal LV geometry

AIMS

Aortic valve sclerosis (AVS) without hemodynamically significant obstruction is related to cardiovascular morbidity and mortality independent of left ventricular (LV) hypertrophy, although the underlying mechanisms remain unknown. This study investigated the prevalence of AVS and its association with subclinical LV systolic and diastolic dysfunction in individuals with normal LV geometry free of cardiovascular disease.

METHODS

We examined 962 participants with normal LV geometry and free from significant AV stenosis who underwent standard and speckle-tracking echocardiography. AVS was categorized into 4 groups as follows: no AVS, AV thickening, calcification on one leaflet and calcification on ≥2 leaflets.

RESULTS

Among the 962 participants, 767 (79.7%) individuals were classified as no AVS, 74 (7.7%) as AV thickening, 87 (9.0%) as calcification on one leaflet, and 34 (3.5%) as calcification on ≥2 leaflets. The prevalence of subclinical LV diastolic dysfunction (E/e' ratio ≥13) and systolic dysfunction (LV global longitudinal strain (GLS) > -17.0% for men and > -18.0% for women) were greater in AVS groups than those in no AVS group. Subclinical LV diastolic impairment was evident from AV thickening and systolic dysfunction was observed at AV calcification. Multivariable logistic regression analysis demonstrated that AV thickening as well as calcification were independently associated with subclinical LV diastolic impairment (all p < 0.05), while only AV calcification on ≥2 leaflets conferred significant increased risk of impaired LVGLS.

CONCLUSIONS

AVS was observed in approximately 20% individuals without cardiac disease and was associated with subclinical LV diastolic and systolic function even in the absence of LV morphological change.

Incidence of Preclinical Heart Failure in a Community Population

Background

A high prevalence of preclinical heart failure (HF) (Stages A and B) has previously been shown. The aim of this study was to explore factors associated with the incidence of preclinical HF in a community population.

Methods and Results

Retrospective review of 393 healthy community individuals aged ≥45 years from the Olmsted County Heart Function Study that returned for 2 visits, 4 years apart. At visit 2, individuals that remained normal were compared with those that developed preclinical HF. By the second visit, 191 (49%) developed preclinical HF (12.1 cases per 100 person‐years of follow‐up); 65 (34%) Stage A and 126 (66%) Stage B. Those that developed preclinical HF (n=191) were older (P=0.004), had a higher body mass index (P<0.001), and increased left ventricular mass index (P=0.006). When evaluated separately, increased body mass index was seen with development of Stage A (P<0.001) or Stage B (P=0.009). Echocardiographic markers of diastolic function were statistically different in those that developed Stage A [higher E/e’ (P<0.001), lower e’ (P<0.001)] and Stage B [higher left atrial volume index (P<0.001), higher E/e’ (P<0.001), lower e’ (P<0.001)]. NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) was higher at visit 2 in those that developed Stage A or B (P<0.001 for both). Hypertension (57%), obesity (34%), and hyperlipidemia (25%) were common in the development of Stage A. Of patients who developed Stage B, 71% (n=84) had moderate or severe diastolic dysfunction.

Conclusions

There is a high incidence of preclinical HF in a community population. Development of Stage A was driven by hypertension and obesity, while preclinical diastolic dysfunction was seen commonly in those that developed Stage B.

A multi-scale and multi-domain heart sound feature-based machine learning model for ACC/AHA heart failure stage classification

OBJECTIVE

Heart sounds can reflect detrimental changes in cardiac mechanical activity that are common pathological characteristics of chronic heart failure (CHF). The ACC/AHA heart failure (HF) stage classification is essential for clinical decision-making and the management of CHF. Herein, a machine learning model that makes use of multi-scale and multi-domain heart sound features was proposed to provide an objective aid for ACC/AHA HF stage classification.

APPROACH

A dataset containing phonocardiogram (PCG) signals from 275 subjects was obtained from two medical institutions and used in this study. Complementary ensemble empirical mode decomposition and tunable-Q wavelet transform were used to construct self-adaptive sub-sequences and multi-level sub-band signals for PCG signals. Time-domain, frequency-domain and nonlinear feature extraction were then applied to the original PCG signal, heart sound sub-sequences and sub-band signals to construct multi-scale and multi-domain heart sound features. The features selected via the least absolute shrinkage and selection operator were fed into a machine learning classifier for ACC/AHA HF stage classification. Finally, mainstream machine learning classifiers, including least-squares support vector machine (LS-SVM), deep belief network (DBN) and random forest (RF), were compared to determine the optimal model.

MAIN RESULTS

The results showed that the LS-SVM, which utilized a combination of multi-scale and multi-domain features, achieved better classification performance than the DBN and RF using multi-scale or multi-domain features alone or together, with average sensitivity, specificity, and accuracy of 0.821, 0.955 and 0.820 on the testing set, respectively.

SIGNIFICANCE

PCG signal analysis provides efficient measurement information regarding CHF severity and is a promising noninvasive method for ACC/AHA HF stage classification.

Relationship Between Subclinical Cardiovascular Diseases and Cardiac Morpho-Functional Parameters in Preclinical Stage A/B Heart Failure Adults with Type 2 Diabetes

PURPOSE

Type 2 diabetes mellitus (T2DM) is a common risk factor for cardiovascular disease which increases the risk of heart failure. This study aimed to determine whether clinical characteristics and subclinical cardiovascular disease (CVD) features are correlated with echocardiographic morpho-functional parameters of T2DM patients.

PATIENTS AND METHODS

Two hundred and fifty-five T2DM patients without a history of coronary heart disease were enrolled in this cross-sectional study. The demographic characteristics, glucose and lipid levels were assessed for each patient. Carotid ultrasonography and peripheral artery examination were performed to measure carotid intima-media thickness (cIMT), carotid plaque, ankle-brachial index (ABI), brachial artery pulse wave velocity (baPWV), and carotid-femoral pulse wave velocity (cfPWV). Furthermore, echocardiography was conducted to evaluate cardiac morphology and systolic and diastolic function. The relationship between clinical characteristics, subclinical cardiovascular diseases, and cardiac morpho-functional parameters was explored with the Pearson and stepwise multivariable linear regression analyses.

RESULTS

A total of 255 subjects aged 18-80 years were enrolled in the study. Multiple regression analysis revealed that left ventricular mass index (LVMI) was correlated with age (β=0.463, p = 0.000) and systolic blood pressure (SBP) (β=0.179, p = 0.003). Relative wall thickness (RWT) was related to cfPWV (β=0.006, p = 0.007) and homeostasis model assessment of insulin resistance (HOMA-IR) (β=0.000, p = 0.036). In contrast, left ventricular ejection fraction (LVEF) was inversely related to cIMT (β=-0.925, p = 0.019). The ratio of the peak flow velocity of early diastole to atrial contraction (peak E/A) was correlated with age (β=-0.014, p = 0.000), diastolic blood pressure (DBP) (β=-0.006, p = 0.001) and cfPWV (β=-0.025, p = 0.044).

CONCLUSION

In preclinical stage A/B heart failure adults with T2DM, age, BP, HOMA-IR, cfPWV and cIMT are correlated with cardiac morpho-functional parameters.

Risk factors for asymptomatic echocardiographic abnormalities that predict symptomatic heart failure

Aims

Risk factors for asymptomatic echocardiographic abnormalities that predict symptomatic heart failure (HF) may provide insight into early mechanisms of HF pathogenesis. We examined risk factors associated with asymptomatic echocardiographic structural, systolic, and diastolic abnormalities, separately and in combination, and interactions between risk factors, in the prospective community‐based SCReening Evaluation of the Evolution of New HF (SCREEN‐HF) Study cohort of 3190 participants at increased risk of cardiovascular disease.

Methods and results

Inclusion criteria were age ≥ 60 years with one or more of hypertension, diabetes, ischaemic heart disease, valvular heart disease, abnormal heart rhythm, cerebrovascular disease, or renal impairment. Exclusion criteria were known HF, ejection fraction < 50%, or >mild valve abnormality. Structural, systolic, and diastolic echocardiographic abnormalities were defined according to the Atherosclerosis Risk in Communities study criteria, and risk factors for asymptomatic structural, systolic, and diastolic abnormalities were identified using logistic regression analysis. In multivariable analysis, increased body mass index (BMI), non‐steroidal anti‐inflammatory drug therapy, and alcohol intake were risk factors for isolated structural abnormality, whereas male gender, increased heart rate, atrial fibrillation (AF), angiotensin‐converting enzyme inhibitor therapy, and obstructive sleep apnoea were associated with a lower risk. Moreover, male gender, smoking, increased systolic blood pressure, and physical inactivity were risk factors for isolated systolic abnormality, whereas increased pulse pressure and antihypertensive therapy were associated with a lower risk. Furthermore, increased age, blood pressure, amino‐terminal pro‐B‐type natriuretic peptide level, and warfarin therapy (associated with AF) were risk factors for isolated diastolic abnormality, whereas increased heart rate and triglyceride level (associated with BMI) were associated with a lower risk. The association of increased heart rate with lower risk of structural and diastolic abnormalities was independent of β‐blocker therapy. Interactions between risk factors differed for structural, systolic, and diastolic abnormalities.

Conclusions

The different risk factors for asymptomatic structural, systolic, and diastolic abnormalities that predict symptomatic HF, and the interactions between risk factors, illustrate how these structural, systolic, and diastolic abnormalities represent unique trajectories that lead to symptomatic HF. Improved understanding of these trajectories may assist in the design of HF prevention strategies.

Echocardiographic phenogrouping by machine learning for risk stratification in the general population

Aims

There is a need for better phenotypic characterization of the asymptomatic stages of cardiac maladaptation. We tested the hypothesis that an unsupervised clustering analysis utilizing echocardiographic indexes reflecting left heart structure and function could identify phenotypically distinct groups of asymptomatic individuals in the general population.

Methods and results

We prospectively studied 1407 community-dwelling individuals (mean age, 51.2 years; 51.1% women), in whom we performed clinical and echocardiographic examination at baseline and collected cardiac events on average 8.8 years later. Cardiac phenotypes that were correlated at r > 0.8 were filtered, leaving 21 echocardiographic features, and systolic blood pressure for phenogrouping. We employed hierarchical and Gaussian mixture model-based clustering. Cox regression was used to demonstrate the clinical validity of constructed phenogroups. Unsupervised clustering analyses classified study participants into three distinct phenogroups that differed markedly in echocardiographic indexes. Indeed, cluster 3 had the worst left ventricular (LV) diastolic function (i.e. lowest e' velocity and left atrial (LA) reservoir strain, highest E/e', and LA volume index) and LV remodelling. The phenogroups were also different in cardiovascular risk factor profiles. We observed increase in the risk for incidence of adverse events across phenogroups. In the third phenogroup, the multivariable adjusted risk was significantly higher than the average population risk for major cardiovascular events (51%, P = 0.0028).

Conclusion

Unsupervised learning algorithms integrating routinely measured cardiac imaging and haemodynamic data can provide a clinically meaningful classification of cardiac health in asymptomatic individuals. This approach might facilitate early detection of cardiac maladaptation and improve risk stratification.

Progression of Preclinical Heart Failure: A Description of Stage A and B Heart Failure in a Community Population

BACKGROUND

The aims of this study are to evaluate the rate of progression of preclinical (Stage A and B) heart failure, identify associated characteristics, and evaluate long-term outcomes.

METHODS

Retrospective review of the Olmsted County Heart Function Study. Individuals categorized as Stage A or B heart failure at initial visit that returned for a second visit 4 years later were included. Logistic regression analyses evaluated group differences with adjustment for age and sex.

RESULTS

At visit 1, 413 (32%) individuals were classified as Stage A and 413 (32%) as Stage B. By visit 2, 146 (35%) individuals from Stage A progressed with the vast majority (n=142) progressing to Stage B. In comparison, a total of 23 (6%) individuals progressed from Stage B. A greater rate of progression was seen for Stage A compared with Stage B (8.7 per 100 person-years [95% CI, 7.4-10.2] versus 1.4 per 100 person-years [95% CI, 0.9-2.1]; P<0.001). NT-proBNP correlated with progression for Stage B (P=0.01), but not for Stage A (P=0.39). A multivariate model found female sex (odds ratio, 1.65 [95% CI, 1.05-2.58]; P=0.03), increased E/e' (odds ratio, 1.13 [95% CI, 1.02-1.26], P=0.02), and beta blocker use (odds ratio, 2.19 [95% CI, 1.25-3.82], P=0.006) were associated with progression for Stage A. There was a signal that cardiovascular mortality was higher in individuals who progressed, although not statistically significant (P=0.06 for Stage A and P=0.05 for Stage B).

CONCLUSIONS

There is significant progression of preclinical heart failure in a community population, with progression rates higher for Stage A. NT-proBNP correlated with progression for Stage B, but not for Stage A. No statistically significant differences in long-term outcomes were seen. Study results have clinical implications important to help guide future heart failure screening and prevention strategies.

Challenges and Opportunities for the Prevention and Treatment of Cardiovascular Disease Among Young Adults: Report From a National Heart, Lung, and Blood Institute Working Group

Improvements in cardiovascular disease (CVD) rates among young adults in the past 2 decades have been offset by increasing racial/ethnic and gender disparities, persistence of unhealthy lifestyle habits, overweight and obesity, and other CVD risk factors. To enhance the promotion of cardiovascular health among young adults 18 to 39 years old, the medical and broader public health community must understand the biological, interpersonal, and behavioral features of this life stage. Therefore, the National Heart, Lung, and Blood Institute, with support from the Office of Behavioral and Social Science Research, convened a 2-day workshop in Bethesda, Maryland, in September 2017 to identify research challenges and opportunities related to the cardiovascular health of young adults. The current generation of young adults live in an environment undergoing substantial economic, social, and technological transformations, differentiating them from prior research cohorts of young adults. Although the accumulation of clinical and behavioral risk factors for CVD begins early in life, and research suggests early risk is an important determinant of future events, few trials have studied prevention and treatment of CVD in participants <40 years old. Building an evidence base for CVD prevention in this population will require the engagement of young adults, who are often disconnected from the healthcare system and may not prioritize long-term health. These changes demand a repositioning of existing evidence-based treatments to accommodate new sociotechnical contexts. In this article, the authors review the recent literature and current research opportunities to advance the cardiovascular health of today's young adults.

Diagnostic Capacity of Pocket-Sized Ultrasound Devices at Point of Care by a Non-radiologist Resident in Patients with Suspected Abdominal Pathology

Studies have reported the usefulness and tolerability in practice of abdominal ultrasound performed by non-radiologists in various clinical situations. This prospective observational single-center study included 184 patients hospitalized in an internal medicine department who underwent conventional abdominal ultrasound. A medical resident with basic training performed point-of-care clinical ultrasound using a pocket-sized device. The concordance obtained between the researcher and the radiologist was good (k >0.6) for the gallbladder, splenomegaly, longitudinal diameter of the kidney, presence of renal cysts and hydronephrosis. The specificity was >90% for all parameters assessed except normal renal size. A negative predictive value >90% was obtained for all variables studied except the presence of hepatic space-occupying lesions and gallbladder pathology, the negative predictive values for which were >80%. A positive predictive value >80% was obtained for all of these variables, except the presence of adenopathies, hepatomegaly, space-occupying lesions, echogenicity and/or enlargement of the biliary tract, left renal atrophy and right renal masses. We conclude there was a high concordance between a conventional abdominal study and that performed with a pocket-sized ultrasound device after a brief learning curve.

Left Ventricular Diastolic Dysfunction in Type 2 Diabetes-Progress and Perspectives

In-depth understanding of early cardiovascular manifestations in diabetes is high on international research and prevention agendas given that cardiovascular events are the leading cause of death for diabetic patients. Our aim was to review recent developments in the echocardiographic assessment of left ventricular diastolic dysfunction (LVDD) as a telltale pre-clinical disturbance preceding diabetic cardiomyopathy. We analyzed papers in which patients had been comprehensively assessed echocardiographically according to the latest LVDD guidelines (2016), and those affording comparisons with previous, widely used recommendations (2009). We found that the updated algorithm for LVDD is more effective in predicting adverse cardiovascular events in patients with established LVDD, and less specific in grading other patients (labelled "indeterminate"). This may prove instrumental for recruiting "indeterminate" LVDD cases among patients with type 2 diabetes mellitus (T2DM) in future screening programs. As an interesting consideration, the elevated values of the index E/e' can point to early diastolic impairment, foretelling diabetic cardiomyopathy. Identifying subclinical signs early makes clinical sense, but the complex nature of T2DM calls for further research. Specifically, longitudinal studies on rigorously selected cohorts of diabetic patients are needed to better understand and predict the subtle, slow onset of cardiac manifestations with T2DM as a complicating backdrop.

Age-specific diastolic dysfunction improves prediction of symptomatic heart failure by Stage B heart failure

Aims

We investigated whether addition of diastolic dysfunction (DD) and longitudinal strain (LS) to Stage B heart failure (SBHF) criteria (structural or systolic abnormality) improves prediction of symptomatic HF in participants of the SCReening Evaluation of the Evolution of New Heart Failure study, a self‐selected population at increased cardiovascular disease risk recruited from members of a health insurance fund in Melbourne and Shepparton, Australia. Both American Society of Echocardiography and European Association of Cardiovascular Imaging (ASE/EACVI) criteria and age‐specific Atherosclerosis Risk in Communities (ARIC) study criteria, for SBHF and DD, and ARIC criteria for abnormal LS, were examined.

Methods and results

Inclusion criteria were age ≥60 years with one or more of self‐reported ischaemic or other heart disease, irregular or rapid heart rhythm, cerebrovascular disease, renal impairment, or treatment for hypertension or diabetes for ≥2 years. Exclusion criteria were known HF, or ejection fraction <50% or >mild valve abnormality detected on previous echocardiography or other imaging. Echocardiography was performed in 3190 participants who were followed for a median of 3.9 (interquartile range: 3.4, 4.5) years after echocardiography. Symptomatic HF was diagnosed in 139 participants at a median of 3.1 (interquartile range: 2.1, 3.9) years after echocardiography. ARIC structural, systolic, and diastolic abnormalities predicted HF in univariate and multivariable proportional hazards analyses, whereas ASE/EACVI structural and systolic, but not diastolic, abnormalities predicted HF. ARIC and ASE/EACVI SBHF criteria predicted HF with sensitivities of 81% and 55%, specificities of 39% and 76%, and C statistics of 0.60 (95% confidence interval: 0.57, 0.64) and 0.66 (0.61, 0.71), respectively. Adding ARIC DD to SBHF increased sensitivity to 94% with specificity of 24% and C statistic of 0.59 (0.57, 0.61), whereas addition of ASE/EACVI DD to SBHF increased sensitivity to 97% but reduced specificity to 9% and the C statistic to 0.52 (0.50, 0.54, P < 0.0001). Addition of LS to ARIC or ASE/EACVI SBHF criteria had minimal impact on prediction of HF.

Conclusions

Age‐specific ARIC DD criteria, but not ASE/EACVI DD criteria, predicted symptomatic HF, and addition of age‐specific ARIC DD criteria to ARIC SBHF criteria improved prediction of symptomatic HF in asymptomatic individuals with cardiovascular disease risk factors. Addition of LS to ASE/EACVI or ARIC SBHF criteria did not improve prediction of symptomatic HF.