Lessons learned from multi-scale modeling of the failing heart.

Heart failure constitutes a major public health problem worldwide. Affected patients experience a number of changes in the electrical function of the heart that predispose to potentially lethal cardiac arrhythmias. Due to the multitude of electrophysiological changes that may occur during heart failure, the scientific literature is complex and sometimes ambiguous, perhaps because these findings are highly dependent on the etiology, the stage of heart failure, and the experimental model used to study these changes. Nevertheless, a number of common features of failing hearts have been documented. Prolongation of the action potential (AP) involving ion channel remodeling and alterations in calcium handling have been established as the hallmark characteristics of myocytes isolated from failing hearts. Intercellular uncoupling and fibrosis are identified as major arrhythmogenic factors. Multi-scale computational simulations are a powerful tool that complements experimental and clinical research. The development of biophysically detailed computer models of single myocytes and cardiac tissues has contributed greatly to our understanding of processes underlying excitation and repolarization in the heart. The electrical, structural, and metabolic remodeling that arises in cardiac tissues during heart failure has been addressed from different computational perspectives to further understand the arrhythmogenic substrate. This review summarizes the contributions from computational modeling and simulation to predict the underlying mechanisms of heart failure phenotypes and their implications for arrhythmogenesis, ranging from the cellular level to whole-heart simulations. The main aspects of heart failure are presented in several related sections. An overview of the main electrophysiological and structural changes that have been observed experimentally in failing hearts is followed by the description and discussion of the simulation work in this field at the cellular level, and then in 2D and 3D cardiac structures. The implications for arrhythmogenesis in heart failure are also discussed including therapeutic measures, such as drug effects and cardiac resynchronization therapy. Finally, the future challenges in heart failure modeling and simulation will be discussed.

Longitudinal NT-proBNP: Associations With Echocardiographic Changes and Outcomes in Heart Failure.

BACKGROUND: The relationship of serial NT-proBNP (N-terminal pro-B-type natriuretic peptide) measurements with changes in cardiac features and outcomes in heart failure (HF) remains incompletely understood. We determined whether common clinical covariates impact these relationships. METHODS AND RESULTS: In 2 nationwide observational populations with HF, the relationship of serial NT-proBNP measurements with serial echocardiographic parameters and outcomes was analyzed, further stratified by HF with reduced versus preserved left ventricular ejection fraction, inpatient versus outpatient enrollment, age, obesity, chronic kidney disease, atrial fibrillation, and attainment of ≥50% guideline-recommended doses of renin-angiotensin system inhibitors and ß-blockers. Among 1911 patients (mean±SD age, 65.1±13.4 years; 26.6% women; 62% inpatient and 38% outpatient), NT-proBNP declined overall, with more rapid declines among inpatients, those with obesity, those with atrial fibrillation, and those attaining ≥50% guideline-recommended doses. Each doubling of NT-proBNP was associated with increases in left ventricular volume (by 6.1 mL), E/e' (transmitral to mitral annular early diastolic velocity ratio) (by 1.4 points), left atrial volume (by 3.6 mL), and reduced left ventricular ejection fraction (by -2.1%). The effect sizes of these associations were lower among patients with HF with preserved ejection fraction, atrial fibrillation, or advanced age (Pinteraction<0.001). A landmark analysis identified that an SD increase in NT-proBNP over 6 months was associated with a 27% increase in the risk of the composite event of HF hospitalization or all-cause death between 6 months and 2 years (adjusted hazard ratio, 1.27 [95% CI, 1.15-1.40]; P<0.001). CONCLUSIONS: The relationships between NT-proBNP and structural/functional remodeling differed by age, presence of atrial fibrillation, and HF phenotypes. The association of increased NT-proBNP with increased risk of adverse outcomes was consistent in all subgroups.

Heart Failure With Mid-range or Recovered Ejection Fraction: Differential Determinants of Transition.

The recent definition of an intermediate clinical phenotype of heart failure (HF) based on an ejection fraction (EF) of between 40% and 49%, namely HF with mid-range EF (HFmrEF), has fuelled investigations into the clinical profile and prognosis of this patient group. HFmrEF shares common clinical features with other HF phenotypes, such as a high prevalence of ischaemic aetiology, as in HF with reduced EF (HFrEF), or hypertension and diabetes, as in HF with preserved EF (HFpEF), and benefits from the cornerstone drugs indicated for HFrEF. Among the HF phenotypes, HFmrEF is characterised by the highest rate of transition to either recovery or worsening of the severe systolic dysfunction profile that is the target of disease-modifying therapies, with opposite prognostic implications. This article focuses on the epidemiology, clinical characteristics and therapeutic approaches for HFmrEF, and discusses the major determinants of transition to HFpEF or HFrEF.

Phenotype-Specific Association of Single-Nucleotide Polymorphisms with Heart Failure and Preserved Ejection Fraction: a Genome-Wide Association Analysis of the Cardiovascular Health Study.

Little is known about genetics of heart failure with preserved ejection fraction (HFpEF) in part because of the many comorbidities in this population. To identify single-nucleotide polymorphisms (SNPs) associated with HFpEF, we analyzed phenotypic and genotypic data from the Cardiovascular Health Study, which profiled patients using a 50,000 SNP array. Results were explored using novel SNP- and gene-centric tools. We performed analyses to determine whether some SNPs were relevant only in certain phenotypes. Among 3804 patients, 7 clinical factors and 9 SNPs were significantly associated with HFpEF; the most notable of which was rs6996224, a SNP associated with transforming growth factor-beta receptor 3. Most SNPs were associated with HFpEF only in the absence of a clinical predictor. Significant SNPs represented genes involved in myocyte proliferation, transforming growth factor-beta/erbB signaling, and extracellular matrix formation. These findings suggest that genetic factors may be more important in some phenotypes than others.

Exploring Guidelines for Classification of Major Heart Failure Subtypes by Using Machine Learning.

BACKGROUND: Heart failure (HF) manifests as at least two subtypes. The current paradigm distinguishes the two by using both the metric ejection fraction (EF) and a constraint for end-diastolic volume. About half of all HF patients exhibit preserved EF. In contrast, the classical type of HF shows a reduced EF. Common practice sets the cut-off point often at or near EF = 50%, thus defining a linear divider. However, a rationale for this safe choice is lacking, while the assumption regarding applicability of strict linearity has not been justified. Additionally, some studies opt for eliminating patients from consideration for HF if 40 < EF < 50% (gray zone). Thus, there is a need for documented classification guidelines, solving gray zone ambiguity and formulating crisp delineation of transitions between phenotypes. METHODS: Machine learning (ML) models are applied to classify HF subtypes within the ventricular volume domain, rather than by the single use of EF. Various ML models, both unsupervised and supervised, are employed to establish a foundation for classification. Data regarding 48 HF patients are employed as training set for subsequent classification of Monte Carlo-generated surrogate HF patients (n = 403). Next, we map consequences when EF cut-off differs from 50% (as proposed for women) and analyze HF candidates not covered by current rules. RESULTS: The training set yields best results for the Support Vector Machine method (test error 4.06%), covers the gray zone, and other clinically relevant HF candidates. End-systolic volume (ESV) emerges as a logical discriminator rather than EF as in the prevailing paradigm. CONCLUSIONS: Selected ML models offer promise for classifying HF patients (including the gray zone), when driven by ventricular volume data. ML analysis indicates that ESV has a role in the development of guidelines to parse HF subtypes. The documented curvilinear relationship between EF and ESV suggests that the assumption concerning a linear EF divider may not be of general utility over the complete clinically relevant range.