Daniel H. Katz

Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction

Background— Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (phenomapping) could identify phenotypically distinct HFpEF categories. Methods and Results— We prospectively studied 397 patients with HFpEF and performed detailed clinical, laboratory, ECG, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering, to define and characterize mutually exclusive groups making up a novel classification of HFpEF. All phenomapping analyses were performed by investigators blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years; 62% were female; 39% were black; and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (eg, phenogroup 3 had an increased risk of HF hospitalization [hazard ratio, 4.2; 95% confidence interval, 2.0–9.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF phenogroup classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107). Conclusions— Phenomapping results in a novel classification of HFpEF. Statistical learning algorithms applied to dense phenotypic data may allow improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.

Prognostic Importance of Pathophysiologic Markers in Patients With Heart Failure and Preserved Ejection Fraction

Background— Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome associated with multiple pathophysiologic abnormalities, including left ventricular (LV) diastolic dysfunction, longitudinal LV systolic dysfunction, abnormal ventricular-arterial coupling, pulmonary hypertension, and right ventricular (RV) remodeling/dysfunction. However, the relative prognostic significance of each of these pathophysiologic abnormalities in HFpEF is unknown. Methods and Results— We prospectively studied 419 patients with HFpEF using echocardiography and sphygmomanometry to assess HFpEF pathophysiologic markers. Cox proportional hazards analyses were used to determine the associations between pathophysiologic markers and outcomes. Mean age was 65±12 years; 62% were women; 39% were black; comorbidities were common; and study participants met published criteria for HFpEF. RV abnormalities were frequent: 28% had abnormal tricuspid annular plane systolic excursion, 15% had reduced RV fractional area change, and 34% had RV hypertrophy. During a median follow-up time of 18 months, 102 (24%) were hospitalized for HF and 175 (42%) experienced the composite end point of cardiovascular hospitalization or death. Decreased LV compliance, measured as reduced LV end-diastolic volume at an idealized LV end-diastolic pressure of 20 mm Hg (EDV20), and RV remodeling, as indicated by increased RV wall thickness, were the 2 pathophysiologic markers most predictive of worse outcomes: adjusted hazard ratio per 1 SD decrease in EDV20=1.39 (95% confidence interval [CI], 1.10–1.75; P=0.006), and hazard ratio per 1 SD increase in RV wall thickness=1.37 (95% CI, 1.16–1.61; P<0.001). These associations persisted after additional adjustment for markers of HF severity. By contrast, markers of LV relaxation, longitudinal LV systolic dysfunction, and ventricular-arterial coupling were not significantly associated with adverse outcomes. Conclusions— In patients with HFpEF, reduced LV compliance and RV remodeling are the strongest pathophysiologic predictors of adverse outcomes.

Phenotypic Spectrum of Heart Failure with Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome, with several underlying etiologic and pathophysiologic factors. The heterogeneity of the HFpEF syndrome may explain why (1) diagnosing and treating HFpEF is so challenging and (2) clinical trials in HFpEF have failed thus far. Here we describe 4 ways of categorizing HFpEF based on pathophysiology, clinical/etiologic subtype, type of clinical presentation, and quantitative phenomics (phenomapping analysis). Regardless of the classification method used, improved phenotypic characterization of HFpEF, and matching targeted therapies with specific HFpEF subtypes, will be a critical step towards improving outcomes in this increasingly prevalent syndrome.

Association of Low-Grade Albuminuria With Adverse Cardiac Mechanics Findings From the Hypertension Genetic Epidemiology Network (HyperGEN) Study

Background— Albuminuria is a marker of endothelial dysfunction and has been associated with adverse cardiovascular outcomes. The reasons for this association are unclear but may be attributable to the relationship between endothelial dysfunction and intrinsic myocardial dysfunction. Methods and Results— In the Hypertension Genetic Epidemiology Network (HyperGEN) Study, a population- and family-based study of hypertension, we examined the relationship between urine albumin-to-creatinine ratio (UACR) and cardiac mechanics (n=1894, all of whom had normal left ventricular ejection fraction and wall motion). We performed speckle-tracking echocardiographic analysis to quantify global longitudinal, circumferential, and radial strain, and early diastolic (e′) tissue velocities. We used E/e′ ratio as a marker of increased left ventricular filling pressures. We used multivariable-adjusted linear mixed effect models to determine independent associations between UACR and cardiac mechanics. The mean age was 50±14 years, 59% were female, and 46% were black. Comorbidities were increasingly prevalent among higher UACR quartiles. Albuminuria was associated with global longitudinal strain, global circumferential strain, global radial strain, e′ velocity, and E/e′ ratio on unadjusted analyses. After adjustment for covariates, UACR was independently associated with lower absolute global longitudinal strain (multivariable-adjusted mean global longitudinal strain [95% confidence interval] for UACR Quartile 1 = 15.3 [15.0–15.5]% versus UACR Q4 = 14.6 [14.3–14.9]%, P for trend <0.001) and increased E/e′ ratio (Q1 = 25.3 [23.5–27.1] versus Q4 = 29.0 [27.0–31.0], P=0.003). The association between UACR and global longitudinal strain was present even in participants with UACR < 30 mg/g (P<0.001 after multivariable adjustment). Conclusions— Albuminuria, even at low levels, is associated with adverse cardiac mechanics and higher E/e′ ratio.

Albuminuria is independently associated with cardiac remodeling, abnormal right and left ventricular function, and worse outcomes in heart failure with preserved ejection fraction.

OBJECTIVES The purpose of this study was to determine the relationship between albuminuria and cardiac structure/function in heart failure with preserved ejection fraction (HFpEF). BACKGROUND Albuminuria, a marker of endothelial dysfunction, has been associated with adverse cardiovascular outcomes in HFpEF. However, the relationship between albuminuria and cardiac structure/function in HFpEF has not been well studied. METHODS We measured urinary albumin-to-creatinine ratio (UACR) and performed comprehensive echocardiography, including tissue Doppler imaging and right ventricular (RV) evaluation, in a prospective study of 144 patients with HFpEF. Multivariable-adjusted linear regression was used to determine the association between UACR and echocardiographic parameters. Cox proportional hazards analyses were used to determine the association between UACR and outcomes. RESULTS The mean age was 66 ± 11 years, 62% were female, and 42% were African American. Higher UACR was associated with greater left ventricular mass, lower preload-recruitable stroke work, and lower global longitudinal strain. Higher UACR was also significantly associated with RV remodeling (for each doubling of UACR, RV wall thickness was 0.9 mm higher [95% confidence interval: 0.05 to 0.14 mm; p = 0.001, adjusted p = 0.01]) and worse RV systolic function (for each doubling of UACR, RV fractional area change was 0.56% lower [95% confidence interval: 0.14 to 0.98%; p = 0.01, adjusted p = 0.03]. The association between UACR and RV parameters persisted after the exclusion of patients with macroalbuminuria (UACR >300 mg/g). Increased UACR was also independently associated with worse outcomes. CONCLUSIONS In HFpEF, increased UACR is a prognostic marker and is associated with increased RV and left ventricular remodeling and longitudinal systolic dysfunction. (Classification of Heart Failure With Preserved Ejection Fraction; NCT01030991).

Addressing statin adverse effects in the clinic: the 5 Ms.

With the release of the 2013 American College of Cardiology/American Heart Association (ACC/AHA) Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults, emphasis has been placed on using evidence-based intensity of therapy to reduce atherosclerotic cardiovascular disease (ASCVD) risk, rather than focusing on goal cholesterol levels. Before initiating therapy, however, it is critical that physicians and patients discuss 4 key topics: (1) the benefit of ASCVD risk reduction, (2) medication adverse effects, (3) drug–drug interactions, and (4) patient preferences. To facilitate discussion of statin adverse effects, we present here an evidence-based review of the 5 Ms of statin adverse effects: metabolism, muscle, medication interactions, major organ effects, and memory. “Metabolism” represents the small risk of new-onset diabetes that comes with statins, which is highest in those with diabetes risk factors. “Muscle” requires discussion of the wide range of muscle symptoms that occur with statins but emphasizes that these have been no more prevalent than those experienced with placebo in randomized controlled trials (RCTs). “Medication interactions” emphasize that statins interact with numerous medications. Interaction profiles vary widely between statins, and patients should be made aware of the most common interactions with their prescription. “Major organ effects” prompt the physician to review the possibility of a transient transaminitis as well as the recent observation of rare acute kidney injury with statin use. Both are rare and do not require routine monitoring. Finally, “memory” references the recent observational data suggesting statins may contribute to memory loss and confusion, both of which have not been observed in RCTs and resolve with drug cessation. Reviewing these common effects has the possibility to strengthen the doctor–patient relationship and boost both medication adherence and patient satisfaction.

Archeological Echocardiography: Digitization and Speckle Tracking Analysis of Archival Echocardiograms in the HyperGEN Study

Several large epidemiologic studies and clinical trials have included echocardiography, but images were stored in analog format and these studies predated tissue Doppler imaging (TDI) and speckle tracking echocardiography (STE). We hypothesized that digitization of analog echocardiograms, with subsequent quantification of cardiac mechanics using STE, is feasible, reproducible, accurate, and produces clinically valid results.

Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction

We sought to evaluate whether unbiased machine learning of dense phenotypic data (“phenomapping”) could identify distinct hypertension subgroups that are associated with the myocardial substrate (i.e., abnormal cardiac mechanics) for heart failure with preserved ejection fraction (HFpEF). In the HyperGEN study, a population- and family-based study of hypertension, we studied 1273 hypertensive patients utilizing clinical, laboratory, and conventional echocardiographic phenotyping of the study participants. We used machine learning analysis of 47 continuous phenotypic variables to identify mutually exclusive groups constituting a novel classification of hypertension. The phenomapping analysis classified study participants into 2 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, and indices of cardiac mechanics (e.g., phenogroup #2 had a decreased absolute longitudinal strain [12.8 ± 4.1 vs. 14.6 ± 3.5%] even after adjustment for traditional comorbidities [p < 0.001]). The 2 hypertension phenogroups may represent distinct subtypes that may benefit from targeted therapies for the prevention of HFpEF.