Perspectives on Implementing a Multidomain Approach to Caring for Older Adults With Heart Failure

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153220/1/jgs16183_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153220/2/jgs16183-sup-0001-supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153220/3/jgs16183.pd

Omecamtiv mecarbil in Black patients with heart failure and reduced ejection fraction: insights from GALACTIC-HF

Background: Omecamtiv mecarbil improves cardiovascular outcomes in patients with heart failure (HF) with reduced ejection fraction (EF). Consistency of drug benefit across race is a key public health topic. Objectives: The purpose of this study was to evaluate the effect of omecamtiv mecarbil among self-identified Black patients. Methods: In GALACTIC-HF (Global Approach to Lowering Adverse Cardiac Outcomes Through Improving Contractility in Heart Failure) patients with symptomatic HF, elevated natriuretic peptides, and left ventricular ejection fraction (LVEF) ≤35% were randomized to omecamtiv mecarbil or placebo. The primary outcome was a composite of time to first event of HF or cardiovascular death. The authors analyzed treatment effects in Black vs White patients in countries contributing at least 10 Black participants. Results: Black patients accounted for 6.8% (n = 562) of overall enrollment and 29% of U.S. enrollment. Most Black patients enrolled in the United States, South Africa, and Brazil (n = 535, 95%). Compared with White patients enrolled from these countries (n = 1,129), Black patients differed in demographics, comorbid conditions, received higher rates of medical therapy and lower rates of device therapies, and experienced higher overall event rates. The effect of omecamtiv mecarbil was consistent in Black vs White patients, with no difference in the primary endpoint (HR = 0.83 vs 0.88, P-interaction = 0.66), similar improvements in heart rate and N-terminal pro–B-type natriuretic peptide, and no significant safety signals. Among endpoints, the only nominally significant treatment-by-race interaction was the placebo-corrected change in blood pressure from baseline in Black vs White patients (+3.4 vs −0.7 mm Hg, P-interaction = 0.02). Conclusions: GALACTIC-HF enrolled more Black patients than other recent HF trials. Black patients treated with omecamtiv mecarbil had similar benefit and safety compared with White counterparts

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Quantitative Electrocardiographic Measures and Long-Term Mortality in Exercise Test Patients With Clinically Normal Resting Electrocardiograms

BACKGROUND: Currently the only function of the resting electrocardiogram (ECG) in patients referred for exercise testing is to determine whether imaging is mandated. It is unknown if subtle ECG findings in those patients with clinically normal resting ECGs have prognostic significance. METHODS: We performed a single-center cohort study of 18,964 patients without known CVD, who had a clinically normal resting ECG and who underwent treadmill exercise testing for evaluation of suspected CAD. Eleven quantitative ECG measures related to heart rate, conduction, left ventricular mass, or repolarization were collected digitally. The primary outcome was all-cause mortality. The prognostic importance of a composite ECG score was assessed by measuring its impact on the c-index (analogous to area under ROC curve), and by measures of reclassification. RESULTS: During a median follow-up of 10.7 years 1,585 patients died. The four most predictive digital ECG variables were higher ventricular rate, more left-ward QRS axis, and more downward ST segment deviation, and longer QT interval. The ECG score was independently associated with mortality (75(th) vs. 25(th) percentile HR 1.36, 95% CI [1.25 to 1.49], P<.0001). The ECG score had modest impact on discrimination (change in c-index 0.04) and reclassification of risk (3.0% decrease of relative integrated discrimination improvement, P<.001). CONCLUSIONS: Subtle ECG findings relating to heart rate, conduction, left ventricular mass, or repolarization in patients with clinically normal ECGs referred for exercise testing may provide modest additional prognostic information over and above clinical and exercise measures

Cardiac resynchronization therapy-heart failure (CRT-HF) clinic: A novel model of care.

BackgroundPost-implant care of patients with heart failure (HF) undergoing cardiac resynchronization therapy (CRT) is not addressed in current HF or CRT guidelines and is often fragmented with poor communication between specialties. We sought to develop a new model of post-CRT care which could be implemented in busy clinical settings.Methods and resultsWe designed a novel, multidisciplinary approach to standardizing post CRT care. All patients receiving a CRT device at the Cleveland Clinic between March 2017 and August 2018 were invited to be seen in the clinic 6 months post implant. A one-time collaborative visit encompassing cardiac imaging, heart failure, and electrophysiology care was performed. We recorded the operational feasibility of the clinic in terms of patient throughput as well as patient characteristics, interventions, and new diagnoses made. Between September 2017 and February 2019, 150 patients were seen in the clinic. Of these, 125 patients had their index CRT implanted for standard indications and were included in the current analysis. Approximately 45 minutes were dedicated for each patient visit. Interventions in care were made in 95% of patients, with CRT non-responders offered a higher number of interventions as compared to responders (median 3 versus 2 interventions). Types of interventions were device-related (26% of population), medication-related (74%), and referral for alternate medical services (80%).ConclusionsMultidisciplinary post-implant care of patients with HF receiving CRT devices, regardless of CRT response status, is feasible and results in frequent medical interventions

Identifying Important Risk Factors for Survival in Patient With Systolic Heart Failure Using Random Survival Forests

BACKGROUND: Heart failure survival models are typically constructed using Cox-proportional hazards regression. Regression modeling suffers from a number of limitations, including bias introduced by commonly used variable selection methods. We illustrate the value of an intuitive, robust approach to variable selection, random survival forests (RSF), in a large clinical cohort. RSF is a potentially powerful extension of Classification and Regression Trees (CART), with lower variance and bias. METHODS AND RESULTS: We studied 2231 adult systolic heart failure patients who underwent cardiopulmonary stress testing. During a mean follow-up of 5 years, 742 patients died. Thirty-nine demographic, cardiac and noncardiac co-morbidity, and stress testing variables were analyzed as potential predictors of all-cause mortality. A RSF of 2000 trees was constructed, with each tree constructed on a bootstrap sample from the original cohort. The most predictive variables were defined as those near the tree trunks (averaged over the forest). The RSF identified peak VO(2), serum BUN, and treadmill exercise time as the three most important predictors of survival. The RSF predicted survival similarly to a conventional Cox-proportional hazards model (out-of-bag C-index of 0.705 for RSF vs 0.698 for Cox-proportional hazards model). CONCLUSIONS: A random survival forests model in a cohort of heart failure patients performed as well as a traditional Cox-proportional hazard model, and may serve as a more intuitive approach for clinicians to identify important risk factors for all-cause mortality

High-Dimensional Variable Selection for Survival Data

The minimal depth of a maximal subtree is a dimensionless order statistic measuring the predictiveness of a variable in a survival tree. We derive the distribution of the minimal depth and use it for high-dimensional variable selection using random survival forests. In big p and small n problems (where p is the dimension and n is the sample size), the distribution of the minimal depth reveals a “ceiling effect ” in which a tree simply cannot be grown deep enough to properly identify predictive variables. Motivated by this limitation, we develop a new regularized algorithm, termed RSF-Variable Hunting. This algorithm exploits maximal subtrees for effective variable selection under such scenarios. Several applications are presented demonstrating the methodology, including the problem of gene selection using microarray data. In this work we focus only on survival settings, although our methodology also applies to other random forests applications, including regression and classification settings. All examples presented here use the R-software package randomSurvivalForest