The Trifecta of Precision Care in Heart Failure: Biology, Biomarkers, and Big Data

Implications of trial eligibility in patients with heart failure with mildly reduced or preserved ejection fraction

AIMS

Clinical trials in heart failure with mildly reduced or preserved ejection fraction (HFmrEF/HFpEF) commonly have detailed eligibility criteria. This may contribute to challenges with efficient enrolment and questions regarding the generalizability of trial findings.

METHODS AND RESULTS

Patients with HFmrEF/HFpEF from a large US healthcare system were identified through a computable phenotype applied in linked imaging and electronic health record databases. We evaluated shared eligibility criteria from five recent/ongoing HFmrEF/HFpEF trials (PARAGON-HF, EMPEROR-Preserved, DELIVER, FINE-ARTS, and SPIRRIT-HFpEF) and compared clinical and echocardiographic features as well as outcomes between trial-eligible and trial-ineligible patients. Among 5552 patients with HFpEF/HFmrEF, 792 (14%) were eligible for trial consideration, having met all criteria assessed. Causes of ineligibility included lack of recent loop diuretics (37%), significant pulmonary disease (24%), reduced estimated glomerular filtration rate (17%), recent stroke/transient ischaemic attack (13%), or low natriuretic peptides (12%); 53% of ineligible patients had >1 reason for exclusion. Compared with eligible patients, ineligible patients were younger (age 71 vs. 75 years, P < 0.001) with higher rates of coronary artery disease (66% vs. 59%, P < 0.001) and peripheral vascular disease (40% vs. 33%, P < 0.001), but less mitral regurgitation, lower E/e' ratio, and smaller left atrial sizes. Both eligible and ineligible patients demonstrated high rates of structural heart disease consistent with HFpEF [elevated left atrial size or left ventricular (LV) hypertrophy/increased LV mass], although this was slightly higher among eligible patients (95% vs. 92%, P = 0.001). The two cohorts demonstrated similar LV global longitudinal strain along with a similar prevalence of atrial fibrillation/flutter, hypertension, and obesity. Ineligible patients had similar all-cause mortality (33% vs. 33% at 3 years) to those eligible but lower rates of heart failure hospitalization (20% vs. 28% at 3 years, P < 0.001).

CONCLUSIONS

Among patients with HFmrEF/HFpEF from a large health system, approximately one in seven were eligible for major trials based on key criteria applied through a clinical computable phenotype. These findings highlight the large proportion of patients with HFmrEF/HFpEF ineligible for contemporary trials for whom the generalizability of trial findings may be questioned and further investigation would be beneficial.

Left Ventricular Ejection Fraction and the Future of Heart Failure Phenotyping

Heart failure (HF) is a complex syndrome traditionally classified by left ventricular ejection fraction (LVEF) cutpoints. Although LVEF is prognostic for risk of events and predictive of response to some HF therapies, LVEF is a continuous variable and cutpoints are arbitrary, often based on historical clinical trial enrichment decisions rather than physiology. Holistic evaluation of the treatment effects for therapies throughout the LVEF range suggests the standard categorization paradigm for HF merits modification. The multidisciplinary Heart Failure Collaboratory reviewed data from large-scale HF clinical trials and found that many HF therapies have demonstrated therapeutic benefit across a large range of LVEF, but specific treatment effects vary across that range. Therefore, HF should practically be classified by association with an LVEF that is reduced or not reduced, while acknowledging uncertainty around the precise LVEF cutpoint, and future research should evaluate new therapies across the continuum of LVEF.

Randomized Trial of Targeted Transendocardial Mesenchymal Precursor Cell Therapy in Patients With Heart Failure

BACKGROUND

Mesenchymal precursor cells (MPCs) are allogeneic, immunoselected cells with anti-inflammatory properties that could improve outcomes in heart failure with reduced ejection fraction (HFrEF).

OBJECTIVES

This study assessed the efficacy and safety of MPCs in patients with high-risk HFrEF.

METHODS

This randomized, double-blind, multicenter study evaluated a single transendocardial administration procedure of MPCs or sham-control in 565 intention-to-treat patients with HFrEF on guideline-directed therapies. The primary endpoint was time-to-recurrent events caused by decompensated HFrEF or successfully resuscitated symptomatic ventricular arrhythmias. Hierarchical secondary endpoints included components of the primary endpoint, time-to-first terminal cardiac events, and all-cause death. Separate and composite major adverse cardiovascular events analyses were performed for myocardial infarction or stroke or cardiovascular death. Baseline and 12-month echocardiography was performed. Baseline plasma high-sensitivity C-reactive protein levels were evaluated for disease severity.

RESULTS

The primary endpoint was similar between treatment groups (HR: 1.17; 95% CI: 0.81-1.69; P = 0.41) as were terminal cardiac events and secondary endpoints. Compared with control subjects, MPCs increased left ventricular ejection fraction from baseline to 12 months, especially in patients with inflammation. MPCs decreased the risk of myocardial infarction or stroke by 58% (HR: 0.42; 95% CI: 0.23-0.76) and the risk of 3-point major adverse cardiovascular events by 28% (HR: 0.72; 95% CI: 0.51-1.03) in the analysis population (n = 537), and by 75% (HR: 0.25; 95% CI: 0.09-0.66) and 38% (HR: 0.62; 95% CI: 0.39-1.00), respectively, in patients with inflammation (baseline high-sensitivity C-reactive protein ≥2 mg/L).

CONCLUSIONS

The primary and secondary endpoints of the trial were negative. Positive signals in prespecified, and post hoc exploratory analyses suggest MPCs may improve outcomes, especially in patients with inflammation.

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

Why has positive inotropy failed in chronic heart failure? Lessons from prior inotrope trials

Current pharmacological therapies for heart failure with reduced ejection fraction are largely either repurposed anti‐hypertensives that blunt overactivation of the neurohormonal system or diuretics that decrease congestion. However, they do not address the symptoms of heart failure that result from reductions in cardiac output and reserve. Over the last few decades, numerous attempts have been made to develop and test positive cardiac inotropes that improve cardiac haemodynamics. However, definitive clinical trials have failed to show a survival benefit. As a result, no positive inotrope is currently approved for long‐term use in heart failure. The focus of this state‐of‐the‐art review is to revisit prior clinical trials and to understand the causes for their findings. Using the learnings from those experiences, we propose a framework for future trials of such agents that maximizes their potential for success. This includes enriching the trials with patients who are most likely to derive benefit, using biomarkers and imaging in trial design and execution, evaluating efficacy based on a wider range of intermediate phenotypes, and collecting detailed data on functional status and quality of life. With a rapidly growing population of patients with advanced heart failure, the epidemiologic insignificance of heart transplantation as a therapeutic intervention, and both the cost and morbidity associated with ventricular assist devices, there is an enormous potential for positive inotropic therapies to impact the outcomes that matter most to patients.