Clinical Adoption of Prognostic Biomarkers: The Case for Heart Failure

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Abstract

The recent explosion of scientific knowledge and technological progress has led to the discovery of a large array of circulating molecules commonly referred to as biomarkers. Biomarkers in heart failure (HF) research have been used to provide pathophysiologic insights, aid in establishing the diagnosis, refine prognosis, guide management, and target treatment. However, beyond diagnostic applications of natriuretic peptides, there are currently few widely recognized applications for biomarkers in HF. This represents a remarkable discordance considering the number of molecules that have been shown to correlate with outcomes, refine risk prediction, or track disease severity in HF in the past decade. In this article, we use a broad framework proposed for cardiovascular risk markers to summarize the current state of biomarker development for patients with HF. We use this framework to identify the challenges of biomarker adoption for risk prediction, disease management, and treatment selection for HF and suggest considerations for future research.

Section snippets

Framework for the development of new biomarkers in heart failure

The plethora of biomarkers in cardiovascular disease has necessitated a framework for the evaluation of emerging biomarkers in the context of clinical applications. Building on the original “benchmark criteria” for cardiovascular biomarkers initially proposed by Morrow and de Lemos in 2007,4 Maisel5 has recently proposed a revision to reflect the specific needs of the patients with HF and incorporate the possibilities of biomarker-guided targeted therapy and “biomonitoring” (Table 1). Similar

Current status of HF biomarker development

Recently, van Kimmenade and Januzzi7 have summarized the various domains of established and developing protein-based biomarkers in HF using a pathophysiologic classification. Realizing that several markers contribute to multiple pathways, we follow this classification in Table 2 to summarize the current development status of these markers. Similar to development of new drugs or devices, there are several steps in the process for an investigational biomarker to become a clinical tool for disease

Prognostic biomarkers in heart failure: building consistent evidence

The key to consistent (and comparable) evidence, with the ultimate goal to promote introduction of a new biomarker into practice, is reasonably consistent definitions of the population and the outcome of interest. For HF, these definitions have been drifting over time with little consistency even among contemporary studies.

The fallacy of “incremental value”

Currently, most investigational markers in cardiovascular medicine that target risk prediction undergo the scrutiny of demonstrating risk reclassification value—a concept that goes beyond an independent P value for prediction of outcomes or increment in C statistic.68 We expand on the risk reclassification in the next subheading. Because reclassification by definition requires a clinical risk prediction model as a yardstick to examine the added value of the new biomarker, the bar is invariably

Risk reclassification: the crossroads between statistical and clinical significance

Briefly, risk reclassification implies a change in the projected risk in the right direction with the use of the new marker as compared with the projected risk with the baseline, usually clinical model. This is accomplished when the biomarker-added model assigns higher risk to a substantial proportion of patients who eventually develop the event of interest and, conversely, lower risk to a substantial proportion of patients who do not develop the event. This is best clinically interpretable

Risk classification and clinical decision making in HF

For stable, eligible patients with stage D HF, decisions for LVAD implantation and/or listing for heart transplantation may be facilitated by 1-year mortality projections with multifactorial risk assessment tools.70 Therefore, prognosis refinement with biomarkers could impact decision making in this group. In fact, although biomarker-added models have not been endorsed for decisions in these patients, studies have suggested that biomarkers can meaningfully reclassify risk in these patients.8

Baseline vs serial biomarker measurements for risk assessment

Beyond serial measurements of BNP or NT-proBNP for HF status monitoring purposes, several other biomarkers have been evaluated in serial determinations as risk prediction tools. Cardiac troponins have been extensively studied both in acute77, 78, 79 and chronic80, 81 HF. Despite the potential of serial measurements to provide useful insights, especially in AHF, the issues in terms of a development framework are compounded with serial determinations. For example, in patients with AHF, troponins

Biomarkers for HF management

There are 3 main uses of biomarkers in the management of HF. First, biomarkers can be used as risk stratification tools as discussed above. Second, biomarkers can be used as biomonitoring tools with serial determinations to guide treatment intensity and facilitate decisions for management. In this respect, natriuretic peptide–guided therapy has been already tested extensively in clinical trials. The evidence, however, has been inconsistent thus far.82 A pooled data meta-analysis in 2009 showed

Biomarkers as therapeutic targets for HF treatment

Perhaps the most intriguing potential of biomarkers in HF is patient selection for specific therapies based on underlying pathophysiology as determined by biomarkers. Several post hoc analyses from clinical trials have shown that certain therapies benefit a specific subset of patients characterized by elevated levels of a relevant biomarker. For example, in a post hoc analysis from CORONA (Controlled Rosuvastatin Multinational Trial in Heart Failure), rosuvastatin treatment was associated with

Future directions

For biomarkers to become useful for risk stratification, a consensus is needed on a standardized framework for population and outcome definitions and a finite time horizon. Modeling of outcomes should be adapted to reflect the characteristics of the HF population of interest. On top of standard HF characteristics, a comorbidity score would be important to consider. Clinical application of risk stratification for decision making has persistent challenges before entering mainstream HF practice.

Statement of Conflict of Interest

All authors declare that there are no conflicts of interest.

Acknowledgments

Support: Supported in part by (a) PHS Grant UL1 RR025008 from the Clinical and Translational Science Award program, National Institutes of Health, National Center for Research Resources and (b) Grant 1U10HL110302-01 from the National Institutes of Health, National Heart, Lung, and Blood Institute.

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