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Value-Based Care in Cardiometabolic Health: A Medical Director’s Perspective on the Inaugural Edition

Value-Based Care in Cardiometabolic Health May 2012, Vol 1, No 1 - Payers' Perspectives

I have eagerly awaited the opportunity to preview the first edition of Value-Based Care in Cardiometabolic Health. No area of medicine has more impact on patients, creates more use of medical resources, and drives cost more than cardiometabolic disease.

From a health plan perspective, cardiovascular disease (CVD) and diabetes are 2 of the most prevalent chronic conditions in the US population. Although we have made great strides in the diagnosis, prevention, and treatment of heart disease, it still remains the number one killer of Americans today. Similarly, diabetes is growing at epidemic proportions, following the wave of obesity in the United States. For most health plans, medical spending on CVD is the largest category of spending among the major chronic illnesses. And CVD drugs, including drugs for hyperlipidemia and hypertension, as well as diabetes drugs, account for a major portion of the resources spent under the pharmacy benefit.

According to the latest Express Scripts’ Drug Trend Report, drugs for diabetes, hyperlipidemia, and hypertension account for approximately 30% of the traditional pharmacy spending.1 A recent report from the Centers for Disease Control and Prevention estimates that 45% of adults in the United States have one of the conditions treated by medications in these top 3 therapy classes when diagnosed and undiagnosed prevalence is considered.2 And things may only get worse. We only have to look at the data published in an article by Huang and colleagues3 to get a sense of the impact diabetes will continue to have on the already financially challenged healthcare system.

It is imperative that health plans keep up with the latest developments in the field of cardiometabolic diseases, because of the major impact that these illnesses have on the plan’s resources. In addition to keeping abreast of the new developments in cardiometabolic health, we must also be able to understand the economic impact of changes in technology, and how changing technology could potentially add value to the plan’s members, both clinically and economically.

New Technologies

For instance, diagnostic testing modalities are areas of near-explosive growth. Health plans must assess these new technologies in a timely manner so as not to withhold coverage of promising technologies from their members, while at the same time balancing the fiscal responsibility of not wasting resources on ineffective or marginally effective new technology.

To help with this task, Value-Based Care in Cardiometabolic Health brings a wealth of information to my (virtual) desktop. For instance, in this issue we read about the use of a gene-expression test to assist in the evaluation of chest pain and about the use of galectin-3 to assess heart failure risk.

As we know, the evaluation of chest pain can be complex and costly. Current evaluative modalities can lead to invasive procedures in a significant number of patients, with up to 60% of those who undergo elective cardiac catheterization having minimal or no disease. This is an area that is ripe for a better way to stratify patients with disease from those who have minimal risk for a cardiac-related event. Reporting on work presented by Robert S. Schwartz, MD, FACC, we gain insight about a gene-expression test for use in symptomatic, nondiabetic patients. This “noninvasive whole blood test from the expression levels of 23 genes can identify patients unlikely to have coronary artery disease events or require revascularization procedures over the next 12 months.” It is most impressive that “at a threshold geneexpression score of ≤15, the negative predictive value of the gene-expression score was 91% for procedures within 30 days and 91% at 12 months.” Doctors now have access to a simple, noninvasive way to risk-stratify patients with nonacute chest pain more effectively, potentially avoiding costly and risky invasive procedures in patients who are unlikely to have significant disease.

Regarding heart failure, we learn that “a blood test for galectin-3, a unique protein that binds to carbo - hydrates known as ‘beta-galactosides,’ appears to be a reliable predictor of incident heart failure (HF) and of the response to treatment for HF.” Again, clinicians will have the potential to better risk-stratify those who are at risk for severe complications or even death from heart failure. These are just 2 examples of timely information on possible “game-changing” technology that should prove invaluable to busy medical and pharmacy directors.

Cost Considerations

In this issue we also learn about a simple, low-cost, glucose-insulinpotassium solution has the potential to save lives from those experiencing an acute coronary event when used in the field. On the cost side, there is a call to determine the cost-effectiveness of cardiac imaging procedures—one of the fastest growing areas of medical technology today.

Finally, I read with interest the article that reviews the physicians’ evolving role in value-based purchasing (page 1). Understanding this approach is essential for those of us who lead change in medical reimbursement as we try to move the system from a volume- based purchasing system to a quality-based system.

Keeping up with the technology, policy changes, and clinical advances in cardiology or diabetes care can be a daunting task. Yet we are called on to do this not only in these fields, but in all areas of medicine. After reviewing the content of this first issue of Value-Based Care in Cardiometabolic Health, I am optimistic that this journal will become a trusted resource for providers and payers who must keep abreast of new information in this most prominent area of chronic illness.


  1. Express Scripts. 2011 Drug Trend Report. April 2012. Accessed May 10, 2012.
  2. Fryar CD, Hirsch R, Eberhardt MS, et al. Centers for Disease Control and Prevention. Hypertension, high serum total cholesterol, and diabetes: racial and ethnic prevalence differences in US adults, 1999-2006. NCHS data brief. No 36. April 2010. Accessed May 7, 2012.
  3. Huang ES, Basu A, O’Grady MJ, Capretta JC. Using clinical information to project federal health care spending. Health Aff (Millwood). 2009;28:w978-w990. Epub 2009 Sept 1.
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Last modified: August 30, 2021