CardioExchange Archive

CardioExchange’s John Ryan interviews John W. McEvoy regarding his recent review article, published in the American Journal of Cardiology, about how to interpret cardiac risk for an individual patient when risk estimates are more accurate for populations of patients.

Ryan: How do you explain the difficulties of risk prediction with patients?

McEvoy: This is a difficult but necessary exercise. So much of what we do in medicine is based on risk. Indeed, fully informing patients about the risks and benefits of their care depends on having at least a simple understanding of risk. In addition, putting risk scores aside, knowledge derived from randomized controlled trials regarding best practices in medical therapeutics is heavily influenced by the notion of risk. For example, randomized studies have shown that ACE inhibitors reduce the incidence of cardiac events among post-MI patients, but many individuals in the control arms of these studies did not experience events — and some in the treatment arm did. It is sobering to recognize how much of what we do is based on probability and uncertainty.

One of the reasons my coauthors and I were motivated to write the review article is that the concept of individual risk applied — truly applied — to any given person is an oxymoron. Risk for an individual is like a square peg for a round hole. We can never know, or estimate, one person’s risk. In fact, if you do the math, the confidence interval for a given risk estimate in one person would range from a 0% to a 100% chance of a cardiac event. Thus, risk is not “personalized” and I think of risk as a “group-phenomenon.” I am sure that many other physicians do, too.

This may not seem new or too important to some CardioExchange readers, but to me it does say one important thing: I cannot present risk to my individual patients as “your” risk. Thus, I never say, “Mr. Jones, your risk of a heart attack is 15% over the next 10 years,” because I cannot vouch for the statement’s accuracy. Instead, I present the concept this way: “Mr. Jones, if I were to take 100 patients exactly like you, 15 of them, on average, would have a heart attack in 10 years.” Perhaps this approach is too pedantic, but I happen to think it is a more honest message. Along those lines, I highly recommend the Mayo clinic’s statin treatment decision app (statindecisionaid.mayoclinic.org ). It is a user-friendly, simple tool that helps to inform patients about this important matter, especially regarding risk as a group phenomenon.

It’s debatable whether the notion that individual risk is an oxymoron goes any further than this simple change in how I choose to present risk to my patients. Ultimately, I cannot say that the individual-risk problem is an issue when it comes to allocating therapies. As doctors, we all have more than one patient. Over the daily, weekly, and yearly course of our work, we participate in decisions for large groups of patients. Thus, the use of risk to guide therapeutic decisions will, on average, be accurate for our entire group of patients.

Ryan: With the Schrödinger cat comparison in your article, I think you have to look at the experiment from the point of view of the cat and, in this case, the patient. The issue is not whether disease is present but, rather, if the patient is at risk for a cardiac event and what that means to the patient. Is that a fair critique?

McEvoy: Maybe I should answer this question with two other questions: What puts the patient at risk for the cardiac event that he or she fears? Can the patient be at risk for a cardiac event if he or she does not have the disease (atherosclerosis)?

I would venture that the answer to the first question is “disease” and that the answer to the second question is “no.” Therefore, I think that understanding disease burden, assuming it is actually knowable for a given patient, can be useful. If the patient really wants to know (without a doubt) about his or her cardiac status, I can noninvasively measure the disease with good accuracy (e.g., with coronary artery calcium [CAC]). However, I can never estimate that person’s individual risk to as high a level of certainty.

Again, this is mostly pedantic thinking, and I don’t usually let this influence my management — unless I think a given patient could be an outlier and the risk-prediction algorithm puts that patient in the wrong risk group (e.g., because he or she has a strong family history, a type A personality, even an earlobe crease!).

Some critics would say that everyone has some atherosclerosis, so knowing it is present is not useful. However, the extent and burden of disease varies widely, and we know from CAC studies that patients with a low burden of disease (atherosclerosis), as reflected by zero CAC, have exceedingly low event rates. Thus, if I want to be certain about something (or if something about the patient makes me think he or she is a risk outlier), then I know I can at least be certain about his or her presence and extent of disease (by getting a CAC score). This contrasts, if you ask me, with the inherent uncertainty of a risk estimate for a given person.

Again, this underlying reality does not affect my usual day-to-day care, but I do keep it in mind if something about a patient is worrying me. Unfortunately, as a cardiology community, we do not know whether being certain about the presence or absence of disease (e.g., through CAC testing) can influence outcomes as part of a therapeutic strategy. Thus, I think we really need a CAC trial to be supported and conducted, if for no other reason than to build a better evidence base for a test that physicians are increasingly ordering and patients are increasingly seeking.

Our review paper was meant as an interesting platform for this discussion. The physics analogies will not suit everyone’s taste, but I also know that many readers really enjoyed them.