Thursday, July 23, 2015

Heart disease - the 7.5% solution?

Statins are in the news again, and not just because of the new PCSK9-based drugs, at least one of which is likely to be approved by the FDA this week, probably for a small class of at-risk patients.  These drugs will drive LDL cholesterol levels through the floor, while generating an estimated 17.8 billion for pharma by the year 2023 (and that's before we even know whether they will reduce risk of heart attack and stroke).

No, this is about your run-of-the-mill class of LDL-lowering statins.  In late 2013, the American Heart Association and the American College of Cardiology recommended new guidelines for determining who should be on statins.
  • anyone who has cardiovascular disease, including angina (chest pain with exercise or stress), a previous heart attack or stroke, or other related conditions
  • anyone with a very high level of harmful LDL cholesterol (generally an LDL above greater than 190 milligrams per deciliter of blood [mg/dL])
  • anyone with diabetes between the ages of 40 and 75 years
  • anyone with a greater than 7.5% chance of having a heart attack or stroke or developing other form of cardiovascular disease in the next 10 years.
Risk score is based on the ASCVD calculator, which uses basic data (age, sex, total cholesterol, HDL cholesterol, systolic blood pressure, and smoking and diabetes status) to calculate risk.  Unlike the previous Adult Treatment Panel III (ATP III) guidelines which were based on a target LDL level and risk factors determined by the long-term Framingham heart disease study (using the Framingham Risk Calculator), these new guidelines were based on a risk profile.  With these new guidelines, it was thought that about 13 million additional Americans would benefit from statins, for a total of a third of all Americans.

study published in the Journal of the American Medical Association last week asks whether these guidelines were better at identifying at-risk individuals than the old ATP III guidelines.  The prospective study followed up 2435 people from the Framingham study who had never taken statins.  Based on the ATP III guidelines, 14% would have been 'eligible' compared with 39%, based on the 2013 guidelines.
The median follow-up was 9.4 (interquartile range, 8.1-10.1) years. There were a total of 74 (3.0%) incident CVD events (40 nonfatal myocardial infarctions, 31 nonfatal strokes, and 3 with fatal CHD) and 43 (1.8%) incident CHD events (40 nonfatal myocardial infarctions and 3 with fatal CHD).
Among those eligible for statin treatment by the ATP III guidelines, 6.9% (24/348) developed incident CVD compared with 2.4% (50/2087) among noneligible participants (HR, 3.1; 95% CI, 1.9-5.0; P less than .001). Applying the ACC/AHA guidelines, among those eligible for statin treatment, 6.3% (59/941) developed incident CVD compared with only 1.0% (15/1494) among those not eligible (HR, 6.8; 95% CI, 3.8-11.9; P less than .001). Therefore, the HR of having incident CVD among statin-eligible vs noneligible participants was significantly higher when applying the ACC/AHA guidelines’ statin eligibility criteria compared with the ATP III guidelines (P less than .001).
That is, according to this study, the 2013 ACC/AHA guidelines identified more people at risk of heart disease than the ATP III guidelines.  That's presumably progress in understanding heart disease risk, and so a good thing. (Does anyone else find the use of the word 'eligible' odd, though?  Like statins are a reward for passing the risk threshold?)

But why don't they ask about family history?  That is one of the most useful bits of data a physician can have about a patient's risk of heart disease (and other things).  Is it too cynical to suggest that acknowledging its usefulness might diminish the importance of what has been learned from the Framingham study?

Less cynically, one reason, though we don't know if the various investigators considered it in this way, is that family history integrates all factors, including those that are being specifically measured (like blood pressure, LDL levels, and so on). Whether they are genetic or environmental, they went into determining whether the relative had heart disease.  So counting family history and LDL, or for that matter, weight and BMI, also not included, may be redundant to an unknown extent.  For risk factors, this would perhaps inflate the apparent risk, but for protective factors the opposite.  But family history is debatably the best single factor, perhaps as important as all the test-battery factors.  At least, it's important to consider why that alone, or that somehow corrected for redundancy, should be a part of all of this.

So, apparently we don't know more about the causes of ASCVD now than we did before 2013, we're just evaluating what we know differently.  So, assuming that statins really do reduce risk of ASCVD, that more people are 'eligible' is thought to be a good thing.  Though, as the JAMA commentary on this article notes in urging increased treatment with statins, "Although a 10-year ASCVD risk threshold of 7.5% or higher might initially seem to be a low threshold, many, indeed most, CVD events occur among the low-risk members of the population."

Wait!  "Low-risk" is defined by us, based on what we know about heart disease!  Our understanding is clearly wrong if all these 'low-risk' people are really high-risk!  Not to mention that there's clearly a huge false-positive pool if a risk estimate of 7.5 out of 100 makes a person eligible for statins!  That means that 92.5 of those 100 people are taking statins even though they weren't going to have a stroke or heart attack.  And, all this means, at least to me, that we really don't understand what causes heart attacks or stroke. The Framingham study identified cholesterol, particularly LDL, as a risk factor, but we're not really sure why, and we don't know what levels are in fact most risky, and people with low LDL can have heart attacks, too.  Statins may or may not work by reducing LDL cholesterol, and lower LDL cholesterol may or may not reduce risk.

And, statins can have serious side effects -- physical as well as the cost burden.  So, if of 100 people taking statins a large majority weren't going to have heart attacks anyway, statins are causing a lot of unnecessary side effects without preventing disease.  Though, to be fair, physicians can't predict the future, and must do their best with the information they have.  They don't know who will or won't have a heart attack, because epidemiology hasn't given them enough information.  They've got to treat people with 7.5% risk as if they are at 100% risk of disease.

So, it's not physicians who are failing here, it's epidemiologists.  But I'll even be fair to epidemiologists -- it's the methods, based on population data and probability (which may not even exist; see our series of posts on this starting here), that are failing.  Epidemiologists are doing their best with what they've got.  We don't know precisely what causes heart attacks, but to prevent them, we've got to treat people with low risk as though they are at high risk, and that's because some people at low risk really are at high risk.

No one has 7.5% of a heart attack.  They have 0% or 100% of a heart attack. Figuring out who is in which group is currently impossible.  What we do know for certain is that putting everyone on statins, as though they have 100% risk is very good for the pharmaceutical companies that make them, and good for people whose heart attack or stroke was prevented, even if we will never know which people these were, and unnecessary and even harmful for everyone else.

This is a lousy way to do medicine.  But it's currently the only way we've got.


Jari Stengard said...


You (and Ken?) are missing one important issue here; what is THE goal of prevention programs!

In epidemiology and public health programs the goal of intervention and prevention acts is to reduce health burden of a disesase (such as CVD) in a population at large. A measure of succes is usually mortality but other measures also may be used. Mammograpfy screening in a population is justified (and to my knowledge no one is questionning it) because it is found to reduce breast cancer mortality in population at large, for instance. In contrast, PSA screening is not officially recommended because it has not -yet- demonstrated to reduce prostata cancer mortality in a population at large.

Screening is not a medical act. A doctor does not refer a woman to mammography to lower her probability to breast cancer. Screening searches population's not an individual's health and best!

Similarly we should distinguish CVD prevention programs, where LDL, RR and other risk factors are tested and screened systematically from everyone in a target population and high risk individuals are treated adequatelly and medical practice, where a doctor refers her/his patient to a risk factor test and treats high risk individuals. If I remember correctly, secondary prevention of CVD patients serves as an example of the former case and blood pressure and cholesterol treatment of all CVD patients is found to reduce CVD mortality.

In contrast, epidemiological studies provide only risk score for the doctors in the latter case but they do not justify treatment because we do not know, how such a random risk factor measurement and treatment influence on CVD morbidity (or mortality) in a popultion at large. Actually, I was a bit surpriced that you didn't address in your great blog, what is the population of inference of the ~2000 study population and wheter the result is replicaple! As you may know, replicability is an important issue in stdies which are designed to search genetic "determinants" of CVD and other common diseases. Why we do not require same replicability when we search indications for statin treatment (or for other medical interventions).

Shortly, an use of epidemiological results in medical practice as you describe is opinion based, not evidence based medicine. Unfortunately such an opinion based medicine is common because "it makes sence" and can be easily used for drug markeing purposes.


Anne Buchanan said...

Thanks, Jari. You make excellent points. Indeed, I think the difference between the purpose of epidemiology and the purpose of screening and preventive care is huge, and the overlap between the two hard to determine. How do physicians translate population data from epidemiological studies into care for individual patients? Especially when epidemiological studies are so often inconclusive or contradictory. So, yes, which to believe is opinion-based, as you say. 'Risk' is such a fuzzy concept, and I imagine it's good practice to treat everyone at risk as though they are at equally high risk (though the 2013 guidelines do recommend different statin dosages for people who have high and low risk scores, unlike the earlier guidelines).

There has been some discussion of N=1 studies lately, but, again as you point out, replication is important in determining cause and effect, as well as treatment benefit, so I think population-based studies have to be here to stay, despite their many problems.

I would only differ with you about whether everyone agrees about the benefits of mammogram screening. Just last week the Los Angeles Times had a story about this. "We found that counties that screened more found significantly more breast cancer. But there was no relationship between how much a county screened for breast cancer and how likely its female residents were to die from breast cancer."

Guillaume Batolième said...

What role does additive genetic effects have in the causation and heritability of certain traits? Like say heart disease, or maybe other complex traits like intelligence or cognitive ability?

Peter Visscher is famously a proponent of the additive model and defends it in here:

I was interesting in what the ecodevoevo folks have to say on this matter. I've always thought that at molecular level and cellular level additivity by itself wouldn't hold up and that additive as well as P = G+E models were somewhat simplistic, but I'm not an expert on the subject.

Ken Weiss said...

When so many sites are being tested, with so many statistical issues such as measurement error, data pooling, mainly low rarer allele frequencies, and the like, it is no surprise that individual site dominance or site interactions are poorly enough represented that statistically convincing interactions are hard to document. On the whole, additivity is not hard to understand. But this does not mean that for individuals interactions are not important--though essentially untestable with statistical methods.

If you intercross two strains of inbred animals, so that there is not much variation across the genome you see plenty of single-locus dominance (non-additivity), and from a molecular point of view it is most unlikely that individual genotype interactions are actually additive.

But with so many interactions, differing in each individual in the kinds of data that the Visscher analysis factory churns out, it is not surprising, nor misleading that interactions are a small fraction of the total pattern assessed statistically. Those who suggest that much of the 'missing' heritability is due to epistasis haven't produced much convincing evidence, and again it is ferociously hard to document statistically. Still, prediction of traits in individuals could be seriously undermined by making group-based additivity assumptions.

In this sense, I think there is a big difference between statistical association in large samples over huge data sets, and what is going on at the molecular level, where true additivity is not easy to imagine (for me, anyway). Aggregates do not necessarily represent the situation in individuals.

Dr. Michael Kammenbaum said...

@Ken Weiss

Of course, there’s no reason to expect that these effects should be literally additive if we had the God’s eye view. Everything in biology is non-linear on a large enough scale. But conversely, everything is linear on a small enough scale. The point is that for many complex traits, the ratio of the typical interaction effect size to the number of approximately independent effects is small enough that an additive model will do a good enough job– you typically won’t have enough power to justify tacking on interaction terms anyway. Whether there are deep reasons issuing from genome architecture to expect this a priori is another question, but that is what the data seems to say.

Dr. Michael Kammenbaum said...

So what I mean to say is, complex traits with a genetic background like SES (which we have identified genes for) are very likely instantiated by mostly additive effects, according to data.