Why Risk Screening For Heart Disease Is As Good As Crystal Ball Gazing

If weather forecasts were as reliable as cardiovascular risk prediction tools, meteorologists would miss two thirds of all hurricanes, expect rain for 8 out of 10 sunny days, and fail to see the parallels to fortune telling.    

When you are older than 35 and visit your doctor, there is a good chance he will evaluate your risk of suffering a heart attack or stroke over the next 10 years. The motivation behind this risk scoring is to prevent such an event while you still can. After all, these cardiovascular diseases are the number one causes of disability and death. In Europe alone 1.8 Million people die from it every year. In fact, they die prematurely, which means at an age younger than 75. [tweet this].


That's why, at first blush, it sounds reasonable to develop risk prediction scores to help doctors identify the high-risk patient whose asymptomatic state makes him blissfully unaware of being a walking time bomb. Forewarned is forearmed, or something like that the reasoning goes. But what if the forewarning part is as reliable as a six weeks weather forecast and the forearming as effective as the wish for world peace?

As with any medical technology, risk prediction tools should be judged by their ability to improve YOUR health outcome before they are used on YOU. While the latest publication about the UK QRISK score is an upbeat evaluation of its improved performance, it fails to convince me that using these tools actually makes sense [1]. 

Let's look at the data first: 

The QRISK score was developed for the UK population, because the grand dame of risk prediction scores, the Framingham Risk Score (FRS), doesn't do so hot in northern European people. FRS was seen to over-predict the risk in the UK population by up to 50%. In an effort to do better than that, QRISK was developed. It packs a lot more variables into its score than FRS. In its latest version, QRISK includes the risk factors age, smoking status (with a 5-level differentiation), ethnicity, blood pressure, cholesterol, BMI, family history, socioeconomic status, and various disease diagnoses. An algorithm calculates your risk, expressed as a %-chance to suffer a heart attack or stroke over the next 10 years. 

In clinical practice a 20% risk is defined as the critical threshold that separates the high-risk person from those in the low-to-moderate risk categories. 20% is an entirely arbitrary number, selected simply for convenience's sake and economic reasons. Set it too high, and you identify too few at-risk people, set it too low and you have to deal with too many false positives, that is, people who you would treat for elevated risk but who will not suffer an event even if you didn't treat them. The latter is clearly a strain on limited health budgets.

Now, let's see how QRISK at a threshold of 20% risk would work for you, provided you are between 30 and 84 years old, which is the age range to which QRISK is applicable. Let's also assume you are female.  

For every 1000 women, 40 will suffer a first heart attack or stroke over the next 10 years. Of these 40 obviously high-risk, women, QRISK identifies 17 correctly. Which means the remaining 23, or 60% of all those who will suffer a heart attack or stroke, fly below the QRISK radar. But that's not the intriguing part. We get to that by looking at the group of women who are identified as high-risk. 

If the 20% risk score threshold predicts correctly, then about 20 of every 100 women identified as high-risk will suffer a first event over the next 10 years. After all, that's what a 20% risk means: Of a hundred women having the same profile, 20 will eventually suffer a first heart attack or stroke over the next 10 years. Which brings us to the really juicy part: In the population from which QRISK was developed, 16% of the high-risk women actually did suffer that predicted heart attack or stroke. 

You are forgiven if you don't immediately see, why I call this the juicy part. But think about it this way: The QRISK numbers were not plugged from an observational study, which simply observes and follows women for 10 years, without doing anything to or with them. These numbers represent women who were identified to be at high risk by the very health care system, which claims to do the risk scoring to protect them from such events in the first place. So, what happened to actually preventing those events? 16% vs. 20% doesn't sound like a terrific preventive job. 

By the way, for men the figures are very much the same. The reason why I chose women is because there is an inconsistency in the study's published tables which compare the events in two age groups - the 35-74 year old men, and the 30-80 year old men. The number of heart attacks and strokes is given as 54 and 50 for the first and second group respectively. But it can't be that there are less events in the 30-80 year range than in the 35-74 year range. Since there is no such detectable inconsistency in the numbers for women, I chose them as the example.  

Back to the risk score and a summary of its performance. First, the score misses 60% of all cases right off the bat. Second, among the correctly identified future sufferers of heart attacks and strokes, the subsequent treatment only prevents a small minority of events, which amounts to about 4% of all cases happening over the 10-year period.  If our preventive interventions were worth their salt, we should see no, or only a few, cases happening in the high-risk group. Because this is the group, which is supposed to benefit from intensive treatment and intervention. 

This public health strategy of targeting the high-risk part of the population with an intervention is appropriately called the high-risk strategy. As we have seen, it makes public health miss the majority of disease events, which it set out to prevent in the first place. So what is the alternative? It's called the population strategy. And, yes, it means targeting the entire population in an effort to reduce all people's exposure to whatever are the causes of the disease. That entails necessarily a one-size-fits-all approach to health. Which you encounter in the form of those exercise and diet recommendations preached to us from every public health pulpit. 

In theory, this strategy could potentially have a large effect on the health of the entire population, materializing as a substantial reduction in the number of heart attacks and strokes. But when you look at it from YOUR point of view, you have to invest the sizeable effort of changing your eating and exercising habits, while you'll find the benefits hardly perceivable. After all, health is when you don't feel it. A prevented disease is never perceived as such. In public health, this situation, where an individual's large perceived sacrifice yields only an imperceptibly small personal benefit, is called the prevention paradox. It's a more academic way of saying it doesn't work either.
    

The data are certainly there to prove my case. In my previous post I highlighted how little change in health behaviors has happened over the past 20 years. And the little change, that did happen, went mostly into the wrong direction. 

Which is why we will continue to see most of us dying, ironically, from preventable diseases: heart disease, stroke, diabetes, many cancers. Which is why I'm questioning the current clinical practice of risk scoring. After all, it costs money and time.

It's this question which has lead some researchers to suggest giving everybody above the age of 50 a so-called polypill. A pill which reduces blood pressure and cholesterol, and which delivers a low dose of aspirin. It aims at killing three birds with one stone: hypertension, hypercholesterolemia and thrombotic events, all of which are causally related to heart attack and stroke. But to me, the polypill is preventive medicine's declaration of bankruptcy.

In my next post, I will talk about this, about how preventive medicine may really work, and, most importantly, what it means to you. Practically and presently. Because we already have the tools to help you prevent your heart attack or stroke. And those tools don't go by the name of any known risk score. if you are still keen on scoring your risk, we have a tool on our website for you to do that. It also shows you, how your risk would be if all risk factors were in the green zone, or how your risk will be if you maintain your current status over the next ten years. You can play around with it here, and make a couple of other tests, too. But don't get fooled by numbers. Your greatest risk is to take those risk scores too seriously. 

Reference:

1. Collins, G.S. and D.G. Altman, Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ, 2012. 344.

Collins GS, & Altman DG (2012). Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ (Clinical research ed.), 344 PMID: 22723603

How to survive the health care system.

You have heard about good and bad cholesterol. You have heard that increasing the former and reducing the latter will cut your risk of heart disease. You will now hear what's principally wrong with this strategy of attacking risk factors. And how it prevents us from eradicating the heart disease epidemic sweeping the globe. 

On 30th November 2006, Jeff Kindler, the CEO of Pfizer, praised their about-to-be released drug for increasing good cholesterol as "...one of the most important compounds of our generation."

Three days later Pfizer halted the phase 3 clinical trial of its hoped-for blockbuster drug. The simple reason: the drug's ingredient, torcetrapib, did what it was supposed to: increase good cholesterol. But it also increased patients' risk for heart attack, stroke or death from any cause [1]. You probably see the common theme behind this and the story of my previous post: a drug improves a risk factor but worsens risk. Fortunately, Pfizer didn't cover that up.

Now, before you admire Pfizer as an outstanding citizen of the pharma world, let's look at their track record in the ethics department: In 2009 Pfizer pleaded guilty to a felony violation of the Food, Drug and Cosmetic Act for misbranding their drug Bextra and three others "with the intent to defraud or mislead".

The anti-inflammatory drug Bextra had been pulled off the market 4 years earlier, but Pfizer bribed its way into physicians' prescription blocks. The consequence: a $ 2.3 Billion criminal fine, the largest ever awarded. You would think of such a fine as putting a serious dent into a company's balance sheet. Well, 4th quarter profits 2008 were only 10% of what they used to be. Pfizer had made a provision for what they knew was coming. But compared to the $ 50 Billion in annual sales that's nothing over which a CEO would lose sleep.

Last month, Merck was fined $ 321 Million for similar offences related to Vioxx, a drug of the same class as Bextra.

Altogether, Big Pharma has been fined $ 8 Billion over the past 10 years for repeatedly defrauding the U.S. health care system. If they do it repeatedly, it's probably not because they are slow learners. It's because - 'cherchez l'argent' - there is money in it. If that's the case, then what has been brought into the open, may just be the proverbial tip of the iceberg. Now, an iceberg of monetary fraud is one thing, an iceberg of defrauding you of your health is quite another. Let me backup my suspicions, again with a recent example: Tamiflu

Roche's Tamiflu is the only orally administered influenza antiviral drug in its class (neuraminidase inhibitors). Governments all over the world had been stockpiling it before the 2009 influenza outbreak. So did the U.S. government at a cost of $ 1.5 Billion for Tamiflu and Relenza. 

Based on what evidence? Chiefly on a meta-analysis of 10 studies which, in 2003, informed the public that Tamiflu substantially reduced the need for antibiotics and also reduced the risk of developing serious complications [2]. What's wrong with that? That 8 of the 10 studies cited in this Roche-sponsored meta-analysis had not been published in peer-reviewed journals. That is, independent researchers have been unable to verify such claims. Not that they didn't try. The most authoritative organization for conducting meta-analyses is the independent Cochrane Collaboration. The Cochrane researchers Peter Doshi and colleagues attempted to verify the claims of the Kaiser meta-analysis, so named after its lead author. But despite repeated requests, Roche has remained uncooperative in sharing the full data reports, offering reasons of which Doshi and colleagues had to say that "none seemed credible" [3].

In a March 2012 interview with the Swiss newspaper Neue Zürcher Zeitung, the head of the German arm of the Cochrane Collaboration, Prof. Gerd Antes, enlightened readers about Roche's way of doctoring the Kaiser meta-analysis. Not only did Kaiser NOT have access to the 8 studies, Roche had simply given him their evaluation of these Roche sponsored studies' data. And the other two studies alone do NOT support the overall "result" of the meta-analysis. If it wasn't OUR tax dollars and OUR health, I'd say it was funny how governments all over the world are gullible (that's the polite version for 'stupid and reckless') enough to shovel billions of Dollars and Euros into Big Pharma's pockets. Based on nothing else than doctored data. With my small lab we are currently jumping through hoops to get a few ten thousand Euros from a government research fund for a project that will take us 2 years to accomplish. I'm not complaining, I just mention it for contrast.

If I was a conspiracy buff, which I am really not, I would suspect a put-up affair: Politicians first accept lobbyists' contributions for which in return they waste our money on useless, or even dangerous, drugs, after which they earn brownie points from us by publicly wrist-slapping Big Pharma for their deception with fines which look big but really are not. With Pfizer alone spending annually around $ 12 Million for lobbying to lawmakers, Roche and Merck  each $ 8 Million, this scenario doesn't look that far fetched. At least for Big Pharma the return on (lobbying-)investment appears obscenely good. 

Anyway, what you have seen in the case of Tamiflu is called publication bias. It means that positive results are more often reported and published than negative ones. This bias comes with a serious side effect: Meta-analyses and reviews, which typically inform medical practice guidelines, can only be as accurate as their knowledge base. If that base is biased, so will be the treatment or advice which you receive from your doctor.

How can we gauge the extent of this problem? That's surprisingly simple, provided you have access to files of the ethics boards and committees. Every study must be approved by an ethics committee before it can be carried out. Prof. Antes has this type of access. He and his Cochrane team found that of all studies, which received an ethics committee's approval since 2000, only 50% had their results published. Which simply means for every published study there is another one, which, in all likelihood, comes to a different conclusion with respect to effect and risk of its subject drug or treatment.

That is frightening. You might wonder how medicine can be such an inaccurate science. It's because we still know so little. Every biomarker, every hormone every molecule represents one small node in an immensely intricate network of biochemical pathways. At each node sebverlas pathways may intersect, some of them we know, many we don't. So when we develop a drug which manipulates one pathway through one of these nodes, we inadvertently interfere with other pathways. These effects may or may not turn up in the three phases of clinical trials. Recall the torcetrapib example, which I mentioned in the beginning of this post, and the glitazone example of the previous post.

That's why reducing risk is not as straight forward as reducing risk factors. Take the all-time favorite, high blood cholesterol. Almost two thirds of newly diagnosed Indian patients with coronary artery disease (CAD) have normal blood cholesterol levels [4]. Then there is the mother of all risk scores, the Framingham Risk Score (FRS). It uses age, gender, total cholesterol, good cholesterol, blood pressure, smoking and diabetes status to compute your 10-year risk for heart disease. But more than 40% of those people who develop the disease, are flying below the FRS radar. That is, they wouldn't have raised a red flag even if their doctor had examined them the day before their heart attack [5].

Now, you probably think that we are making progress in identifying more and better biomarkers to predict disease risk. Boy, are you wrong. In a review, published just 2 months ago, its authors rounded up 36 new biomarkers which are not already included in the FRS [6]. Of those 36, they investigated the 10 most promising, as determined by the numbers of studies published. Together these 10 had almost 123,000 studies to their names. Mind you, that's only the number of studies with a focus on cardiovascular disease! To what effect? None for most of them. And for the three with a moderate predictive value, BNP, CRP and fibrinogen, the authors found evidence for, you guessed it, bias.

Performing studies is no cheap business. So why burn money on biomarkers, which we know to be of little use?  Because they have great monetary value. Think about it. Once a biomarker, such as cholesterol, has been accepted as a risk predictor into clinical practice, developing drugs which improve that biomarker is the logical next step. And once the widespread use of the cholesterol lowering drug is found to correlate with some reduction of heart attacks and strokes, public health is happy with this drug. Because in public health, we are not concerned with you or with your personal health. We are concerned with the health of the population at large. Back to our example of the cholesterol lowering drug.  The more people qualify for its prescription, the larger the business of developing, producing and distributing this drug. Voila, here is Big Pharma's big business.

Now, let me ask you a hypothetical question: What would you be prepared to do to prevent a $ 1.8 Billion investment from going down the drain? That's the current cost estimate for bringing one drug to market [7]. It takes into account that only a fraction of the molecules, which show some promise in laboratory rats, will make it into a phase 1 clinical trial where they are tested on humans. And only one in 5 of those which make it to phase 1 trials will make it all the way to phase 3 and from there to your pharmacist. With more than 10 years for the entire process, mind you. So let me refine my question, what would you do with your drug which, in phase 3 trial, shows some effect but also a side effect in quite a number of participants? You would certainly NOT be tempted to hide these effects from the public, because you are such a nice person. But some other people, who also have shareholders  breathing down their necks, well, they might be tempted to swing the trial data in favor of the expected blockbuster earnings, don't you think?

Which brings us to the question: how can you survive this system with your health intact? The simple answer is, don't develop any risk factors in the first place. Stay out of this health care system. At least don't find yourself in it as a patient with a chronic condition. What that means to your chances at longevity and celebrating your 80th birthday in full health, you have read in my previous post  "When risk scores for heart attack really suck!"

In the next post I will tell you what your individual way to optimal health and longevity will look like. And how we, in my lab, work on making an individualized prevention strategy a reality, soon. For everyone. At least for everyone who wants it. Stay tuned. 

1.           Barter, P.J., et al., Effects of torcetrapib in patients at high risk for coronary events. New England Journal of Medicine, 2007. 357(21): p. 2109-22.

2.           Kaiser, L., et al., Impact of oseltamivir treatment on influenza-related lower respiratory tract complications and hospitalizations. Archives of Internal Medicine, 2003. 163(14): p. 1667-72.

3.           Doshi, P., T. Jefferson, and C. Del Mar, The imperative to share clinical study reports: recommendations from the tamiflu experience. PLoS Med, 2012. 9(4): p. e1001201.

4.           Achari, V. and A.K. Thakur, Association of major modifiable risk factors among patients with coronary artery disease--a retrospective analysis. Journal of the Association of Physicians of India, 2004. 52: p. 103-8.

5.           Wannamethee, S.G., et al., Metabolic syndrome vs Framingham Risk Score for prediction of coronary heart disease, stroke, and type 2 diabetes mellitus. Arch Intern Med, 2005. 165(22): p. 2644-50.

6.           Ioannidis, J.P.A. and I. Tzoulaki, Minimal and Null Predictive Effects for the Most Popular Blood Biomarkers of Cardiovascular Disease. Circulation Research, 2012. 110(5): p. 658-662.

7.           Paul, S.M., et al., How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov, 2010. 9(3): p. 203-214.

Research Blogging:
Barter, P., Caulfield, M., Eriksson, M., Grundy, S., Kastelein, J., Komajda, M., Lopez-Sendon, J., Mosca, L., Tardif, J., Waters, D., Shear, C., Revkin, J., Buhr, K., Fisher, M., Tall, A., & Brewer, B. (2007). Effects of Torcetrapib in Patients at High Risk for Coronary Events New England Journal of Medicine, 357 (21), 2109-2122 DOI: 10.1056/NEJMoa0706628
Kaiser, L. (2003). Impact of Oseltamivir Treatment on Influenza-Related Lower Respiratory Tract Complications and Hospitalizations Archives of Internal Medicine, 163 (14), 1667-1672 DOI: 10.1001/archinte.163.14.1667
Doshi P, Jefferson T, & Del Mar C (2012). The imperative to share clinical study reports: recommendations from the tamiflu experience. PLoS medicine, 9 (4) PMID: 22505850
Wannamethee, S. (2005). Metabolic Syndrome vs Framingham Risk Score for Prediction of Coronary Heart Disease, Stroke, and Type 2 Diabetes Mellitus Archives of Internal Medicine, 165 (22), 2644-2650 DOI: 10.1001/archinte.165.22.2644


Ioannidis JP, & Tzoulaki I (2012). Minimal and null predictive effects for the most popular blood biomarkers of cardiovascular disease. Circulation research, 110 (5), 658-62 PMID: 22383708

Paul, S., Mytelka, D., Dunwiddie, C., Persinger, C., Munos, B., Lindborg, S., & Schacht, A. (2010). How to improve R&D productivity: the pharmaceutical industry's grand challenge Nature Reviews Drug Discovery DOI: 10.1038/nrd3078
 

When risk scores for heart attack really suck!

When risk scores really suck.

If you are a man aged 55 or younger, or a woman aged 65 or younger and have had your risk for heart attack and stroke profiled recently, chances are your doctor told you that you have a low risk. So you probably walked out of her clinic, seeing no reason to change your lifestyle. Now here I am, the party pooper, who is going to rain on your parade. How so?

Well, first off, those risk scores, like the Framingham score used in the US and the PROCAM score used here in Germany, typically look at things like cholesterol, blood pressure, blood sugar, smoking status, age and gender. From these values the scores determine your 10-year forward risk. Conventionally, if your chances of suffering a heart attack, stroke or any other of the cardiovascular diseases endpoints is less than 10% for that 10-year period, yours is categorized as low-risk. If it was in excess of 20%, you would be considered a high-risk person, and anything in between is called moderate risk. Now here is the problem: of the women who are hospitalized for their first heart attack at an age younger than 65, typically none would have scored as high-risk even a day before the event [1].  In fact, 95% of these women would have flown under the risk radar in the low-risk altitude.

How come, you may ask. To understand the reason you need to know how heart attacks and strokes happen. Most of them are the result of a blood clot being formed at the site of a ruptured plaque (those fatty streaks) in one of your arteries. Traveling downstream these clots may be dissolved or they may be not. If they get stuck some place downstream, blocking the supply of blood, and thereby of oxygen, to your heart or brain tissue, a heart attack or stroke occurs. But most plaque ruptures do not cause a heart attack or stroke. There is a large element of chance involved. Fact of the matter is, we can't really predict which plaques will cause a heart attack or stroke. We can't even say whether a stable or a so-called vulnerable plaque will still be stable or vulnerable in a few months down the line. They can change their status. Which means, even if your doctor was able to map all the plaques in all the arteries throughout your body, he still wouldn't be able to tell you exactly your risk. How much less accurate will his risk prediction be when he uses risk factors which just correlate somewhat with plaque burden, such as cholesterol? There you go.  

Which is why you should not look at 10-year risk, but at lifetime risk. For a woman that risk stands at roughly 40% once she has reached the age of 50 [2]. Men, by the way have a 52% risk at that age. But here is the kicker: being free of any of the risk factors (those of the Framingham or PROCAM variety) at that age, means a dramatically lower lifetime risk of 8% and 5% for women and men respectively.

So here you are. Your doctor has just sent you off with a low-risk assurance for the next 10 years, even though 2 of your risk factors are elevated. You walk out of his clinic with a strong sense of invulnerability and no real motivation to change your lifestyle and to get those two risk factors back into the green zone. That's why risk scores really suck. When they rain on your parade later on it's a lot worse than if I, the party pooper, do it right now. Don't you think?

1.           Akosah, K.O., et al., Preventing myocardial infarction in the young adult in the first place: how do the National Cholesterol Education Panel III guidelines perform? Journal of the American College of Cardiology, 2003. 41(9): p. 1475-9.

2.           Lloyd-Jones, D.M., et al., Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age.Circulation, 2006. 113(6): p. 791-8.