Can Personalized Medicine Save the Health Care System?
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Sandeep Vijan, MD, MS LDI Research Seminar- September 30, 2011
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Can Personalized Medicine Save the Health Care System?
- 7. Retinopathy risk vs. HbA1c
- 13. Probability model Patient MI Live No MI Live Die Die
- 23. Interpreting clinical trials Risk group Event rate: controls Event rate: intervention RRR ARR NNT All 6.0% 5.0% 17% 1% 100 High risk (20%) 15.0% 12.5% 17% 2.6% 39 Moderate risk (40%) 5.0% 4.2% 17% 0.85% 118 Low risk (40%) 2.5% 2.1% 17% 0.42% 238
- 24. Interpreting clinical trials Risk group Event rate: controls RRR ARR NNT Power All 5.6% 21% 1.1% 85 0.75 0 risk factors (n=1505) 1.6% -41% -0.6% -152 0.68 1 risk factor (n=3170) 3.2% 3% 0.0009 1042 2 risk factors (n=2673) 6.1% 23% 1.4% 71 3 risk factors (n=1152) 11.7% 35% 4.2% 24 4+ risk factors (n=300) 21.9% 36% 7.7% 13
- 25. Benefits of tPA vs. streptokinase (by percentile of benefit)
- 26. Retinopathy screening Risk of blindness (lifetime) Risk group No screening Annual screening High risk (Age 45, A1c 11%) 22.4% 10.1% Moderate risk (Age 65, A1c 9%) 2.7% 1.3% Low risk (Age 75, A1c 7%) 0.8% 0.4%
- 27. Retinopathy screening Screening interval (years) Risk group 1 2 3 4 5 High 10.1% 10.4% 10.7% 11.2% 11.8% Moderate 1.3% 1.4% 1.5% 1.7% 1.8% Low 0.4% 0.4% 0.5% 0.6% 0.6%
- 31. Cardiovascular prevention Population-based LDL-based Risk-based Total events prevented 0.75 million 0.79 million 1.56 million NNT to prevent one event 156 37 19
- 33. Cardiovascular prevention Strategy CV events prevented (millions, per 5 yrs) QALYs saved (millions, per 5 yrs) CV events prevented per 1000 persons treated QALYs saved per 1000 persons treated NCEP standard 1.67 1.83 44.0 48 NCEP intensive 2.39 2.40 44.9 45 Personalized 2.82 2.92 53.2 55
- 37. Genetic screening Average cost Average life expectancy CE ratio ($/year of life saved) No screening $1240 17.1215 yrs - No genetic testing $1680 17.1745 yrs $8,090 Genetic testing $1960 17.1751 yrs $465,000
- 40. Cost-effectiveness of colonoscopy Assumes 60% adherence to colonoscopy Stopping age Life expectancy (from age 50) Costs Incremental C-E ratio 60 17.1766 $1780 - 70 17.1790 $1820 $18,100 80 17.1791 $1850 $675,000
- 41. Cost-effectiveness of colonoscopy Assumes 100% adherence to colonoscopy Stopping age Life expectancy (from age 50) Costs Incremental C-E ratio 60 17.1947 $1950 - 70 17.1955 $2130 $234,300 80 17.1954 $2200 Net harm and higher cost
- 42. What about comorbidity? Assumes 60% adherence to colonoscopy and 2x normal mortality (e.g., diabetes) Stopping age Life expectancy (from age 50) Costs Incremental C-E ratio 60 14.0588 $1450 - 70 14.0589 $1520 $414,600 80 14.0588 $1530 Net harm and higher cost
- 50. Pictograph Risk of developing colorectal cancer with and without screening
Notas del editor
- Consider this scenario. There is an educational trial shows that teaching remedial reading to kids, on average, improves scores on reading tests. Your child, who is (of course) an advanced reader, is thus subjected to a standardized remedial reading class because it helps the average kid. I ’m sure it took most of you about 1 second to flatly reject this concept as appealing for your child; after all, Ann Arbor is like Lake Wobegon, where all the children are above average. As amusing as that sounds, it is certainly true: Ann Arbor test scores are higher than most because of the makeup of the population here. The usual standards thus make very little sense here.
- So, what are the problems with setting fixed targets like these? First, the targets - particularly for continuous measures such as A1c, BP, and LDL - are based on mean results from clinical trials. An obvious, but almost never cited point, is that fact that if the mean value in a trial is the target, then exactly 50% of the patients in a given clinical trial receive “bad” care - as half had to have values above the target. As stupid as this seems it’s a really hard point to get across to some. We then have to ask if it’s reasonable to assume that we can do better than a trial in usual clinical practice. Given that trials are invariably highly selective and have volunteer populations, this seems pretty far-fetched. Another issue is that the relationships between these types of measures and outcomes are most likely (though not definitively) log-linear.
- This is an example of a log-linear relationship from a clinical trial, the UK Prospective Diabetes Study. This is the only real study of glycemic control in type 2 diabetes. This graph shows the relationship between the risk of retinopathy progression and HbA1c. The main thing to note is that people with very high A1c levels get a lot more benefit from a reduction in A1c than those at lower levels. For example, patients who start at 12 and goto 11 have about a 6% absolute risk reduction in retinopathy progression. The “average” in this trial, from an A1c of 8 to 7, is about a 2% reduction. So the same 1% lowering of a1c gets you 3 times as much benefit if you start at 12 vs. starting at 8. You can imagine how just a few people at higher risk therefore substantially skew the average results of a trial.
- Here ’s a couple more issues with setting these targets. The targets are falsely dichotomized. The previous graph showed that the risk is continuous; it doesn’t go away if you achieve an A1c of 7, and it doesn’t suddenly jump up if you are above 7. However, if you judge quality on whether a target is met, you are equally bad if your a1c is 7.1% or if it’s 13%. This provides a pretty strong incentive to treat those with mild elevations; you’re not going to get that 13% to 7%, but you can certainly get the 7.2% there. The problem is that the patient who really needs treatment is the one at 13%, even if you only get to 9; they get 100 times the benefit. Another problem is that we assume that all of these are of equal importance, when they are clearly not. You don’t get the same benefit from an eye exam as you do from lowering blood pressure. Functionally these measures just don’t look at a patient as a whole.
- Even the measures that are “naturally” dichotmous, like whether you’ve had an eye exam, tend to be overly blunt. Does it really make sense that, regardless of your risk of developing retinopathy, colorectal cancer, or the flu, that we all should get exactly the same test? It defies common sense. There is no consideration of the underlying patient risk profile. In guideline and performance measure discussions, there is a constant refrain and a simplistic assumpton that providers just can’t handle complexity well, and that we should dumb down the measures to make them as simple as possible. Forgive me for giving most doctors a little more credit than that. We are trained specifically to make judgment calls about diagnosis and treatment; strict guidelines functionally remove this skill. Now, it is true that we are not very good at estimating probabilities of disease; we can get a general feel for, say “high, medium, or low” risk, but can’t quantify this well. Much of what I’ll talk about is how we can quantify risk of outcomes, and how this translates into usable tools to improve our decision making for individual patients, and make treatment more efficient. Before I do this, I want to review one more simple idea: why we provide medical care.
- Let me give you an example of over and undertreatment. The current cholesterol guidelines use LDL levels to guide treatment. (I ’ll talk more about some of our work here later). However, what we really are trying to do is prevent heart disease, not treat LDL. You can construct many scenarios where patients at very high risk don’t get treated. For example, a 50 year old male smoker, hypertensive, w/ a family hx of heart disease but an LDL of 100 wouldn’t be treated; a 50 year old female with an LDL of 190 but no other risk factors would. The first patient has a CV risk that is about 5 times higher than the latter. This is actually not uncommon – 20-30% of our treatment is misaligned.
- So, it appears that we are pretty much out of luck, right? Well, hopefully not. It helps, when thinking about quality, to step back to the ultimate reasons that we practice medicine. It ’s really quite simple: We want to help patients live longer, and live better. In other words, we want to decrease mortality and increase quality of life. One of the issues with our current evidence base is that we have a really hard time measuring these things in the timespan of a clinical trial. There are very few interventions, for example, that can clearly decrease total mortality in a 3-5 year window. So we focus on intermediate measures – things like heart attacks, blood pressure, or blood glucose levels.
- The problem is that intervening on these intermediate outcomes does not universally lead to better outcomes, and often the link between the intermediate and ultimate outcomes we care about isn ’t all that strong. Think about an obvious example: a patient who has metastatic lung cancer on hospice care, diabetes, and an A1c of 8.5% on maximal oral therapy, but with no thirst, frequent urination, or other diabetes symptoms. How many of you would feel the urge to place this patient on insulin? Hopefully not many. The question is, how can we figure out who really is likely to get benefit? Some cases are obvious – like the one we just talked about – but what about the 70 year old diabetic with heart disease and an A1c of 7.5%? Now things get a bit harder to figure out – and there is almost certainly not going to be a definitive “right” answer that everyone accepts. Instead, we need to try and figure out what the benefits are, and what they mean to the person who has to experience both the treatments and the risks. What we need is to translate intermediate endpoints into either mortality or quality of life measures. Typically this is done by creating probability (or decision) models relating the intermediate endpoints to the long-term outcomes that directly affect patients. Typically this is done by creating probability (or decision) models relating the intermediate endpoints to the long-term outcomes that directly affect patients. This allows us to try and figure out how much impact, for example, preventing a heart attack will have on quality of life and life expectancy – and can be individualized based on the characteristics that affect the likelihood of developing the disease, or dying from something else first. This can then be compared to other diseases using a common metric.
- The chances of moving between these “states” is what we focus on. In this case, the yellow arrow is the chance that a patient has an MI. We have plenty of good ways of estimating this likelihood based on individual level characteristics – blood pressure, smoking, lipids, family history, medication use, and so on. Obviously, the less likely someone is to have the MI, the less benefit they are going to get from treatment. Quantifying this specifically, and translating it into life expectancy and quality of life, helps us better understand how much benefit we get from any given intervention.
- So, one of the things that these models allow us to do is to really quantify how big an effect a treatment has on the things that matter most: making patients live longer and better. Now, I ’ll add one caveat to this. There are other goals, and I’m not going to list them all, but there is one we HAVE to think about in the current environment. It’s pretty far down this list in the eyes of most physicians. But as we figure out how to help patients live longer and better, we need to do this as efficiently as possible. It is gaining increasing importance given that medical expenditures are now such a hot button political issue. Growth in medical spending is projected to create a multi-trillion dollar deficit in Medicare and is being touted as a great threat to our economy. Yet the arguments about this are largely focused on the fact that we do all kinds of pointless stuff - and I’m about to offend some, I’m sure - like MRIs for back pain, PCI for stable angina, and so on. While this is undoubtedly important, there are some patients who do benefit from these things, and many who don’t. It’s the focus on the AVERAGE that’s the problem. So, the question is, can we figure out how to use and apply treatments in a way that accounts for individual benefit and thus improves efficiency?
- The “wave of the future” is the use of genomic markers to identify risk. There are two broad possibilities in terms of using these markers to individualize treatment. The first is to use markers to identify increased or decreased risk of disease. Many of these are being identified in Iceland by DECODE genetics, where they have a homeogeneous population, excellent electronic medical record links, and a willing population participating in genetic research. There are also many groups working on this in academia and industry elsewhere. Markers can also be used to determine response to treatment. A good example is a polyphorphism in TPMT, which reduces methylation and de-activation of mercaptopurines; TPMT deficiencies lead to life threatening myleosuppresion for patients treated with standard thiopurine doses. At this point relatively few such tests are broadly validated and there are little data on how these can be best used to optimize individual or population health. Howeer, we are doing analyses that examine the use of this type of marker in a specific clinical setting, which I ’ll talk about a bit later. In a sense we are trying to understand what is necessary for these markers to be useful on a broad scale.
- So, that ’s the theory and the basis of much of the research that people in our group have been doing. There are a series of studies we’ve done over the years that demonstrate how we can use this concept to improve care – and efficiency. These studies span a variety of methods, diseases, and treatments. That’s one advantage of being a generalist – you get to study whatever you want, and collaborate with folks across the spectrum of disease. It’s certainly one of the main attractions to a place like Michigan.
- A couple of years ago, Rod Hayward, one of my mentors, gave grand rounds and talked about a better way of interpreting clinical trials. This flows from and into the concept of individualizing care by understanding the global risk of a patient. We conducted a series of simulation analyses to examine how a small proportion of high-risk individuals could influence the results of a clinical trial. Here you can see a typical clinical trial. The results that are usually presented are highlighted in gold: the average results of the trial. This trial had a reduction in events from 6% to 5%, which is a 17% RRR and a 1% ARR. In the lower rows, you can see that, if you group the trial subjects into three categories, the absolute benefit dramatically decreases – from 2.6% to 0.42% - as baseline risk decreases. This fits with what I ’ve shown you previously.
- But more important is how we figure out WHO those high, moderate, and low-risk people are. If we try to define it with a single variable – say just using cholesterol levels – we end up with very poor statistical results – many false positives, false negatives, and little real information. Think about this clinically: do you ever really want to classify a patient with just one number? It doesn ’t do even a decent job of describing someone in most cases. Instead, we should be using risk indexes or continuous measures of risk, where possible. Here’s why. Consider a condition – say heart attack – that has 6 known risk factors – such as diabetes, smoking, blood pressure, cholesterol, and so on. Each of these has a relative risk of 2.0 for developing a heart attack.You look at a treatment that reduces risk of MI by 21%, although there are about a 3 in 1000 rate of serious bleeding. If you look at each of these risk factors, one by one, you end up with only 10-20% power, at most, to actually detect a difference. This isn’t surprising, since you kill statistical power when you divide a large group into smaller ones. But, when you look at them with a risk-index composed of adding up each of these factors in any given individual, you get this. The group with no risk factors, making up 17% of the population studied, is actually HARMED by the treatment: there is a total risk increase, despite the overall benefit seen in the trial. Those with 1 risk factor get essentially zero benefit. Those two groups, notice, make up over half of the population here. Despite the average benefit seen in this trial, more than half of patients get zero benefit and a decent percentage of those are actually HURT by the treatment. The remaining folks get reasonable benefit. So, we could effectively reduce our use of this treatment by 50% and actually save more lives, because we don’t cause serious side effects in those with low risk! These are simulations, of course. But we looked at this in a real clinical trial in 2 publications led by our colleague David Kent. We definitively showed, in the GUSTO trial of tPA vs. streptokinase, that tPA, despite being better on average, actually hurt the lowest risk quartile of patients in GUSTO, and did nothing at all for the lower risk half of the population in the trial.
- Here, the underlying risk and benefit of treatment was estimated with the thrombolytic prediction instrument, an instrument that is quite easy to use in real time and uses a combination of clinical and ECG data to estimate mortality risk from MI, with and without treatment with thrombolytics. This particular graph is a comparison of the incremental benefits of tPA vs. streptokinase. You can see here that on the far right, the lowest risk group actually has negative effects from thrombolytics, and up to about 40-50% the benefits of tPA are quite small. On the other hand, in the highest risk group you can see an enormous benefit from tPA. In fact, my suspicion is that the reason that Genentech let us use their data was because at the time, Europe and the VA were not using tPA due to cost; in a separate analysis, we showed that for high-risk patients, tPA was highly cost-effective. This allowed them to expand their market because they knew the horse was out of the barn already in the US.
- Here ’s another example: how often should we screen for diabetic retinopathy? In this case, it turns out you don’t need a full risk score: the stratifying power of just a couple simple variables is more than adequate. In this case, we build a probabilistic model to estimate how often people progress through the stages of diabetic retinopathy, stratified on their age and HbA1c level. This is largely based on the figure I’ve showed you earlier, which clearly related A1c and retinopathy risk. However, there’s another important factor in the mix here: how old is the patient? If they are older, they generally aren’t going to be very likely to live long enough to develop visual loss – unless they happen to already have substantial retinopathy. This slide demonstrates, however, that annual screening is still pretty effective – more than a 50% decrease in the risk of visual loss. So, pretty obvious that we should screen annually, right? Well…
- Let ’s take a closer look. It’s a fallacy to compare annual screening versus nothing when a more nuanced approach is available. If you look at the benefits of screening, stratified by how often you screen, this is what you get. The high-risk patient still gets reasonable benefit. Although I don’t show these numbers, it turns out that the high-risk young patient also spends quite a lot of time blind, because they go blind at a young age. So, you can prevent a fair amount of time blind by screening these people annually, even if the absolute risk reduction is only 3 in 1000. But as your risk decreases, the benefits of annual screening become much less clear. In fact, in the low risk group, annual screening offers absolutely no benefit over screening every 2-3 years. And, from a time spent blind standpoint, you can only prevent a few days in total by screening more aggressively. The bottom line is that it is very expensive, creates long waiting lists, and as Rod Hayward showed in some follow-up work, may actually cause ophthalmologists to delay seeing the patients who really need to be seen.
- We re-examined this using a multivariable risk score: the Framingham equation. Many of you are familiar with this; it is one of the 1 st tools used to help predict an individual ’s risk of CV events and mortality. We looked at 3 strategies in people in the US over the age of 30 without prior CV disease. We looked at a “whole” population strategy, where everyone was treated with a public health intervention (such as diet or exercise); an LDL targeted strategy that treats the people who have the 25% highest LDL levels; and a risk-targeted strategy based on treating those with the top 25% risk of having CV events. The relative risk reductions here are based on reasonable consensus from a literature review.
- CRC screening is rec. for the avg risk population starting at age 50. There are a variety of options, including FOBT, sigmoidoscopy, barium enema, colonoscopy, virtual colonoscopy, and many emerging technologies. However, most experts agree that screening with colonoscopy is the most-effective option if resources are available. One major issue with colorectal cancer is that, outside of the relatively uncommon germline mutations that cause hereditary nonpolyposis colorectal cancer and familial adenomatous polyposis, there are few indicators that help us stratify screening. At this point family history is the primary indicator; although recommendations vary somewhat, people with first degree relatives with early polyps or colorectal cancer are typically advised to undergo screening starting around age 40, or sometime within 5-10 years of the age where there relative was diagnosed. There are few data supporting this recommendation; it ’s based entirely on expert opinion, although the epidemiology suggests these people are at about 2-4 times higher risk relative to the general population. However, because we are left with a reasonably high number of cases of CRC that appear to be sporadic, there is increasing interest and identification of SNPs that are associated with an increased risk of polyp and cancer formation. Most of these have been based on searching the genes (APC and p53) that are associated with HNPCC and FAP. This is being touted by many as the wave of the future - genetics! The promise of the human genome project fulfilled! Last week we were visited by the chief research officer of the VA and he is really pushing this agenda - the possibility of tailoring interventions based on genetic risk.
- A variety of mutations in tumor supressor genes are being evaluated as associated with colorectal polyp and cancer formation. One example is the APC, or adenomatous polpyosis coli gene. A germline mutation in this gene causes FAP, and over the past several years at least one polymorphism, titled APC I1307K polymorphism, has been found to be associated with a 1.5-2 times increased risk of CRC in Ashekenazi Jews. Steve Gruber of genetics, epidemiology, and oncology here at UM was among the leaders in identifying this mutation. Others, such as a mutation in the p53 tumor suppressor gene, which is associated with HNPCC have also been explored; some data suggest that colorectal cancer and polyps can be increased by 2-3 times by certain mutations. Amazingly enough, this has already been marketed: there are commerical tests for APC I1307K. These cost between 2 and 300 dollars. Some experts in this area recommend that persons who are positive have colonoscopy every 2 years starting at age 35 - an incredibly aggressive screening regimen, to be sure. The problem is, we have no idea if it makes any sense. It seems like it should. So, we sought to take an initial look at how this might work, using APC I1307K screening as a “template” - a representative example of the possibility of tailoring screening based on a genetic test.
- I think this last part is a very important part. If we want to use tailoring to improve care and be efficient, we need not only to be able to tell who is at high risk, we need to know who ’s at LOW risk. That’s really the key: avoiding doing stuff in people who don’t need it. So the answer here is that we have a long way to go - at least in this case - before genetic testing is ready for prime time and can improve efficiency at all. The recommendations made by “experts” in this area, which are many times more aggressive than what we modeled here, would be so expensive as to be utterly ridiculous. Even with our somewhat conservative every 5 year screening from age 40, a test like this for the general population, screening 6% of the population would lead to total annual costs approaching $2.5 billion.
- We also show this to be true with low mortality people (though we are much less able to identify these people than the high mortality people), in whom screening through age 80+ is not unreasonable. Interestingly, when we look at high sensitivity FOBT or fecal immunohistochemical testing, we get a slightly different answer, because the test works differently – it is somewhat preventive through polyp detection, but also is more effective at detecting and stage shifting cancer due to the frequency of testing. Our population estimates suggest screening w/ FOBT through age 80 is cost-effective; if someone has done all of their FOBT (i.e., all annual tests starting at age 50), you can stop around age 75; with 2x mortality you should stop around age 70.
- So, what about patient preferences? How do you deal with preferences? Well, I won ’t claim to be expert in this area. We have an entire program, led by Peter Ubel, that does research into how you communicate risks to patients and how patients make decisions in these scenarios. There are a variety of decision-aids and other tools that allow you to communicate these things to patients in a way that they can understand so that they can make informed decisions. But it is cldear that a more individualized assessment of risk will help us to better counsel patients, and will allow patients to make much more informed decisions about whether they really want a treatment.
- The obvious points here are that preferences are likely to vary rather dramatically based on the condition of interest. Chronic disease vs. acute disease, for example, have very different assessments by many. The type of outcomes varies, individual risk varies, and importantly the nature of the interventions vary. But there are at least a couple of rather interesting studies in this area, where individualized assessments of treatments and risks were used to see what patients are likely to get with different treatment choices.
- So, the answer is, at least in the population studied here, that putting people on insulin, despite reducing their risks of diabetes complications, actually leads to a net negative overall effect on QALYs - mediated entirely through quality of life. Put another way, these people - even those who are reasonably young - are better off having poor glucose control and a higher risk of complications than they are taking insulin. Note how dramatically different this is than any current guideline view. However, if they can get to reasonable glucose control with oral agents, then that seems OK - though it ’s of borderline cost-effectiveness. This is a good example of how we can easily be misled by fixed approaches that don’t consider what patients want.
- My colleague Angie Fagerlin, who was here a few weeks ago, has developed and tested a number of approaches to presenting risk information to patients and has found that the most effective method is using a pictograph which can illustrate the benefits of therapy graphically. This represents the efficacy of colorectal cancer screening as an example; the baseline risk without screening is 5.6%, and with screening is 2.5%. With the right decision support system, this figure could be produced automatically in a well-designed EMR system across a vareity of risks. The difficulty is in the time required for counseling/discussing these issues, and we are in the process of pilot testing a variety of methods, including a “medical home” model where a team member counsels patients, self-administered over the internet, and clinician administered. We are also exploring means of testing different ways to assess preferences for various interventions – for medications this is relatively straightforward, but for situations like colon cancer, where many different tests are available, both test characteristics and risk preferences are going to matter.