In medicine, beware of what seems too good to be true

Update 2/17/12:
A reader brought to my attention (thanks!) a very slight inaccuracy in the first table below, which I have corrected. I did the calculations in Excel, which, as you may know, likes to round numbers. 

File this under "misleading." Here is the story:

What's the Latest Development? 

A California start up has developed a breath test that can diagnose lung cancer with a 83 percent accuracy and distinguish between different types of the disease. The procedures which currently exist to test for lung cancer, which is the leading cause of cancer deaths worldwide, result in too many false positives, meaning unnecessary biopsies and radiation imaging. The new devices works by drawing breath "through a series of filters to dry it out and remove bacteria, then [carries it] over an array of sensors."  

What's the Big Idea? 

The company is now testing a version of the machine 1,000 times more accurate than its latest model, which could increase the accuracy of diagnoses to 90 percent, the level likely needed to take the device to market. Because the machine is not specific to a particular group of chemicals, the breath tester could, in principle, test for any disease that has a metabolic breath signature, for example, tuberculosis. "A breath signature could give a snapshot of overall health," says the company's founder, Paul Rhodes. 

Am I just being a luddite by not getting, well, breathless about this? I'll just lay out my argument, and you can be the judge.

There is not doubt that lung cancer is a devastating disease, and we have not done a great job reducing its burden or the associated mortality. However, there are several issues with what is implied above, and some of the assumptions are unclear. First, what does "accuracy" mean? In the world of epidemiology it refers to how well the test identifies true positives and true negatives. If that is in fact what the story means, then 83% may not be bad; we'll regroup on that point at the end late in this post. This brings me to my second point: what is the gold standard that the test is being measured against? In other words, what is it that has the 100% accuracy in lung cancer detection? Is it a chest X-ray, a CT scan, a biopsy, what?

The SEER database, the most rigorous source of cancer statistics in the US, classifies tissue diagnosis as the highest evidence of cancer. However, in some cases a clinical diagnosis is acceptable. The inference of cancer when no tissue is examined is possible when weighing patient risk factors and the behavior of the tumor. So, you see where I am going here? The gold standard is tissue or tumor behavior in a specific patient. Is that what this technology is being measured against? We need to know. And here is another consideration. What if the tissue provides a cancer diagnosis, but the cancer is not likely to become a problem, like in the prostate cancer story, for example?

But all of these issues are but a prelude to what is the real problem with a technology like the one described: the predictive value of a positive test. The story even alludes to this, pointing the finger at other current-day technologies and their rates of false positivity, and away from itself. Yet, in fact, this is the crux of the matter for all diagnostics. Let me show you what I mean.

The incidence of lung cancer in the US is on the order of 60 cases per 100,000 population. Now, let us give this test a huge break and say that it yields (consistently) 99% sensitivity (identifies patients with cancer when cancer is really present) and 99% specificity (identifies patients without cancer when they really do not have cancer). What will this look like numerically given the incidence above if we test 100,000 people?

	Cancer present	Cancer absent	Total
Test +	59	999	1,0589
Test -	1	98,941	98,9421
Total	60	99,940	100,000

If we add up all the "wrong" test results, the false negative (n=1) and the false positives (n=999), we arrive at a 1% "inaccuracy" rate, or 99% accuracy. But what is hiding behind this 99% accuracy is the fact that of all those people with a positive test only a handful, a paltry 6%, actually have cancer. And what does this mean to the other 94%? Additional testing, a lot of it invasive. And what does this testing mean for the healthcare system? You connect the dots.

Let's explore a slightly different scenario. Let us assume that there is a population of patients whose risk for developing lung cancer is 10 times higher than the population average. Let us say that their incidence is 600 cases per 100,000 population. Let us perform the same calculation assigning this same bionic accuracy to the test:

	Cancer present	Cancer absent	Total
Test +	594	994	1,588
Test -	6	98,406	98,412
Total	600	99,400	100,000

The accuracy remains at 99%, but the value of the positive test rises to 37%. Still, 63% of all people testing positive for cancer will go on to unnecessary testing. And imagine the numbers when we try to screen millions of people, rather than just 100,000.

Let us do just one final calculation. Let us reflect the data back to the test in question, where the article claims that the accuracy of the next version of the technology will be 90%. Assuming a high risk population (600 cases per 100,000 population), what does a positive result mean?

	Cancer present	Cancer absent	Total
Test +	540	9,940	10,480
Test -	60	89,460	89,520
Total	600	99,400	100,000

From this table, the accuracy is indeed 90%, concealing the very low value of a positive test of 5%! This means that of the people testing positive for lung cancer with this technology, 95% will be false positives! What is most startling is that to arrive at the same mediocre 37% value for a positive test that we saw above in this population, we would need a population where cancer incidence is a whopping 6,000 per 100,000, or 6%!

I do not want to belabor this issue any further. Screening for disease that is not yet a clinical problem is fraught with many problems, and manufacturers need to be aware of these logic pitfalls. What I have shown you here is that even when the "accuracy" of a test is exquisitely (almost impossibly) high, it is the pre-test probability of, or the patient's risk for the disease that is the overwhelming driver of false positives. Therefore, I give you this conclusion: beware of tests that sound too good to be true -- most of the time they are.

h/t to @gingerly_onward for the story link  

Medicine: The art of applied science

I read this NPR article this morning and had to do a post in response. The gist is that the military is turning to what we might call the less conventional (for us in the West) medical modalities to deal with the injuries sustained by the current crop of vets. Instead of getting them hooked on pain meds for life (we saw plenty of this in the VAs in the '80s and '90s among Vietnam vets), they are turning to stuff like massage and acupuncture. And, predictably, it is stirring up controversy.

The story that is told is of a Sgt. Rick Remalia who fractured his back and pelvis in Afghanistan:

Remalia broke his back, hip and pelvis during a rollover caused by a pair of rocket-propelled grenades in Afghanistan. He still walks with a cane and suffers from mild traumatic brain injury. Pain is an everyday occurrence, which is where the needles come in.

And lately he has been receiving acupuncture treatments, with this result:

"I've had a lot of treatment, and this is the first treatment that I've had where I've been like, OK, wow, I've actually seen a really big difference," he says.

And incidentally, her gets these treatments from a military physician, who, herself a skeptic, admits to perceiving a personal benefit from her own exposure to it:

"I actually had a demonstration of acupuncture on me, and I'm not a spring chicken," she says, "and it didn't make me 16 again, but it certainly did make me feel better than I had, so I figured, hey ... let's give it a shot with our soldiers here."

So, all good so far, right? Well, Harriet Hall is quoted in the same article, and to her this falls right into what she likes to call "quack-ademic" medicine. She says,

"The military has led the way on trauma care and things like that, but the idea that putting needles in somebody's ear is going to substitute for things like morphine is just ridiculous," Hall says.

Now, as you know, I have had some debates with the SBM crowd in the past, and as it turns out, we agree on the science more than we disagree. However, I am thinking that this argument is not about science, but about politics.

I am well aware that a group of anecdotes does not amount to science. And I am also well aware that what we are hearing here are anecdotes. But here is the thing: when your kid tells you that she likes chocolate ice cream better than vanilla, do you ask for evidence that chocolate is better than vanilla at the population level? No, that's absurd! OK, you say, but this is a strawman: nobody is going for a claim of superiority of chocolate ice cream over vanilla. That is true, but is this about the science or about being able to make a claim? If my kid likes chocolate, why not let her have that when ice cream is on the menu? If acupuncture seems to provide some relief to Sgt. Remalia, why not let him have that relief? After all, whose opinion about what works counts in this individual example, ours or the patient's? And if the ethics of using placebo are the concern, there is nothing wrong with letting him know that in large clinical trials the evidence is equivocal, which means that it may work for some and not for others. In fact, this might be a good disclaimer to make before commencing any treatment, one with the right to claims and one without.

Another argument is that there is no way that insurance (or our taxes) should pay for this unproven treatment. Still about science? Do any of you want to stand up and tell Sgt. Remalia, who fought for our freedom, that we will not pay for the only thing that seems to help him, that is pretty cheap and safe and that has very few, if any, long-term adverse effects, in stark contrast to pain killers? Yes, I understand that this is not science, but is there no room for humanism in the practice of medicine? After all we have throaty debates as to whether or not it is ethical to deny a $100,000 payment for a treatment that, on average, prolongs life by 2 weeks. Surely, denying Sgt. Remalia access to this relief would diminish our humanity. And what about the costs of treating addiction to pain killers?

So, here are my points:
1. I completely agree that that acupuncture "works" for Sgt. Remalia, does not mean that "acupuncture works" in the scientific sense. It may or may not work; furthermore, our current models of the universe do not allow us to have an adequate mechanistic explanation. But that is not the point -- it works for this young man whose life will never be the same because he signed up to defend his country. To this extent his "claim" has all kinds of internal validity.
2. Making claims is subject to legal and regulatory frameworks that have very little to do with science. I have done much blogging on clinical vs. statistical considerations in clinical research that feeds regulatory approvals and hence claims, and I remain of the opinion that a lot of the acceptable claims are specious. I know, I know, this is a "tu quoque" argument, but if we are talking about the goose and the gander, well...
3. Whether or not a treatment should be paid for is more prone to political than evidence-based decisions. Given that most medicines work in a minority of patients, and none comes without adverse events, the extent of which remains largely unknown because of our negligence to build real regulatory systems to quantify them, we are spending a lot of dollars on stuff that does not work at the individual level.

Medicine has to be part science and part art; in fact the art is in how and when to apply the science. That latter portion must be about humanism.

   

Big changes in the world of VAP?

As you may or may not be aware, the four main professional societies in the US that include large critical care constituencies, AACN, Chest, SCCM and ATS, have created something called a Critical Care Societies Collaborative (CCSC). Its purpose is essentially to give the critical care community a voice in shaping public policy. And, as you can imagine, one of the current-day issues they are tackling is performance measures.

Now, on this web site we have spent a lot of virtual ink talking about quality metrics, particularly where ventilator-associated pneumonia, or VAP, is concerned. Well, I am happy to report that finally, a strong voice in Critical Care medicine is in agreement with what we have been saying: the VAP bundle needs to go! In fact, here is the sum of the recommendations made to the National Quality Forum in a letter dated February 9, 2012, from the CCSC leaders about VAP (emphasis mine):

The Task Force felt that the VAP “care bundle” minimizes the importance of each individual component measure and neglects the fact that many elements of the existing VAP bundle are known to have important effects outside of VAP reduction, including improved patient survival. The task force also notes that one of the components of the VAP care bundle, stress ulcer prophylaxis, may actually increase the risk of VAP.51Therefore, the Task Force would like to make the following recommendations regarding measure gaps related to VAP:(1) Dissolve the VAP care bundle and instead develop a new group of quality measures related to general evidence‐based practices for patients requiring mechanical ventilation (described above.) These potential measure gaps would include care processes known to reduce morbidity and mortality in patients who are ventilated.
(2) Develop measures using the VAP‐specific measure gaps supported by recent guidelines.52,53 These may include measures for the following evidenced‐based practices:
• Orotracheal rather than nasotracheal intubation to prevent VAP54;
• Subglottic secretion drainage to prevent VAP55;
• Elevating the head of bed to 45 degrees to prevent VAP56;
• Oral antiseptic administration to prevent VAP57;
• When empiric antibiotics are used to treat VAP, initial treatment based on qualitative endotracheal aspirates rather than quantitative bronchoscopic aspirates58; and
• No more than an 8‐day course of antibiotics as treatment for uncomplicated VAP.59
All of these VAP prevention strategies are supported by randomized‐controlled trials. However, not all have favorable cost‐benefit profiles, and all have significant barriers, which may make widespread adoption unfeasible. Although we list them all here, we note that all may not be good quality measures.

So, here it is -- the recommendation. But will it be followed? When I was at SCCM, I heard a presentation that talked about some new metrics being developed by the CCSC in collaboration with the CDC, which will likely replace VAP as the focus of mechanical ventilation complications. I am in the process of learning more about these developments even as we speak, and will update my readers on what I learn. Suffice it to say, change is coming to the world of VAP. And it's about time. 

Teach one thing, or the rule of thirds

When I was a medical student, I did a lot of rotations at the Boston VA in JP. I loved my patients there -- they were patient and kind and stoic. One of the best rotations I did was Hematology, where Lou Fiore was my preceptor. Lou was not only an excellent teacher, but also a terrific doctor and a good human being all around. He used to start our days together by saying, "I'm gonna teach you one thing today." And teach us he did, at least one thing per day. Now I teach. And on occasion I have used the Lou Fiore "I'm gonna teach you one thing today" promise. Well, today is one of those days: I'm gonna teach you one thing.

And here is that thing. I am sure I am not the first one to notice this, but I still think of it as the "Zilberberg rule of thirds." The gist of it is that, for clinical research purposes, one can think of patient populations crudely in thirds: there is one third who are too sick to benefit from any of our interventions, there is one third who are too healthy, so that no matter how we try to tweak, their outcomes will not change, and the middle third, which comprises the "sweet spot" for intervention. So it is a fool's errand to pursue proof of concept studies in either of the bracketing thirds, since it is only the middle third that is likely to show a signal.

Pharmaceutical manufacturers do not always appreciate this trichotomy. Look at Vioxx, for example: when used in patients who were essentially healthy, an unacceptable safety signal arose that drove the drug off the market. Same for SSRIs, where the ill-conceived enthusiasm for treating marginal depression cases seems to be debunking the entire serotonin hypothesis. The flip side is sepsis research: septic shock patients are so far gone that it is difficult for any single therapy to alter their outcomes. Just look at the Xigris story, as well as myriad other therapies that tried and failed. This is the rule of thirds at its most pronounced.

In HEOR the rule of thirds holds as well. To prove cost effectiveness the following questions need to be asked:
1. Is the disease in question prevalent?
2. Is the economic impact of the disease known and substantial?
3. Does the diagnostic/therapy in question alter the course of the disease in such a way as to be significant?
If the answer to any of the questions above is "no," you really need to think carefully about the value proposition.

Some of you will bring up the inter-individual differences, the heterogeneous treatment effect, etc. And yes, these are supremely important. However, though the framework I propose here is simplistic, we have to start somewhere. To be sure, there is a more nuanced approach to this beast, but generally, one will not go wrong by asking these questions before committing huge resources to a project, particularly if the answer to question 2 or 3 is a resounding "no." So, even in health economics it behooves one to know the Zilberberg rule of thirds: choose the right population where the diagnostic/therapeutic advance and its costs can be justified by a substantial gain in the outcomes.

And that is your one thing for today.    

Marie Curie, Geiger counters and mass hysteria: more in common than meets the eye

What do Marie Curie, a Geiger counter and mass hysteria have in common? Well, to answer this question we need to go Sir Arthur Eddington, who was a British astrophysicist and philosopher of science at the turn of the 20th century. He came up with what is frequently referred to as the Eddington parable, which has nothing to do with the stars specifically and everything to do with how we make scientific progress. Here it is for your reading enjoyment, as told in this editorial (available by subscriptionby Diamond and Kaul, two highly respected clinician-researchers:

Let us suppose that an ichthyologist is exploring the life of the ocean. He casts a net into the water and brings up a fishy assortment. Surveying his catch, he [concludes that no] sea-creature is less than two inches long. An onlooker may object that the generalization is wrong. "There are plenty of sea-creatures under two inches long, only your net is not adapted to catch them." The ichthyologist dismisses this objection contemptuously: "Anything uncatchable by my net is ipso facto outside the scope of ichthyological knowledge, and is not part of the kingdom of fishes which has been defined as the theme of ichthyological knowledge. In short, what my net can't catch isn't fish”.

Suppose that a more tactful onlooker makes a rather different suggestion: "I realize that you are right in refusing our friend's hypothesis of uncatchable fish, which cannot be verified by any tests you and I would consider valid. By keeping to your own method of study, you have reached a generalization of the highest importance—to fishmongers, who would not be interested in generalizations about uncatchable fish. Since these generalizations are so important, I would like to help you. You arrived at your generalization in the traditional way by examining the fish. May I point out that you could have arrived more easily at the same generalization by examining the net and the method of using it?"

So,you see my point? Tools determine knowledge. Period.

  

Physician Payment Sunshine Act: More marginal thinking

It never ceases to amaze me how we gravitate to the margins in our thinking: margins seem to have a centrifugal force that is nearly impossible to overcome in today's political discourse. Yet the truth almost always lies at the center, the place that does not generate Op-Eds or produce votes.

I have said this before, and I will say it again: industry-physician relationship is not all bad or all good, there is no one within this relationship that is all bad or all evil, and it does not always benefit or always harm patients! The truth, of course, is somewhere in the middle. Contrary to Stossel's thesis, there is plenty to worry about with respect to corruption promoted by the big money exchanging hands between Pharma and doctors. On the other hand, just because there are instances of corruption and its consequences, not all interaction, financial or otherwise, is counterproductive. I am the first to admit that the much-touted innovation in medicine is rare, and we have largely given up its pursuit in favor of predictable markets and returns. Yet without a robust and transparent collaboration between industry and practitioners there is not only little hope of innovation, but any innovation that may stand a chance is likely to be irrelevant.

Yes, I agree with Stossel that the new reporting regulation is overly punitive and will inevitably result in undue administrative burden. But it would be disingenuous of me to disagree with the fundamental idea that there needs to be at least some degree of transparency in the financial dealings between industry and clinicians, if only to avoid the appearance by the docs of serving two masters.

As in everything in life, the devil is in the details. And it is these details that get buried by the gravitational pull of peripheral thinking and discourse. The solution? How about we stop paying attention to these marginal fallacies and start putting our heads together for real to solve these significant problems? How about we start a rational discussion about what is best for the people and not for the corporations or the economy or reputations? The discussion has been subverted by extremism. It is time to give in to the centripetal pull of reason.      

3 ways to sink a new drug

I don't just rant about methods and evidence -- in my work life I also rant about health economics and outcomes! This is why I was so interested in this post by the health economist Ulf Staginnus called
"New Models for Market Access." I want to give a hat tip to Healtheconomics.com for pointing me here.

The thesis of the article is that we need to refocus our discussion from market access to true innovation in the biopharma sector. There are some priceless quotes here, like this one, for example:

It is amusing, at least to me, to see the continued flood of articles, consultant presentations, blogs, congress announcements, workshops, summits, reorganizations, speeches, etc. all over the place, basically suggesting how the industry just needs to throw a few more people with fancy titles here and there, coupled with slight organizational changes, onto the problem and involve stakeholders and—guess what?!—actually talk to patients and perhaps even payers and all of a sudden, like Alice in Wonderland, everything will be good, after all.

The uncomfortable truth is, it won't be. All this “noise” is only good for one thing, paying the bills of the consultants, which is fine, too, as I have been one myself so I can understand. But it will not address the problem the research-based pharmaceutical industry and its employees are facing. Without a substantial increase in R&D productivity, the pharmaceutical industry's survival (let alone its continued growth prospects), at least in its current form, is in great jeopardy.

Don't you love it? It is hard to disagree. He also calls for more of a focus on the long-term returns than the short-term (duh!), as well as more internal honesty, or having the courage to stand up to the pathologic internal enthusiasm about a late-stage product that will obviously go nowhere. And all of this is on target.

He has this to say about health economics and outcomes and such:

Of course, you need experience in areas such as HE, outcomes research, pricing, economics, policy, advocacy, etc. and all needs to work in sync and early on and with the payer in mind and, yes, most people have understood that by now. So the problem is essentially not in the capabilities, although some are more advanced than others, but rather in the company cultures.

And this, I think, is where I have to disagree with him. In my experience there are glaring deficits in the approach to HEOR within biopharma, though of course there are exceptions to the rule. It starts with the fact that disease burden, and especially its costs, are initially assessed through less than, shall we say, rigorous methods. I have seen this critical information get pieced together through market "research", where 5-10 "thought leaders" are asked for their opinions, and the quantification is based on this tiny non-representative sample of nothing more than guesses. This is a shame because the data usually exist which can give a much more bona fide estimate of the extent of the problem.

The second problem is that articulating the value proposition of a nascent technology is usually an afterthought. In fact it is self-evident that drug pricing must be fed using the information on the burden of disease, and the impact the new technology can make in mitigating such burden. Unfortunately, time and time again I see companies backing into a price simply in reaction to what their Boards perceive the returns should be. And frequently this is based on the overly optimistic market projections flowing from, you guessed it, market "research."

So, the direct result of all this short-sightedness and business as usual is that even innovative useful products are driven into oblivion because there is no realistic look at what the technology is worth or where best to use it. And fixing the problem after the drug or device is on the market is a much bigger challenge for several reasons. First, the acquisition costs of new technologies are bound to be higher than of those already in use. This puts them at a disadvantage in that they get niched into populations that have much greater burdens of illness and therefore less of a chance of doing well. In other words, they are used as a last ditch therapy, which very rarely ends well. Ironically, these are usually not the populations who were studied in the pre-approval studies, and thus the use turns out to be off-label. But here is the real problem: When these technologies are in the "kitchen sink" category, they will almost always end up looking worse in terms of the outcomes than their older counterparts. And to the untrained eye, or an eye who does not have the time to discern the truth, particularly in the setting of perceived high expenses on the new product, this rings the death toll for the drug. But the reality, of course, is that the abysmal outcomes are the result of confounding by indication, where the drug was inappropriately given to those patients who were very unlikely to benefit from in the first place. But you see how this early lack of attention to the articulation of appropriate populations and health economic data can snowball into failure of a promising therapy.

So, if you want your drug to fail, do the opposite of what I recommend below. In other words DO NOT
1. Develop your market understanding
Do it not from the opinions of a handful of "experts" -- experts will rarely tell you the truth. Instead, do epidemiology studies to understand your population and its subpopulations so as to get the most reasonable idea of the disease.
2. Start thinking about the value proposition early 
At the end of a successful Phase 2 program is a good time to do this. The surprise to most companies is how little HEOR studies cost in comparison with their clinical trials program. Yet, as you can see from above, this drop in the ocean can make or break a product.
3. Focus on transparent pricing methods
When pricing the technology, be very very sure that you have all of the ducks in a row, meaning:
                    a. do understand your market 
                    b. do understand the burden and costs of the disease
                    c. do understand how your product impacts these costs
                    d. do price the product to reflect this balance
It is truly embarrassing to have to admit that your price reflects nothing more than the greed of your investors. Trust me, you will not score points with your customers.

Staginnus makes one other important point which I generally agree with:

And let's face it, if you need a major workshop and intensive external “coaching” to help define the value of your product … well, there actually is little to none. If it was really good, it would have been obvious from the start. So maybe we ought to stop beating around the bush and move on if there is nothing to be done anymore. 

There is a nuance here, however, as in most things. Given what I have said above, products are more likely to gut than sell themselves. So, while I agree that you do not need a throng of consultants in suits and hair gel to pollute your offices, you do need to understand how to articulate this value, even when it appears obvious.

          

When end of life is not

Twenty years ago, I helped save a man's life.

So begins this New York Times essay by Peter Bach, MD, where he talks about the inadequacy of resource use at the end of life as a policy metric. Now, I am not very fond of policy metrics, as most of you know. So, imagine my surprise when I found myself disagreeing vehemently with Peter's argument. Well, to be fair, I did not disagree with him completely. I only disagreed with the thesis that he constructed, skillfully yet transparently fallaciously (wow, a double adverb, I am going to literary hell!) Here is what got me.

He describes a case of a middle-aged man who was experiencing a disorganized heart rhythm, which ultimately resulted in dead bowel and sepsis. The man became critically ill, the story continues, but three weeks later he went home alive and well. This, Dr. Bach says, is why end of life resource utilization is a bad metric: if this guy, who had a high risk of dying, had in fact died in the hospital, the resources spent on his hospital care would have been considered wasted by the measurement. And I could not agree more that lumping all terminal resource use under one umbrella of wasteful spending is idiotic. Unfortunately, knowingly or not, Peter presented a faulty argument.

The case he used as an example is not the case. Indeed it is a straw man constructed for the cynical purpose of easy knock-down. When we talk about futile care, we are not referring to this middle-aged (presumably) relatively healthy guy, no. We are talking about that 95-year-old nursing home patient with advanced dementia being treated in an ICU for urosepsis, or coming into the hospital for a G-tube placement because of no longer being able to eat or drink. We are talking about patients with advanced heart failure and metastatic cancer, whose chances of surviving for the subsequent three months are less than 25%. And yes, we are also talking about some middle-aged guy with gut ischemia, sepsis and worsening multi-organ failure whose chances of surviving to hospital discharge are close to nil; but in his case, instead of being clear from the beginning, the situation evolves.

So, yes, the costs of end of life care, and specifically hospitalizations, are staggering. But more importantly, among patients with terminal illnesses like metastatic cancer, advanced heart failure and dementia, hospitalizations and heroic interventions at the end of life cause unnecessary pain and suffering, and without much, if any, benefit in return. Their families and caregivers suffer as well, and many studies suggest that these caregivers are not interested in prolonging suffering, provided they are aware of the prognosis. Unfortunately, just as many studies suggest that communication between doctors and patients' families about these difficult issues is less than stellar.

So, let me play the devil's advocate and pretend that I support end of life resource utilization as a quality metric. If I did, I certainly would not be interested in depriving Dr. Bach's middle-aged acutely ill patient of the chance to survive. In fact, my aim would be to make sure that we align resource use with where it can do most good, and turn away from interventions that are apt merely to prolong dying.