New study: “harm to patients resulting from medical care was common”

The healthcare webs are buzzing with the results of a new study by Landrigan and cohort from the November 25 issue of the New England Journal of Medicine (read it here). The medical establishment is understandably never eager to see studies in prominent journals highlighting the dark side of medicine, where mistakes result in increased morbidity and mortality, ethical controversies (when to admit mistakes?), legal hassles, and resulting increased overhead at a level that would give Croesus pause. Of course problems are opportunities, and an army of specialists and managers have done a brisk business in recent years working on improving the situation. Doing well by doing good, perhaps? Researchers are getting interested as well: the relatively new field of medical cognitive science is making efforts to help hospitals cut down on errors. But when a study focuses on the lack of improvement in adverse event statistics after years of targeted investments, certain big-shots may be getting nervous. The Landrigan paper implicitly calls into question what return on investment patients and taxpayers are getting relative to the tremendous sums spent on quality assurance, risk management, error control, continuous improvement of safety, and so forth. The authors are careful to note that they sampled from a specific geographical region, and that in any event efforts to reduce adverse events from care should continue. Nonetheless:

In conclusion, harm to patients resulting from medical care was common in North Carolina, and the rate of harm did not appear to decrease significantly during a 6-year period ending in December 2007, despite substantial national attention and allocation of resources to improve the safety of care. Since North Carolina has been a leader in efforts to improve safety, a lack of improvement in this state suggests that further improvement is also needed at the national level.

And this:

In a statewide study of 10 North Carolina hospitals, we found that harm resulting from medical care was common, with little evidence that the rate of harm had decreased substantially over a 6-year period ending in December 2007. Although there was a modest reduction in the rate of preventable harms on the basis of external reviews, the reduction did not reach statistical significance in adjusted analyses. This apparent reduction was not substantiated by the internal reviews, which by all measures were of higher quality than the external reviews (i.e., higher within-team reliability at both primary and secondary review stages and higher agreement with experienced reviewers).

Our findings validate concern raised by patient-safety experts in the United States and Europe that harm resulting from medical care remains very common. Though disappointing, the absence of apparent improvement is not entirely surprising. Despite substantial resource allocation and efforts to draw attention to the patient-safety epidemic on the part of government agencies, health care regulators, and private organizations, the penetration of evidence-based safety practices has been quite modest. For example, only 1.5% of hospitals in the United States have implemented a comprehensive system of electronic medical records, and only 9.1% have even basic electronic record keeping in place; only 17% have computerized provider order entry. Physicians-in-training and nurses alike routinely work hours in excess of those proven to be safe. Compliance with even simple interventions such as hand washing is poor in many centers.

 

 

Venter on what medical benefits have come from the Human Genome Project: “Close to zero to put it precisely”

It looks like there is a full-blown revisionist wave in the making on the medical value of the Human Genome Project and genomics, as you can read in Craig Venter’s recent SPIEGEL interview. Venter has become a very controversial public figure and is nobody’s candidate for an unbiased source. However, given his central role as a bioinformatics/genomics entrepeneur and in developing  gene sequencing, it is certainly worth considering his characterizations of genomics vis-a-vis medicine:

SPIEGEL: And what about the fears about the abuse of gene data through insurers or employers, for example? Do you see that as sheer hysteria?

Venter: Abuse is not a question of whether the data is available. It is an issue of laws. You can’t do anything to change the availability of genetic data. Look at this bottle that you have touched — that’s all I need to obtain your entire genetic information.

SPIEGEL: How much would you be able to learn about us by doing so?

Venter: If anything, we don’t really know how to read the genome and it can’t tell us very much right now. So what’s the ethical debate about?

SPIEGEL: The decoding of your personal genome has so far revealed little more than the fact that your ear wax tends to be moist.

Venter: That’s what you say. And what else have I learned from my genome? Very little. We couldn’t even be certain from my genome what my eye color was. Isn’t that sad? Everyone was looking for miracle ‘yes/no’ answers in the genome. “Yes, you’ll have cancer.” Or “No, you won’t have cancer.” But that’s just not the way it is.

SPIEGEL: So the Human Genome Project has had very little medical benefits so far?

Venter: Close to zero to put it precisely.

SPIEGEL: Did it at least provide us with some new knowledge?

Venter: It certainly has. Eleven years ago, we didn’t even know how many genes humans have. Many estimated that number at 100,000, and some went as high as 300,000. We made a lot of enemies when we claimed that there appeared to be considerably fewer — probably closer to the neighborhood of 40,000! And then we found out that there are only half as many. I was just in Stockholm for the 200th anniversary of the Karolinska Institute. The first presentation was about the many achievements the decoding of the genome has brought. Then I spoke and said that this century will be remembered for how little, and not how much, happened in this field.

SPIEGEL: Why is it taking so long for the results of genome research to be applied in medicine?

Venter: Because we have, in truth, learned nothing from the genome other than probabilities. How does a 1 or 3 percent increased risk for something translate into the clinic? It is useless information.

SPIEGEL: There are hundreds of hereditary diseases that can be traced to defects in individual genes. You can determine a lot more than just probabilities through them. But that still hasn’t led to a flood of new treatments.

Venter: There were false expectations. Take Ataxia telangiectasia, for example, a horrible disease. The nervous system degenerates, and people who have it often die in their early teens. The cause is a defect in a single gene, but it is a developmental gene. If your body is built in the wrong way, then you can’t just take a magic pill to rebuild it. If your brain is wired wrong, then it is wired wrong.

SPIEGEL: Who is to blame for those false expectations?

Venter: We were simply always looking at single genes because they were the only genes we had. When people lose their keys at night, they look under the lamp post. Why? Because that’s where you can still see something.

SPIEGEL: But the keys are really located in the dark?

Venter: Exactly. Why did people think there were so many human genes? It’s because they thought there was going to be one gene for each human trait. And if you want to cure greed, you change the greed gene, right? Or the envy gene, which is probably far more dangerous. But it turns out that we’re pretty complex. If you want to find out why someone gets Alzheimer’s or cancer, then it is not enough to look at one gene. To do so, we have to have the whole picture. It’s like saying you want to explore Valencia and the only thing you can see is this table. You see a little rust, but that tells you nothing about Valencia other than that the air is maybe salty. That’s where we are with the genome. We know nothing.

SPIEGEL: Do you think there will be a time when you can extract all this information to yield real medical results?

Venter: For that to happen we need a lot more information: Information about your body’s chemistry, your physiology, your complete medical history, your brain and your entire life. We would need to do that a million times on different people and correlate that data with their genetic information.

SPIEGEL: Will that lead in the end to the kind of personalized medicine that genetic researchers have always touted? Each person would get his or her own personal treatment that is tailored precisely to that person’s genetic make-up?

Venter: That was another one of these silly naïve notions that was out there. It’s not, ‘Oh, we know your genome, we’re going to make this drug for you.’ That will never happen. It is more important that you use the information in the genome about your personal risks and reduce them through intelligent behavior.

SPIEGEL: You have complained about how naïve genome researchers were in the beginning. Will future generations eventually make fun of us in the same way for how naïve we still are today?

Venter: Only time will tell. Nevertheless, we now have what is going to be one of the most important tools for interpreting the human genome: the first synthetic cell. It will enable us to ask questions that would have been inaccessible before.

“Genomics is a way to do science, not medicine”

Over the last ten years or so many of us have been following developments in pharmacogenomics and bioinformatics, wondering if the revolution was truly upon us. The completion of the Human Genome Project, the advances in gene sequencing chips, computational chemistry algorithms, and ever more sophisticated models of signaling pathways in cells, not to mention the impressive capital available to the biotech industry, all made it seem as if a new class of drugs based on genomic variations was on the way. Optimistic thinkers heralded the coming era of personal drugs tailored to individual genomic differences. Certainly the textbook from my 2009 Epidemiology class made it seem as if gene sequencing would play a progressively larger role in modeling variance in human disease outcomes, data that could be fed back into the pharmaceutical development process. A friend getting his PhD in neuroscience who had no wet-lab experience prior told me how easy it is to run the new automated PCR systems to amplify particular sequences of DNA. These and other developments had me convinced that advances in wet lab science, combined with computational modeling of how drugs interact with receptors and other cellular targets to change gene expression and signaling pathways, would quickly lead to a major new category of medical advances (say, by 2015 or 2020). It seemed the revolution truly was nigh…

As I wrote a few months back, the difficulties that personal genomics companies were having in staying solvent served to dampen optimism somewhat. But more significant than the perilous balance sheet of formerly hyped biotech firms is the accumulating change in the conventional wisdom, suggesting that gene sequencing may not lead to many valuable therapies anytime soon. Certainly the jury is still out on this. But mounting evidence suggests the low-hanging fruit has already been plucked in pharmaceutical design, with the easier molecular targets in the common diseases already identified, leaving the drug companies nervous about pouring billions more into r&d. Most of what I am reading suggests we should still expect great things from applying gene sequencing to pharmacology, but not a new class of breakthrough drugs, much less personalized medicine anytime soon (before, say, 2020 or 2030).

Last spring I went to a well-attended meeting of the Austin Forum called “Bio-tech: the Next Big Thing”, and it was like the Internet bonanza of 1999 all over again. Various scientists and boosters extolled the coming great wave of healthcare benefits resulting from genomic medicine and sundry bioengineering advances. I was teaching a class dealing with this material and thought some dissenting perspectives needed to be aired. At question time, I took the mike and pointed out how vanishingly few actual new drugs pharmacogenomics and bioinformatics etc. have delivered after many billions of private and public dollars spent, and thus should we not be cautious about big investments in risky projects? To his credit, UT Provost and pharmacologist Steven Leslie agreed with me and added a much-needed tone of sobriety to the otherwise exuberant mood (if anyone has a link to his answer, please fwd. as he is a man worth listening to).

The last few months have seen a certain backlash against the genomic medicine hype. Here is a nice summary from the eminently readable Nicholas Wade in the June 1 New York Times: “A Decade Later, Human Genome Project Yields Few New Cures”:

The pharmaceutical industry has spent billions of dollars to reap genomic secrets and is starting to bring several genome-guided drugs to market. While drug companies continue to pour huge amounts of money into genome research, it has become clear that the genetics of most diseases are more complex than anticipated and that it will take many more years before new treatments may be able to transform medicine.

“Genomics is a way to do science, not medicine,” said Harold Varmus, president of the Memorial Sloan-Kettering Cancer Center in New York, who in July will become the director of the National Cancer Institute.

The last decade has brought a flood of discoveries of disease-causing mutations in the human genome. But with most diseases, the findings have explained only a small part of the risk of getting the disease. And many of the genetic variants linked to diseases, some scientists have begun to fear, could be statistical illusions.

The Human Genome Project was started in 1989 with the goal of sequencing, or identifying, all three billion chemical units in the human genetic instruction set, finding the genetic roots of disease and then developing treatments. With the sequence in hand, the next step was to identify the genetic variants that increase the risk for common diseases like cancer and diabetes.

It was far too expensive at that time to think of sequencing patients’ whole genomes. So the National Institutes of Health embraced the idea for a clever shortcut, that of looking just at sites on the genome where many people have a variant DNA unit. But that shortcut appears to have been less than successful.

The theory behind the shortcut was that since the major diseases are common, so too would be the genetic variants that caused them. Natural selection keeps the human genome free of variants that damage health before children are grown, the theory held, but fails against variants that strike later in life, allowing them to become quite common. In 2002 the National Institutes of Health started a $138 million project called the HapMap to catalog the common variants in European, East Asian and African genomes.

With the catalog in hand, the second stage was to see if any of the variants were more common in the patients with a given disease than in healthy people. These studies required large numbers of patients and cost several million dollars apiece. Nearly 400 of them had been completed by 2009. The upshot is that hundreds of common genetic variants have now been statistically linked with various diseases.

But with most diseases, the common variants have turned out to explain just a fraction of the genetic risk. It now seems more likely that each common disease is mostly caused by large numbers of rare variants, ones too rare to have been cataloged by the HapMap.

Here are some excerpts from the December 2009 edition of the Economist: “Looming crisis in Human Genetics” by evolutionary psychologist Geoffrey Miller:

Human geneticists have reached a private crisis of conscience, and it will become public knowledge in 2010…

About five years ago, genetics researchers became excited about new methods for “genome-wide association studies” (GWAS). We already knew from twin, family and adoption studies that all human traits are heritable: genetic differences explain much of the variation between individuals. We knew the genes were there; we just had to find them….

In 2010, GWAS fever will reach its peak. Dozens of papers will report specific genes associated with almost every imaginable trait—intelligence, personality, religiosity, sexuality, longevity, economic risk-taking, consumer preferences, leisure interests and political attitudes. The data are already collected, with DNA samples from large populations already measured for these traits. It’s just a matter of doing the statistics and writing up the papers for Nature Genetics. …

GWAS researchers will, in public, continue trumpeting their successes to science journalists and Science magazine. They will reassure Big Pharma and the grant agencies that GWAS will identify the genes that explain most of the variation in heart disease, cancer, obesity, depression, schizophrenia, Alzheimer’s and ageing itself. …

In private, though, the more thoughtful GWAS researchers are troubled. They hold small, discreet conferences on the “missing heritability” problem: if all these human traits are heritable, why are GWAS studies failing so often? …

But the genes typically do not replicate across studies. Even when they do replicate, they never explain more than a tiny fraction of any interesting trait. In fact, classical Mendelian genetics based on family studies has identified far more disease-risk genes with larger effects than GWAS research has so far.

Why the failure? The missing heritability may reflect limitations of DNA-chip design: GWAS methods so far focus on relatively common genetic variants in regions of DNA that code for proteins. They under-sample rare variants and DNA regions translated into non-coding RNA, which seems to orchestrate most organic development in vertebrates. Or it may be that thousands of small mutations disrupt body and brain in different ways in different populations. At worst, each human trait may depend on hundreds of thousands of genetic variants that add up through gene-expression patterns of mind-numbing complexity.

Uwe E. Reinhardt: healthcare comparative effectiveness analysis and cost-effectiveness analysis

from http://economix.blogs.nytimes.com/2009/03/13/cost-effectiveness-analysis-and-us-health-care/

With so much brouhaha over what should be thought of as basic operations research for health care, it may be well to explore what “comparative effectiveness analysis” is, and how it is related to what is known as “cost-effectiveness analysis.”

The sketch below describes the basic structure of “comparative effectiveness analysis.”

It is assumed that researchers compare two therapies aimed at the same medical condition. The researchers try to determine which of these therapies can be judged “better” in terms of the positive and negative consequences associated with them. In principle, the clinical practice guidelines promulgated by medical specialty societies to help physicians with their daily treatment decisions should be based on this type of carefully structured comparative effectiveness research.

Source: Uwe Reinhardt A diagram of comparative effectiveness analysis.

Alas, in practice most of the currently promulgated guidelines lack that kind of rigorous scientific foundation. For example, as the science reporter Ronald Winslow recently reported in The Wall Street Journal “just 11 percent of more than 2,700 recommendations approved by cardiologists for treating heart patients are supported by high-quality scientific testing, according to new research.”

That circumstance alone justifies spending billions more than we traditionally have on operations research for an industry that now absorbs $2.5 trillion or close to 17 percent of our gross domestic product. Why anyone would oppose that kind of research challenges one’s imagination.

Early drafts of the economic stimulus bill, however, referred not only to “comparative effectiveness research,” which keeps the analysis strictly in the clinical realm, but also to “comparative cost-effectiveness analysis,” which brings economics into the inquiry. That analysis seeks to establish which of several alternative therapeutic strategies capable of achieving a given therapeutic goal is the least-cost strategy. It seems a sensible form of inquiry in a nation that is dismayed over the rising cost of its health care.

Indeed, in recent years most industrialized nations have begun to subject clinical practices in their health systems to this type of analysis, as have private insurers in the United States (see, for example, the American Journal of Managed Care).

In Congress, however, the word “cost” in this connection remains anathema. This is despite the fact that that same Congress rings its hands in despair over the millions of American families priced out by the ever-rising cost of health care, and over the bigger chunk of the federal budget taken up by Medicare and Medicare.

So, in the end, the offensive term “cost-effectiveness analysis” was stricken from the bill.

The opposition to cost-effectiveness analysis in health care comes from two distinct groups that work closely together and reinforce one another.

The first group includes individuals or enterprises that book other people’s health-care spending as their own health-care income.

The manufacturers of pharmaceutical and biotechnology products or of medical devices are often found in that group, even though in some instances the greater economic transparency provided by cost-effectiveness analysis might help them market their health products or health services. Also in this group are physicians who thrive economically from highly resource-intensive medical treatments, even if some of its components are of only marginal clinical benefit.

The second group among the opponents of cost-effectiveness analysis includes individuals who sincerely believe that health and life are “priceless” — for them, cost should never be allowed to enter clinical decisions. It is an utterly romantic notion and, if I may say so, also an utterly a silly one. No society could ever act consistently on such a credo.