Clinician as the Politbureau of medicine?

Do you think that medicine in the US is centralized? I do, but not in the way that we generally understand centralization. And furthermore, it is this centralization that I believe is making the idea of shared decision making so intimidating to some. Here is what I mean.

If you read management texts, centralization refers to an organization that is run predominantly top-down. In other words, a couple of oligarchs at the top of the ladder make all the decisions without consulting anyone below. In this way all the power is concentrated in the hands of the few. In an antithesis to this, in a decentralized organization, grassroots input and initiatives are incorporated into the fabric of the organization. And while in the times of a great crisis, when rapid decisions are necessary, the benefits of centralization may outweigh its risks, during normal day-to-day operations, such unilateral power can result in obviously negative consequences, from discontent among the employees to making the wrong choices. Furthermore, as organizations grow in size, it gets that much more difficult to run them effectively within the centralized paradigm.

Now, let us look at medicine. The traditional model of the doctor-patient relationship relies on the clinician to know what is right for the patient: take this pill and don't worry about the side effects, dear. Now, clearly, when someone shows up to the emergency room in septic shock, there is very little room for a democratic process; we want the doctor to do rapidly what needs to be done to save the patient. But this is a catastrophic exception to the rule of what modern medicine cares for. From pre-diabetes to pre-hypertension to "borderline cholesterol" to osteopenia to mild depression, these are the "diseases" that are prevalent in the office of the 21st century. None of these is particularly urgent or life-threatening. And if we are honest with ourselves, even a devastating diagnosis of cancer does not demand an instantaneous intervention: in the vast majority of cases there is ample time for discussion and contemplation. So, the centralized approach is the wrong way to go. Thus enter the robust discussion about shared decision making. 

Another reason that centralization of medical decisions is crumbling is the expanding patient panels that clinicians need to engage with in order to stay solvent, all within the context of increasing compliance and regulatory burdens along with decreasing reimbursements. Without an equal growth in one's cognitive ability to multi-task, this escalating imbalance is creating a rising risk for unilateral decisions to be plain wrong.

So, in my mind, this is yet another argument for all parties to embrace shared medical decision making to the extent we as patients are willing and able to do so. Because what is the alternative?          

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Why patient lab data should be liberated, with a few caveats

I am admittedly not an expert on health IT, but I am a firm believer in the empowerment of patients to be the driver of her/his health decision making. So this whole discussion about lab data being available directly to the patient is of great interest to me. But it does seem like yet another instance of the two sides coming together not to listen to each other but to be heard by the other side. And as well know, this works so well for any relationship!

Each side's view is represented roughly thusly:
Patients -- these are my data and I have the right to access them as soon as they are available.
Doctors -- we are worried that the sheer volume, complexity and irrelevance of (much) of the data will make it confusing and unnecessarily alarm the patient
Both arguments are valid, of course. But it is important to ask what lurks below the visible portion of each iceberg.

Let's take the patient view. Why do I want immediate access to my data? Well, obviously, because it is mine, it represents the results of testing on my body, and the record should belong to me. I should be able to access it freely whenever I damned well please. I am also more than a little exasperated with having to wait sometimes days to hear from my doctor's office about a result that has been available for a while, but was buried under the reams of paperwork on the MD's desk or his/her assigning a low priority to my data. And I am most exasperated when my lab results get lost or otherwise never make it to me at all. Perhaps if I have direct and unfettered access, this will make thing more efficient for me as an individual.

The doc's view, on the other hand, is that the patient does not necessarily understand what the notation of "low"connotes in reference to, say, total bilirubin, or how to interpret the RDW data. Even more importantly, what if there is an outrageously abnormal value for some important test? Surely the patient will desire an immediate explanation of it and its implications.

So, clearly, both sides have valid concerns. I do think that those of access predominate, as ethically it just makes sense. But for a non-medical person, looking at a lab sheet is like trying to read information about yourself in Chinese: your success in understanding is largely dependent on your ability to read and understand Chinese. So, before that horse leaves the barn, we should think through how to execute this most sensibly. For example, perhaps it is not sensible to have the lab computer directly vomit all of the inane values that no one really looks at right to the patient's account. And backing up a step, perhaps it is time for our lab use to be driven not by the lab equipment packages and processes, but to test only for factors that are of value. If I want to know the patient's creatinine, maybe the other 6 components of the Chem-7 should not be run, or at least not reported. And obscure values like the ones I mentioned above, e.g., RDW, MCHC, etc., should only be available when the situation actually makes them useful, and not just distracting.

I can see a potential positive unintended consequence of this development as well: maybe clinicians will be less trigger-happy ordering all kinds of labs for all kinds of oblique reasons. Maybe, just maybe, this apprehension about the patient's access to all the labs will result in more Bayesian thinking in the office and a lot less shot-gunning. Finally, it will not be all patients that choose to access their data. Let us hope that the selection bias does its job and assures that only those who are truly ready to be educated and empowered decide to do so.

All in all, I am looking forward to the liberation of my lab data. What I worry about is all the calls I will be getting from friends and family to help them understand them. All the same, I will do my part for the education and empowerment that absolutely needs to happen for this to be a successful and meaningful change.        

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Side effects: The subject must become the scientist

A few weeks ago someone I know, a normally robust and energetic woman, began to feel fatigued and listless, and had some strange sensations in her chest. She presented to her primary care MD, who obtained an EKG and a full panel of blood tests. The former showed some non-specific changes, while the latter was entirely normal. Although reassured, she continued to experience malaise. When she fetched her EKG, she received a copy with the computer interpretation indicating that, in its wisdom, the program could not rule out a heart attack. Given that her symptoms continued, and now anxiety was piled on top, she presented to the ED, where a heart attack was excluded, and she was scheduled for a stress test. In the subsequent weeks the symptoms continued off and on, and the stress test turned out to be negative for coronary disease. Great, mazel tov!

What I failed to mention was that just prior to the onset of her symptoms, she had been started on 5-fluorouracil cream for a basal cell skin cancer. And while she did not commit my current device of omission with her doctors (including the dermatologist who prescribed the drug), all denied her constellation of symptoms as a potential side effect. And granted, when I looked it up, there was no mention of anything like fatigue and listlessness. So, does it mean that it is not within the realm of the possible that this drug was responsible?

Not at all. And here is why. Our adverse event reporting is essentially a discretionary system. Here is what the FDA says about their Adverse Event Reporting System (AERS):

Reporting of adverse events from the point of care is voluntary in the United States. FDA receives some adverse event and medication error reports directly from health care professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report these events to the products’ manufacturers. If a manufacturer receives an adverse event report, it is required to send the report to FDA as specified by regulations. 

What this means is that, when a patient complains to a doctor of a symptom, even when its onset is in obvious proximity to a particular medication, the doctor is not compelled to report it. The most an average physician will do is look up the known AE profile of the drug and at best look up its interactions with other medications. But one is not generally inclined to use one's imagination (and the constraints of the shrinking appointments spread across exponentially growing cognitive loads conspire against it too) to entertain the possibility that the current problem is related. And yet since many AEs are particularly rare, the knowledge about them must necessarily rely on scrupulous reporting by the prescribers into a central repository. This is what is missing: not the repository, but the impetus to report.

So, when we go looking up side effects of a given medication, we must take the information for what it is: a woefully incomplete list of what has been experienced by other patients. And when someone asks "Do statins make you stupid," instead of denying the possibility, we should just admit that we don't know. Because once drugs are released by the FDA into the wild of our modern healthcare, by relying on others' reports of AEs we become inadvertent enablers of our ignorance about them.

My friend's symptoms abated after she finished the course of the 5-FU cream. None of the MDs bothered to report her symptoms to the AERS, and nor did she. I am not even sure that any of the players were aware of the possibility. Oh, well, an opportunity lost. We need to feel responsible for gathering this knowledge. The subject must be empowered to become the scientist; this is the only way we can get the full picture of the harm-benefit balance of our considerable and unruly pharmacopeia.

If you want to report a possible side effect of a medication, this FDA web page will guide you through the process.

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Counterfactuals: I know you are, but what am I?

It occurs to me that as we talk more and more about personalized medicine, the tension between the need for individual vs. group data is likely to intensify. And with it, it is important to have the vocabulary to articulate the role for each.

Scientific method, in order to disprove the null hypothesis, demands highly controlled experimental conditions, where only a single exposure is altered. While this is feasible when dealing with chemical reactions in a beaker, and even, to a great extent, with bacteria and single cells in a petri dish, the proposition becomes a whole lot more complicated in higher order biology. In this way, the phrase "all things being equal" must really apply to the individuals or groups under study.

We call this formulation "the theory of counterfactual," and it is defined in the following way by the researchers at the University of North Carolina (see slide #3 in the presentation):

Theory of Counterfactuals

–The fact is that some people receive treatment.

–The counterfactual question is: “What would have happened to those who, in fact, did receive treatment, if they had not received treatment (or the converse)?”

–Counterfactuals cannot be seen or heard—we can only create an estimate of them.

–Take care to utilize appropriate counterfactual

So, essentially what it means is figuring out what would have happened to, for example, Uncle Joe if he had not smoked 2 packs of cigarettes per day for 30 years. Now, our complexity as the human organism makes it impossible (so far) to replicate Uncle Joe precisely in the laboratory, so we must settle for individuals or groups of individuals that resemble Uncle Joe in most if not all identifiable ways in order to understand the isolated effect of heavy smoking on his health outcomes.

So, you see the challenge? This is why we argue about the validity of study designs to answer clinical questions. This is why a randomized controlled trial is viewed as the pinnacle of validity, since in it, just by the sheer force of randomness in the Universe, we expect to get two groups that match in every way except the exposure in question, such as a drug or another therapy. This is why we work so hard statistically in observational studies to assure that the outcome under examination is really due to the exposure of interest (e.g., smoking), "all other things being equal."

But no matter how we slice this pie, this equality can only be approached, but never truly reached. And this asymptotic relationship of our experimental design to reality may be OK in some instances, yet not nearly precise enough in others. We just cannot know the complete picture, since we only have partial information on how the human animal really works. And this is precisely what makes our struggle to infer causality problematic, and precisely what introduces uncertainty into our conclusions.

What is the answer? Is it better to rely on individual experience or group data? As always, I find myself leaning inward toward the middle. Because an individual's experience is prone to many influences, both internal, such as cognitive biases, and external, such as variations in response under different circumstances, it is not valid to extrapolate this experience to a group. In the same vein, because groups represent a conglomeration of individual experiences, smoothing out the inherent variabilities which ultimately determine the individual results, study data are also difficult to apply to individuals. For this reason medicine should be the hybrid of the two: the make-up of the patient can partly fit into the larger set of persons with similar characteristics, yet also jut out into the perilous territory of idiosyncratic individuality. This is precisely what makes medicine so imprecise. This is precisely the tension between the science and the art of medicine. Because "counterfactuals cannot be seen or heard," Uncle Joe!          

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Medicine and the internet: Harnessing the yottabytes

What if medicine in the US is just like the internet? What if it is just as difficult to separate the chaff from the wheat in medicine as it is on the web?

Both the curse and the blessing of the web is its accessibility. This means that anyone's voice can be heard. And it also means that anyone's voice can be heard. So, we are just as likely to stumble upon drivel as we are on information gold. And what takes time and skill is separating the two into neat piles, one to be ruthlessly discarded, and the other cherished for how it enriches us. To be sure without the web we might not have had access to either, and it is the egalitarian nature of the internet that gives us such a variety of sources in our information diet.

Now, let's look at medicine. Every day we hear about how much noise there is in the field, and this noise is difficult, if not impossible, to separate from the signal. Some signals are becoming much clearer, and they tell us that by being too egalitarian in medicine, we have likely been causing great harm. Take, for example, PSA and mammography screenings. The drumbeat of harm associated with these highly non-specific tests and the resultant chase after false positive results, is getting deafening, and rightfully so. Every day we hear that researchers have uncovered a breakthrough mechanism or treatment, and we hear with increasing frequency that a treatment previously thought to be sacrosanct is a bunch of rubbish. What gets lost among all this noise is the possibility of a true breakthrough in disease management or treatment or cure.

Think how hard it is to separate general valuable content from bunk on the web. Now, think of the logs of increase in the levels of difficulty of this task in medicine, where difficult concepts are further shrouded in the opaque cloth of arcane and obfuscating terminology. In fact, it is so difficult, that the class previously designated as the interpreters of this information for the lay public, physicians, are unable to keep up. There is a need for a whole new class of interpreters now -- researchers and patient advocates. And while this is good for the market and the economy, since it creates jobs that had not existed before, it begs a more critical evaluation vis a vis its impact on public's health. It also begs the question of the value of this gadgetry and information glut in medicine -- what is truly the wheat and what is the chaff? And what happens when you continuously try to drink from a fire hose? And do we turn down the stream, or is there another way?

Is it feasible to limit this stream of idea and information generation? Furthermore, is it sensible to do so? Many worry that putting limitations on this is tantamount to stifling innovation. But what is innovation? The most pertinent definition to the current discussion in the Merriam-Webster dictionary is "a new idea, method or device." Nowhere does the definition incorporate the value of this idea, method or device. Perhaps it is left to the free market to determine this value and ultimate use of such innovation. Well, in a market that claims to be free, but is filled with cynical machinations in the form of favoritism, subsidies and pricing games, is objective value really what is valued? And indeed, given the complexity of these "innovations", is it even possible for the end-user to judge their value, even if the market were free?

Yet, even despite all these challenges to establishing the value of innovation on the back end, I am not sure that centrally limiting idea generation is either feasible or right. In the case of ideas on the web, I have come to the conclusion that such microblogging platforms as Twitter can be invaluable filters of information, where my network of favorite tweeters whom I follow faithfully provides me with the wheat that has already been cleaned, yet not always overprocessed. Is this possible in medicine? I know that the FDA and CMS are supposed to provide some filtration for such medical information and interventions, but each is statutorily handcuffed and gagged not to stray beyond their legislative agendas. Therefore, a value filter should not be a body beholden to the letter of the law, or to political or financial interests. It needs to be driven by the spirit of scientific curiosity, objective evaluation and pragmatism. Most importantly, it must be open to a conversation that incorporates respectful dissent and many different perspectives.

Twitter arose out of the drive to share information, and it has shaped itself as a tool for developing value in the gargantuan and ever-growing world of yottabytes. Perhaps it is citizen bloggers and tweeters, including e-patients and clinicians and researchers and writers and others, who will ultimately solve this information glut in medicine by extracting the kernel of usefulness from this morass of vegetation. Harnessing this power systematically and accurately is the next challenge of our information age.

Because ultimately, for human cognition and health, less is more. And we are still human.              

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Soliciting contributions: "Healthcare professional as e-patient" series

I am contemplating a series of posts arising from my own recent experience as an e-patient to help the broader e-patient community navigate the stormy medical waters with a bit more comfort. I am looking for other healthcare professionals who have had their own experiences as an e-patient that may be instructive for non-healthcare professionals as patients to contribute to the series. Namely, I am most interested in helping people establish better communication lines and channels with their healthcare providers. I am not looking for a comprehensive description of every aspect of your encounter, but rather one specific point that may be particularly instructive. If you have more than one to share, that is great too, we can do that as well. No bitching or moaning, just lessons that we can all learn from.

Issues I would like to touch upon range from how to bring out risk-benefit balance to how to feel OK about confronting your physician with dissenting information to how best to communicate (not everyone is good on e-mail, for example), given our individual styles and time constraints.

I think contributions from healthcare professionals may be very valuable, as we can see both sides of the coin, so to speak.

Would love feedback from both, healthcare professionals and e-patients on what would be valuable. If you are interested in contributing, please, either let me know in the comments section or e-mail me at healthcareetcblog@gmail.com. If you have an idea for a post, please, be very specific about what your theme is, as I will make decisions based on how relevant it is to what I am envisioning.

This is jut a thought at this point, but seems like there may be something to it. Looking forward to your ideas.

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Reviewing medical literature part 3 continued: threats to validity

As promised, today we talk about confounding and interaction.

A confounder is a factor related to both, the exposure and the outcome. Take for example the relationship between alcohol and head and neck cancer. While we know that heavy alcohol consumption is associated with a heightened risk of head and neck cancer, we also know that people who consume a lot of alcohol are also more likely to be smokers, and smoking in turn raises the risk of H&N CA. So, in this case smoking is a confounder of the relationship between alcohol consumption and the development of H&N CA. It is virtually impossible to get rid of all confounding completely in any study design, save for possibly in a well designed RCT, where randomization presumably assures equal distribution of all characteristics; and even there you need an element of luck. In observational studies our only hope to deal with confounding is through statistical manipulation we call "adjustment", as it is virtually impossible to chase it away any other way. And in the end we still sigh and admit to the possibility of residual confounding. Nevertheless, going through the exercise is still necessary in order to get closer to the true association of the main exposure and the outcome of interest.

There are multiple ways of dealing with the confounding conundrum. The techniques used are matching, stratification, regression modeling, propensity scoring and instrumental variables. By far the most commonly used method is regression modeling. This is a rather complex computation that requires much forethought (in other words, "Professional driver on a closed circuit; don't try this at home"). The frustrating part is that, just because the investigators did the regression, does not mean that they did it right. Yet word limits for journal articles often preclude authors from giving enough detail on what they did. At the very least they should tell you what kind of a regression they ran and how they chose the terms that went into it. Regression modeling relies on all kinds of assumptions about the data, and it is my personal belief, though I have no solid evidence to prove it, that these assumptions are not always met.

And here are the specific commonly encountered types of regressions and when each should be used:
1. Linear regression. This is a computation used for outcomes that are continuous variables (i.e., variables represented by a continuum of numbers, like age, for example). This technique's main assumption is that the exposure and outcome are related to each other in a linear fashion. The resulting beta coefficient is the slope of this relationship if it is graphed.
2. Logistic regression. This is done when the outcome variable is categorical (i.e., one of two or more categories, like gender, for example, or death). The result of a logistic regression is an adjusted odds ratio (OR). It is interpreted as an increase or a decrease in the odds of the outcome occurring due to the presence of the main exposure. Thus, a OR of 0.66 means that there is a 34% reduction in the odds (used interchangeably with risk, though this is not quite accurate) of the outcome due to the presence of the exposure. Conversely, a OR of 1.34 means the opposite, or a 34% increase in the odds of the outcome if the exposure is present.
3. Cox proportional hazards. This is a common type of a model developed for a time to event, also known as "survival analysis" (even if not done for survival per se as the outcome). The resulting value is a hazard ratio (HR). For example, if we are talking about a healthcare-associated infection's impact on the risk of remaining in the hospital longer, a HR of, say, 1.8 means that a HAI increases the risk of being in the hospital by 80% at any time during the hospitalization. To me this tends to be the most problematic technique in terms of assumptions, as it requires that the risk of an even stays constant throughout the time frame of the analysis, and how often does this hold true? For this reason the investigators should be explicit about whether or not they tested for the assumption of proportional hazards and whether this was met.

Let's now touch upon the other techniques that help us to unravel confounding. Matching is just that: it is a process of matching subjects with the primary exposure to those without in a cohort study or subjects with the outcome to those without in a case-control study, based on certain characteristics, such as age, gender, comorbidities, disease severity, etc.; you get the picture. By its nature, matching reduces the amount of analyzable data, and thus reduces the power of the study. So, is is most efficiently applied in a case-control setting, where it actually improves the efficiency of enrollment.

Stratification is the next technique. The word "stratum" means "layer", and stratification refers to describing what happens to the layers of the population of interest with and without the confounding characteristic. In the above example of smoking confounding the alcohol and H&N CA relationship, stratifying the analyses by smoking (comparing the H&N CA rates among drinkers and non-drinkers in the smoking group separately from the non-smoking group) can divorce the impact of the main exposure from that of the confounder on the outcome. This method has some distinct intuitive appeal, though its cognitive effectiveness and efficiency dwindle the more strata we need to examine.

Propensity scoring is gaining popularity as an adjustment method in the medical literature. A propensity score is essentially a number, usually derived from a regression analysis, giving the propensity of each subject for a particular exposure. So, in terms of smoking, we can create a propensity score based on other common characteristics that predict smoking. Interestingly, some of these characteristics will be present also in people who are not smokers, yielding a similar propensity score in the absence of this exposure. Matching smokers to non-smokers based on the propensity score and examining their respective outcomes allows us to understand the independent impact of smoking on, say, the development of coronary artery disease. As in regression modeling, the devil is in the details. Some studies have indicated that most papers that employ propensity scoring as the adjustment method do not do this correctly. So, again, questions need to be asked and details of the technique elicited. There is just no shortcut to statistics.

Finally, a couple of words about instrumental variables. This method comes to us from econometrics. An instrumental variable is one that is related to the exposure but not the outcome. One of the most famous uses of this method was published by a fellow you may have heard of, Mark McClellan, where he looked at the proximity to a cardiac intervention center as the instrumental variable in the outcomes of acute coronary events. Essentially, he argued, the randomness of whether or not you are close to a center randomizes you to the type of treatment you get. Incidentally, in this study he showed that invasive interventions were responsible for a very small fraction of the long-term outcomes of heart attacks. I have not seen this method used that much in the literature I read or review, but am intrigued by its potential.

And now, to finish out this post, let's talk about interaction. "Interaction" is a term mostly used by statisticians to describe what epidemiologists call "effect modification" or "effect heterogeneity". It is just what the name implies: there may be certain secondary exposures that either potentiate or diminish the impact of the main exposure of interest on the outcome. Take the triad of smoking, asbestos and lung cancer. We know that the risk of lung cancer among smokers who are also exposed to asbestos is far higher than among those who have not been exposed to asbestos. Thus, asbestos modifies the effect of smoking on lung cancer. So, to analyze those smokers exposed to asbestos together with those who were not will result in an inaccurate measure of the association of smoking with lung cancer. More importantly, it will fail to recognize this very important potentiator of tobacco's carcinogenic activity. To deal with this, we need to be aware of the potentially interacting exposures, and either stratify our analyses based on the effect modifier or work the interaction term (usually constructed as a product of the two exposures, in out case smoking and asbestos) into the regression modeling. In my experience as a peer reviewer, interactions are rarely explored adequately. In fact, I am not even sure that some investigators understand the importance of recognizing this phenomenon. Yet, the entire idea of heterogeneous treatment effect (HTE) and our pathetic lack of understanding of its impact on our current bleak therapeutic landscape, is the result of this very lack of awareness. The future of medicine truly hinges on understanding interaction. Literally. Seriously. OK, at least in part.

In the next installment(s) of the series we will start tackling study analyses. Thanks for sticking with me.        

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Reviewing medical literature, part 2a: Study design

It is true that the study question should inform the study design. I am sure you are aware of the broadest categorization of study design -- observational vs. interventional. When I read a study, after identifying the research question I go through a simple 4-step exercise:
1. I look for what the authors say their study design is. This should be pretty easily accessible early in the Methods section of the paper, though that is not always the case. If it is available,
2. I mentally judge whether or not it is feasible to derive an answer to the posed question using the current study design. For example, I spend a lot of time thinking about issues of therapeutic effectiveness and cost-effectiveness, and a randomized controlled trial exploring efficacy of a therapy cannot adequately answer the effectiveness questions.
If the design of the study appears appropriate,
3. I structure my reading of the paper in such a way as to verify that the stated design is, in fact, the actual design. If it is, then I move on to evaluate other components of the paper. If it is not what the authors say,
4. I assign my own understanding to the actual design at hand an go through the same mental list as above with the current understanding in mind.

Here is a scheme that I often use to categorize study designs:

As already mentioned, the first broad division is between observational studies and interventional trials. An anecdote from my course this past semester illustrates that this is not always a straight-forward distinction to make. In my class we were looking at this sub-study of the Women's Health Initiative (WHI), that pesky undertaking that sank the post-menopausal hormone replacement enterprise. The data for the study were derived from the 3 randomized controlled trials (RCT) of HRT, diet and calcium and vitamin D, as well as from the observational component of the WHI. So, is it observational or interventional? The answer to this is confusing to the point of pulling the wool over even experienced clinicians' eyes, as became obvious in my class. To answer the question, we need to go back to definitions of "interventional" and "observational". To qualify as an interventional, a study needs to have the intervention be a deliberate part of the study design. A common example of this type of a study is the randomized controlled trial, the sine qua non of drug evaluation and approval process. Here the drug is administered as a part of the study, not as a background of regular treatment. In contradistinction, an observational study is just that: an opportunistic observation of what is happening to a group of people under ordinary circumstances. Here no specific treatment is predetermined by the study design. Given that the above study looked at multivitamin supplementation as the main exposure, despite its utilization of the data from RCTs, the study was observational. So, the moral of this tale is to be vigilant and examine the design carefully and thoroughly.

We often hear that observational designs are well suited to hypothesis generation only. Well, this is both true and false. Some studies actually can test hypotheses, while others are relegated to generation only. For example, cross-sectional and ecological studies are well suited to generating hypotheses to be tested by another design. To take a recent controversy as an example, the debunked link between vaccinations and autism initially gained steam from the observation that as the vaccination rates were rising, so was the incidence of autism. The type of a study that shows two events changing at the group/population level either in the same or in the opposite direction is called "ecologic". Similar types of studies gave rise to the vitamin D and cancer association hypothesis, showing geographic variation in cancer rates based on the availability of sun exposure. But, as demonstrated well by the vaccine-autism debacle, running with the links from ecological studies is dangerous, as they are prone to a so-called "ecological fallacy". It occurs when, despite the finding in groups of a linked change of the two factors under investigation, there is absolutely no connection between them at the individual level. So, don't let anyone tell you that they tested an hypothesis in an ecological study!

Similarly in cross-sectional studies an hypothesis cannot be tested, and, therefore, causation cannot be "proven". This is due to the fundamental property of "a snapshot in time" that defines a cross sectional study. Since all events (with few minor exceptions) happen at the same time, it is not possible to assign causation to the exposure-outcome couplet. These studies can merely help us think of further questions to test.

So, to connect the design back to the question, if a study purports to "explore a link between exposure X and outcome Y", either an ecologic or a cross-sectional design is OK. On the other hand, if you see one of these designs used to "test the hypothesis that exposure X causes outcome Y", run the other way screaming.

We will stop here for now, and in the next post will continue our discussion of study designs. Not sure yet if we can finish it in one more post, or if it will require multiple postings. Start praying to the goddess of conciseness now!

    

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Reviewing medical literature, part 1: The study question

Let's start at the beginning. Why do we do research and write papers? No, not just to get famous, tenured or funded. The fundamental task of science is to answer questions. The big questions of all time get broken down into infinitesimally small chunks that can be answered with experimental or observational scientific methods. These answers integrated together provide the model for life as we understand it.

Clearly, the question is the most important part of the equation, and this is why in my semester-long graduate epidemiology course on the evaluative sciences we spend fully the first four to five weeks talking about how to develop a valid and answerable question. The cornerstone of this validity is its importance. Hence, the first question that we pose is: Is the study question important?

This is a bit of a loaded question, though. Important to whom? How is "important" defined? This is somewhat subjective, yet needs to be scrutinized nevertheless. In the context of an individual patient, the question may become: Is the study question important to me? So, importance is dependent on perspective. Nevertheless, there are questions upon whose importance we can all agree. For example, the importance of the question of whether our current fast-food life style promotes obesity and diabetes is hard to dispute.

Regardless of how we feel about the importance of the question, we must first identify the said research question. At least some of the time you will be able to find it in the primary paper, buried in the last paragraph of the Introduction section. Most of the questions we ask relate to etiologic relationships ("etiology" is medicalese for "causation"). Now, you have heard many times that an observational study cannot answer a causal question. Yet, why do we bother with the time, energy and money needed to run observational studies? Without getting too much into the weeds, philosophers of science tell us that no single study design can give us unequivocal evidence of causality. We can merely come close to it. What does this mean in practical terms? It means that, although most observational studies are still interested in causality rather than a mere association, we have to be more circumspect in how we interpret the results from such studies than from interventional ones. But I am jumping ahead.

Once we have identified and established the importance of the question, we need to evaluate its quality. A question of high quality is 1). clear, 2). specific, and 3). answerable. The question that I posed above regarding fast food and obesity possesses none of these characteristics. It is too broad and open to interpretation. If I were really posing a question in this vein, I would choose a single well defined exposure (consuming 3 cans of soda per day) influencing a single outcome (10% body weight gain) over a specific period of time (over 30 weeks). While this is a much narrower question that the one I proposed above, it is only by answering bundles of such narrow questions and putting the information together that we can arrive at the big picture.

A general principle that I like to teach to my student is the PICO or PECOT model (I did not come up with it, but am its avid user). In PICO, P=population, I=intervention or exposure, C=comparator, and O=outcome. The PECOT model is an adaptation of the PICO for observations over time, resulting in P=population, E=exposure, C=comparator, O=outcome, T=time. These models can help not only pose the question, but to unravel the often mysterious and far from transparent intent of the investigators.

Once you have identified the question and dealt with its importance, you are ready to move on to the next step: evaluating the study design as it relates to the question at hand. We will discuss this in the next post.

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Series launch: Critical review of medical literature

Today I am launching a series of posts on how to read medical literature critically. The series should provide a solid foundation for this task and dove-tail nicely with some of the more dense methods themes that occur on this blog. Who should read the series? Everyone. Although the current model of dissemination of medical information relies on a layer of translators (journalists and clinicians), it is my belief that every educated patient must at the very least understand how these interpreters of medical knowledge (should) examine it to arrive at the information imparted to the public. At the same time, both journalists and clinicians may benefit from this refresher. Finally, my own pet project is to get to a better place with peer reviews -- you know how variable the quality of those can be from my previous posts. So, I particularly encourage new peer reviewers for clinical journals to read this series.  

First, a conflict of interest statement. What comes first -- the chicken or the egg? What comes first -- expertise in something or a company hiring you to develop a product? Well, in my case I would like to think that it was the expertise that came first and that Pfizer asked me to develop this content based on what I know, not on the fact that they funded the effort. At any rate, this is my disclaimer: I developed this presentation about three years ago with (modest) funding from Pfizer, and they had it on a web site intended for physician access. Does this mere fact invalidate what I have to say? I don't think so, but you be the judge.

Roughly, the series will examine how to evaluate the following components of any study:
1. Study question
2. Study design
3. Study analyses
4. Study results
5. Study reporting
6. Study conclusions
I am not trying to give you a comprehensive course on how all of this is done, but merely make the reader aware of what entails a critical review of a paper.

Look for the first installment of the series shortly.

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The changing language of medicine

A very close friend of mine has breast cancer. It is a very small tumor, diagnosed on an annual mammogram, requiring confirmation with a breast MRI. She had a lumpectomy today, and I was with her at the hospital. This proved to be an enlightening experience.

To put things in perspective, when I was in training and in practice (yes, in the dark ages when we were expected to stay awake AND care for patients for over 48 hours at a time every 3 days), we had not heard of patient-centered medicine. I learned that my role was to diagnose, come up with a plan of action and convince the patient at any cost that my plan was the correct one. To be sure, I always tried to do this in a nice way, but would get a bit impatient when my judgment was questioned. This is the behavior modeled for me by my elders and others whom I respected.

Well, that was then. Having had quite a few years to reflect on the practice of medicine in the context of our healthcare system, I have learned just how misguided this attitude is. And, being a Sagittarius, I cannot fathom how this universal truth is escaping others. Yet escaping it is. This became obvious to me today.

My friend had to have a nuclear medicine test prior to her lumpectomy to define the extent of axillary nodal involvement. She had been told that this is an arduous and painful experience that cannot be mitigated with pre-medication. She was also informed that asking the radiologist to deliver the radionuclide slowly rather than as a rapid push might reduce the sensation. So, my friend, who is herself a physician, was prepared for a civilized and simple conversation with the practitioner. Yet, this is not what transpired. You would think that being asked to deliver the chemical slowly is not such a big and unreasonable request. Well, if you thought this, you were wrong: evidently this was such a big ego blow to the radiologist that she felt compelled to respond snidely, "Well, OK, I am not going to fight with you about it". Now, this is off-putting under the best of circumstances. Imagine being about to go to the OR to have a cancer removed from your breast, and having this snide come-back thrown at you. And why? What is the harm in going along with the patient's request if it makes no difference in the end-result of the test? Is it really necessary to diminish her in such a blatant way?

Well, this physician was of a similar vintage to me, and I can only imagine that she came into practice before patient-centered care became the standard. In her mind, as in mine in those distant days, my involvement with the patient's care was not really about the patient necessarily, unless they fell in line with my recommendation. The shameful fact is that my ego was much too fragile to allow a discussion or questions about my considered course of action. How could they go against my years of training, deep knowledge and their best interests? I cannot say for sure, but it is likely that my friend's radiologist was cut from similar cloth. And what is so obvious to me today has not yet been assimilated by so many of my colleagues, including this person.

As I have said before, the new direction for medicine cannot be what I am used to in real estate: "I do not have what you need, but I will show what I do have". The new direction in medicine must undoubtedly be one where the patient is the center of the encounter, and it is the patient's interest rather than the doctor's ego that must be protected assiduously.

Lest you think that the entire hospital experience was negative, let me be clear: of all the people taking care of my friend, the radiologist was the sole disappointing exception. Her surgeons, anesthesiologists, nurses and ancillary personnel went above and beyond my expectations. I was amazed by the level of civility, good humor, politeness and real involvement everyone exhibited -- it was truly different from my days on the wards and pleasantly eye-opening. It even gave me some hope for the future of medicine in the midst of my normally nihilistic ruminations.            

The great poet Rumi said that changing language can change our life. Well, when the recovery room nurse said to my friend "Let me know when you feel that you would rather rest at home than here", I was overcome with warmth and good will. The language of medicine does seem to be changing. And if it continues in this vein, perhaps it will change our lives.

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Why we need collaborations across healthcare sectors

I want to digress from our recent focus on methods and talk a bit about conflict of interest (COI for short). There has been a lot in the press lately about doctors taking money from the biopharmaceutical manufacturers, and doctors inserting unnecessary hardware into patients' hearts and spines. All of this has been happening against the background of a low hum of an ongoing discussion of what constitutes a COI, how much is too much and for what (for example, can a doc who takes research and education dollars from a manufacturer with an interest in anticoagulation sit on a committee that develops the guidelines for prevention of thromboembolic disease?), and how to mitigate these ubiquitous and pesky COIs.

In some ways watching this discussion has been amusing, while in others it has been downright sad. Medical journals, while insisting that advertising money is OK to take (presumably because the editorial and marketing offices are separated by some sort of a fire wall), though professional societies should not be able to take this tainted education money. Professional societies, on the other hand, are running away from the accusations by tightening their continuing medical education (CME) criteria and scrambling to replace the lavish budgets derived from pharma to develop their coveted evidence-based practice guidelines. And while all the pots are calling all the kettles black, academic researchers are being barred from collaborating with the industry on research projects, and industry researchers are being precluded from presenting their data at professional society meetings. While all the time the public is being whipped into lather about these alleged systematic transgressions, and forced to cheer for the ensuing retribution.

But, like many things in life, and especially stuff that we discuss on this blog, this issue is neither black nor white. Don't take me wrong: I am not condoning the egregious excesses of greed demonstrated by some members of my hallowed profession. If you have been reading my blog for some time, you know that I do not dispute the shameful reality of many breeches of public trust. I am an ardent supporter of exposing these breeches and of harsh punishments that they deserve. This is not what I am talking about here.

I am much more concerned about the one-sided story that we have been hearing about pharma-academic collaborations. Because of the persecutory nature of public opinion, some institutions are now shying away from such collaborations. This attitude is akin to navigating a treacherous road while looking in the rearview mirror. Yes, there have been transgressions, yes there has been greed and even scientific fraud in the name of money. Does this mean that we need to stop everything and come up with an entirely new way of managing these risks? Absolutely! Does this mean that we have to get rid of all pharma-academic collaborations? Absolutely not! In my humble opinion, erecting non-scaleable walls between these two groups is a big mistake. Here is why.

First, let me make a disclaimer: I do have active ongoing collaborations with multiple manufacturers. I do not take speaking or other promotional money, but limit myself to consulting and research grant funding. I also do a good deal of unfunded research, and I have never taken a penny for any of my blogging or blogging-related activities. And here is the crux of the matter: In this world of über-subspecialization, with the expertise being demographically and geographically diffuse, how can we afford not to collaborate across different types of organizations with different types of capabilities? Can we really afford to leave all of therapeutic development in the hands of organizations whose overarching purpose is to make money? And equally importantly, can we afford to continue this fragmented model of medical development without any thought to integration of the needs of all of the stake holders? I think not. Just as we are reaping the fruit of electronic medical record development in isolation from the end-user, so this isolation of research effort will lead to even less coherence in medicine. And unless we are ready to socialize our entire healthcare system, it seems naïve to expect that this one sector will acquiesce and start working outside of our coveted free market for the good of humankind alone.

My readers know that I am not an industry apologist. On the contrary, I have said many times that there has been bad behavior across all the sectors of healthcare, starting with biopharma. But if we want to advance rather than stagnate and regress, we need robust collaborations. We also need higher ethical standards and greater professionalism to keep public's health as our top priority.

There is COI everywhere, and, while financial COI is most visible, it is the more hidden COI that is most insidious. An hidden COI can be intellectual, reputational, ego-driven, career-mediated, etc. It is incumbent on us all in this complex world to ask questions and mitigate any ill effects of any cognitive biases, including those created by COI. Ultimately, as I have begun to realize of late, nothing will replace an educated and empowered patient: This is the only model that can provide appropriate checks and balances for our oftentimes misaligned and perverse incentives, both academic and economic.

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How e-patients can fix our healthcare system

We got a little into the weeds last week about significance testing and test characteristics. Because information is power, I realized that it may be prudent to back up a bit and do a very explicit primer on medical testing. I am hoping that this will provide some vocabulary for improved patient-clinician communication. But, alas, please do not be surprised if your practitioner looks at you as if you were an alien -- it is safe to say that most clinicians do not think in these terms in their everyday practices. So, educate them!

Let's dig a little deeper into some of the ideas we batted around last week, specifically those pertaining to testing. Let's start by explicitly establishing the purpose of a medical test. The purpose of a medical test is to detect disease when such disease is present. This fact alone should underscore the importance of your physician's ability to arrive at the most likely reasons for your symptoms. This exercise that every doc should go through as he/she is evaluating you is called "differential diagnosis". When I was a practicing MD, my strategy was to come up with 3-5 most likely and 3-5 most deadly if missed potential diagnoses and explore them further with appropriate testing. Arranging these possible diagnoses as a hierarchy can help the clinician to assign informal probabilities to each, a task that is central to Bayesian thinking. From this hierarchy then follows the tactical sequential work-up, avoiding the frantic shotgun approach.

So, having established a hierarchy of diagnoses, we now engage in adjunctive testing. And here is where we really need to be aware not only of our degree of suspicion for each diagnosis, but also the test characteristics as they are reported in the literature and the test characteristics as they exist in the local center where the testing takes place. Why do I differentiate between the literature and practice? We know very well that the mere fact of observation, not to mention experimental cleanliness of trials, often tends to exaggerate the benefits of an intervention. In other words, real world is much messier than the laboratory of clinical research (which of course itself is messy enough). So, it is this compounded messiness that each clinician has to contend with when making testing decisions.

OK, so let us now deconstruct test characteristics even further. We have used the terms sensitivity, specificity, positive and negative predictive values. We've even explored their meanings to an extent. But let's break them down a bit further. Epidemiologists find it helpful to construct 2-by-2 (or 2 x 2) tables to think through some of these constructs, so, let's engage in that briefly. Below you see a typical 2 x 2 table.

In it we traditionally situate disease information in columns and test information in rows. A good test picks up signal when the signal is there while adding minimal noise. The signal is the disease, while the noise is the imprecise nature of all tests. Even simple blood tests, whose "objective accuracy" we take for granted, are subject to these limitations.

It is easiest to think of sensitivity as how well the test picks up the corresponding disease. In the case of mammography from last week, this number is 80%. This means that among 100 women who actually harbor breast cancer a mammogram will recognize 80. This is the "true positive" value, or disease actually present when the test is positive. What about the remaining 20? Well, those real cancers will be missed by this test, and we call them a "false negative". If you look at the 2 x 2 table, it should become obvious that the sum of the true positives and the false negatives adds up to the total number of people with the disease. Are you shocked that our wonderful tests may miss so much disease? Well, stay tuned.

The flip side of sensitivity is "specificity". Specificity refers to whether or not the test is identifying what we think it is identifying. The noise in this value comes from the test in effect hallucinating disease when the person does not have the disease. A test with high specificity will be negative in the overwhelming proportion of people without the disease, so the "true negative" cell of the table will contain almost the entire group of people without disease. Alas, for any test we develop we walk the tight-rope between sensitivity and specificity. That is, depending on our priorities for testing, we have to give up some accuracy in either the sensitivity or the specificity. The more sensitive the test, the higher our confidence that we will not miss the disease when it is there. Unfortunately, what we gain in sensitivity we usually lose in specificity, thus creating higher odds for a host of false positive results. So, there really is no free lunch when it comes to testing. In fact, it is this very tension between sensitivity and specificity that is the crux of the mammography debate. Not as straight-forward as we had thought, right? And this is not even getting into pre-test probabilities or positive and negative predictive values!

Well, let's get into these ideas now. I believe that the positive and negative predictive values of the test are fairly well understood at this point, no? Just to reiterate, a positive predictive value, which is the ratio of true positives to all positive test results (the latter is the sum of the true and false positives, or the sum of the values across the top row of the 2 x 2 table), tells us how confident we can be that a positive test result corresponds to disease being present. Similarly, the negative predictive value, the ratio of true negative test results to all negative test results (again, the latter being the sum across the second row of the 2 x 2 table, or true and false negatives), tells us how confident we can be that a negative test result really represents the absence of disease. The higher the positive and negative predictive values, the more useful the test becomes. However, when one is likely to be quite high but the other quite low, it is a pitfall of our irrationality to rush head first into the test in hopes of obtaining the answer with a high value (as in the case of the negative predictive value for mammography in women aged 40-50 years), since the opposite test result creates a potentially difficult conundrum. This is where pre-test probability of disease comes in.

Now, what is this pre-test probability and how do we calculate it? Ah, this is the pivotal question. The pre-test probability is estimated based on population epidemiology data. In other words, given the type of a person you are (no, I do not mean nice or nasty or funny or droll) in terms of your demographics, heredity, chronic disease burden and current symptoms, what category of risk you fit into based on these population studies of disease. This approach relies on filing you into a particular cubby hole with other subjects whose characteristics are most similar to yours. Are you beginning to appreciate the complexity of this task? And the imprecision of it? Add to this barely functioning crystal ball the clinician's personal cognitive biases, and is it any wonder that we do not do better? And need I even overlay this with another bugaboo, that of the overwhelming amount of information in the face of the incredible shrinking appointment, to demonstrate to you just how NOT straightforward any of this medicine stuff is?

OK, get your fingers out of that Prozac bottle -- it is not all bad! Yes, these are significant barriers to good healthcare. But guess what? The mere fact that you now know these challenges and can call them by their appropriate names gives you more power to be your own steward of your healthcare. Next time a doc appears certain and recommends some sexy new test, you will know that you cannot just say OK and await further results. Your healthcare is a chess match: you and your healthcare provider need to plan 10 moves ahead and play out many different contingencies.

On our end, researchers, policy makers and software developers all need to do better developing more useful and individualizable information, integrating this information into user-friendly systems, and encouraging thoughtful healthcare encounters. I am convinced that patient empowerment with this information followed by teaming up with providers in advocacy in this vein is the only thing that can assure course correction for our mammoth, unruly, dangerous and irrational healthcare system.
           

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1,000 lives per day or 45 lives every hour

In the wake of the recent studies confirming our suspicions that we are no better off today than a decade ago as far as the safety of our healthcare system is concerned, I have been doing a lot of thinking and writing about this issue. The other day I blogged about the fact that there are no simple solutions, yet we must pursue change. Today, this e-mail from 350.org really stopped me in my tracks:

Dear friends,
Climate negotiations can seem quite abstract sometimes.

I'm here in Cancún, Mexico, where UN delegates from around the world spend hours debating details of complex regulations.  Sometimes it seems that everyone has forgotten a crucial fact: the climate is changing much faster than these negotiations are moving. 

Meanwhile, out in the real world, climate impacts are all too visible. Since the negotations began 10 days ago, climate disasters have struck all over the world: flooding in Australia, Venezuela, the Balkans, Columbia, India; wildfires in Israel, Lebanon, Tibet; freak winter storms in Europe and the United States. These events have been devastating--hundreds are dead, and hundreds of thousands have been affected.

To put it in the context of our healthcare system, the unnecessary mortalities and morbidities are happening faster than our quality improvements are moving! In other words, if there are approximately 400,000 avoidable deaths annually attributable to healthcare encounters, this means that every day we delay implementing a viable solution we lose over 1,000 lives per day or about 45 lives every hour or 1 life every 1 and 1/2 minutes! In the time that it took me to write this post, 20 patients have lost their lives unnecessarily. Are any of them your loved ones?

All these lives come with stories, all these lives are loved by someone, and all these lives cannot just be written off as sacrificial lambs in the name of a growing bureaucracy that cannot move the meter. We can wring our collective hands and say that we wish we knew how to stop this gushing bleed. Yet, we continue to conduct business as usual, increasing revenues and testing and interventions and cognitive loads and questionable evidence. Ultimately, should eleven years of doing the same thing and getting the same woefully inadequate result encourage us to continue in the same direction, or should we just come to a full stop for a moment?

I realize that medicine cannot stop -- illness will not stop. But the lifestyle that feeds the gluttonous homicidal machine of healthcare can be altered. A combination of prevention, reduction of interventions of questionable effectiveness and safety, more time for doctors to think about their patients and make decisions together -- this is the path. It is not easy, but neither is losing a partner, a brother or a child to the very idol at whose altar we have come to worship and atone for all of our individual and societal bad choices. Today is the day. Who is with me?  

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