Or perhaps a more apt title would be “Not Knowing that we Don’t Know in Medicine”. A colleague and I gave a lecture last week on EBM in the context of how do we know what to do in medicine. We pointed out that there are 4 general ways that we know what we know in medicine: authority, clinical experience, pathophysiological rationale, and systematic investigation. I serendipitously read an article late last week in a great new series in JAMA called JAMA Diagnostic Test Interpretation. I thought about all the times I used ammonia levels incorrectly and all the times my colleagues and residents used ammonia incorrectly. Why? Was I too lazy to evaluate the literature in this area? Admittedly I hadn’t but it didn’t even occur to me to do so because during my training my senior residents and attendings told me to check ammonia levels in patients who we suspected had hepatic encephalopathy as it “would help make the diagnosis”. 20 years later an epiphany has made me realize I have been doing the wrong thing for all these years. I didn’t know jack about the limitations of ammonia in chronic liver disease.
Why did I trust authority? Was it because John Mellencamp sang “I fought authority and authority always wins” so I didn’t question what I was told to do? I wonder how many other things like this I do with no idea that I am doing it all wrong. Likely a fair amount. The hard part is there isn’t time to go back and review literature on everything we do. Even if I only reviewed the information in a preappraised resource like Dynamed or UpToDate I wouldn’t have enough time as I can barely find time to keep up with newer things without having to go through all the things I “KNOW” already.
I hope articles like this simple review in JAMA will help educate us all. I hope other journals follow JAMA’s lead and make distilled evidence summaries that we can quickly digest to improve the cost-effective care that we provide. I hope each of us will occasionally, maybe just once a month, question how we “KNOW” something and if we can’t give a good enough answer that we will take the time to find that answer. We likely will be surprised by how much we don’t know in medicine.
I’ve left the hardest issue to deal with for last- “Overemphasis on following algorithmic rules”. This has been the most frustrating aspect of my primary care practice. Patients quit being viewed as patients but a set of goals that I had to achieve to be smiled upon fondly by my boss as being “a good doctor”. It took me some time to finally quit playing the game and just do the best I could do and whatever the numbers were so be it.
Algorithmic medicine couldn’t be any more antithetical to EBM. Everyone is viewed the same. EBM clearly, as I have argued in the last three posts, is about individual patient values and circumstances. It’s about clinical experience temporizing what we could do to what we should do. Algorithmic medicine allows no individuality. No temporizing. Thus to claim EBM is in crisis because of algorithmic medicine is wrong. True EBM protects us from the harms of algorithmic medicine.
Interestingly computerized decision support systems (mentioned as a culprit in the first sentence of this section of Greenhalgh’s paper) are at the top of Haynes’ 6S hierarchy of preappraised evidence.
“In these computerized decision support systems (CDSSs), detailed individual patient data are entered into a computer program and matched to programs or algorithms in a computerized knowledge base, resulting in the generation of patient-specific assessments or recommendations for clinicians” – Brian Haynes
At the VA we have a moderately sophisticated CDSS. It warns me if my patient with heart failure is not taking an ACE inhibitor and its smart enough that if I enter an allergy to ACE inhibitors it won’t prompt me to order one. If I tell it that a patient has limited life expectancy it will not prompt me to pursue certain routine health screenings. Thus, I don’t view CDSSs as problematic in and of themselves. The problem arises when physicians don’t consider the whole patient (remember those values and clinical circumstances) in deciding whether or not to follow prompted recommendations.
Greenhalgh has made great points about what happens when good ideas are hijacked and distorted for secondary gain but EBM is not to blame. Victor Montori (@VMontori) said it best in a Tweet to me:
“EBM principles are not in crisis, but corruption of healthcare has oft hidden behind the e-b moniker. EBM helps uncover it“.
In this installation I want to jump ahead in Greenhalgh’s paper to address her last cause of the EBM crisis: “Poor fit for multimorbidity“. Not to worry, I will come back in a future post to cover the remaining “problems” of EBM.
I concur with Greenhalgh that individual studies have limited applicability by themselves in a vacuum to patients with multimorbidity. Guidelines don’t help a they also tend to be single disease focused and developed by single disease -ologists. So is EBM at fault here again? Of course not. EBM skills to the rescue.
The current model of EBM demonstrated below contains 2 important elements: clinical state and circumstances and clinical experience.
Clinical state and circumstances largely refers to the patient’s comorbidities, various other treatments they are receiving, and the clinical setting in which the patient is being seen. Thus, the EBM paradigm is specifically designed to deal with multimorbidity. Clinical expertise is used to discern what impact other comorbidities have on the current clinical question under consideration. and, along with the clinical state/circumstance, helps us decide how to apply a narrowly focused study or guideline in a multimorbid patient. Is this ideal? No. It would be nice if we had studies that included patients with multiple common diseases but we have to treat patients with the best available evidence that we have.
Greenhalgh and colleagues report that the “second aspect of evidence based medicine’s crisis… is the sheer volume of evidence available”. EBM is not the purveyor of what is studied and published. EBM is a set of skills to effectively locate, evaluate, and apply the best available evidence. For much of what we do there is actually a paucity of research data answering clinically relevant questions (despite there being alot of studies- which gets back to her first complaint about distortion of the evidence brand. See part 1 of this series). I teach my students and housestaff to follow the Haynes’ 6S hierarchy when trying to answer clinical questions. As much of the hierarchy is preappraised literature someone else has had to deal with the “sheer volume of evidence”. Many clinical questions can be answered at the top of the pyramid.
I concur with Greenhalgh that guidelines are out of control. I have written on this previously. We don’t need multiple guidelines on the same topic, often with conflicting recommendations. I believe that we would be better off with central control of guideline development under the auspices of an agency like AHRQ or the Institute of Medicine. It would be much easier to produce trustworthy guidelines and guidelines on topics for which we truly need guidance. (Really American Academy of Otolaryngology….do we need a guideline on ear wax removal?) It can be done. AHCPR previously made great guidelines on important topics. Unfortunately we will probably never go back to the good ole days. Guidelines are big business now, with specialty societies staking out their territory and government and companies bastardizing them into myriad performance measures.
Trisha Greenhalgh and colleagues wrote an opinion piece in BMJ recently lamenting (or perhaps exalting) that the EBM movement is in crisis for a variety of reasons. I don’t agree with some of the paper and I will outline in a series of posts why I disagree.
When most people complain about EBM or discuss its shortcomings they usually are not basing their arguments on the current definition of EBM. They use the original definition of EBM in which EBM was defined as the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. This definition evolved to “the integration of best research evidence with clinical expertise and patient values. Our model acknowledges that patients’ preferences rather than clinicians’ preferences should be considered first whenever it is possible to do so“.
The circles in this diagram are ordered based on importance- with patient preferences and actions being most important and research evidence being the least important when practicing EBM. You can see that clinical expertise is used to tie it all together and decide on what should be done, not what could be done.
Back to the Greenhalgh paper. Her first argument is that there has been distortion of the evidence brand. I agree. It seems everyone wants to add the “evidence based” moniker to their product. But she argues beyond just a labeling problem. She argues that the drug and medical device industry is determining our knowledge because they fund so many studies. Is this the fault of EBM? Or should funding agencies like the NIH and regulatory agencies like the FDA be to blame? I think the latter. Industry will always be the main funder of studying their product and they should be. They should bear the cost of getting product to market. That is their focus. To suggest they shouldn’t want to make profit is just ridiculous.
The problem arises in what the FDA (and equivalent agencies in other countries) allows pharma to do. Greenhalgh points out the gamesmanship that pharma plays when studying their drug to get the outcomes they desire. I totally agree with what she points out. Ample research proves her points. But it’s not EBM’s fault. The FDA should demand properly conducted trials with hard clinical outcomes be the standard for drug approval. Companies would do this if they had to to get drug to the market. I also blame journal editors who publish these subpar studies. Why do they? To keep advertising dollars? The FDA should also demand that any study done on a drug be registered and be freely available and published somewhere easily accessible (maybe clinical trials.gov). Those with adequate clinical and EBM skills should be able to detect when pharma is manipulating drug dosages, using surrogate endpoints, or overpowering a trial to detect clinically insignificant results. I look at this as a positive for continuing to train medical students and doctors in these skills.
Research has shown that industry funded studies overestimate the benefits of their drugs by maybe 20-30%. A simple way to deal with this is to take any result from an industry funded study and to reduce it by 20-30%. If the findings remain clinically meaningful then use the drug or device.
I agree with Greenhalgh that current methods to assess study biases are outdated. The Users’ Guides served their purpose but need to be redone to detect the subtle gamesmanship going on in studies. Future and current clinicians need to be trained to detect these subtle biases. Alternatively, why can’t journals have commentaries about every article similar to what BMJ Evidence Based Medicine and ACP Journal Club do. This could then be used to educate journal users on these issues and put the results of studies into perspective.
I am preparing for a talk on the controversy surrounding JNC-8 and came across a post on KevinMD.com by an author of a Cochrane systematic review that aimed to quantify the effects of antihypertensive drug therapy on mortality and morbidity in adults with mild hypertension (systolic blood pressure (BP) 140-159 mmHg and/or diastolic BP 90-99 mmHg) and without cardiovascular disease. This is an important endeavor because the majority of people we consider treating for mild hypertension have no underlying cardiovascular disease.
David Cundiff, MD in his KevinMD.com post made this statement:
The JNC-8 authors simply ignored a systematic review that I co-authored in the Cochrane Database of Systematic Reviews that found no evidence supporting drug treatment for patients of any age with mild hypertension (SBP: 140-159 and/or DBP 90-99) and no previous cardiovascular disease, diabetes, or renal disease (i.e., low risk).
Let’s see if you agree with his assessment of the findings of his systematic review.
As is typical for a Cochrane review the methods are impeccable so we don’t need to critically appraise the review and can review the results. The following images are figures from the review. Examine them and then I will discuss my take on the results.
Coronary Heart Disease results
If you just look at the summary point estimates (black diamonds) you would conclude the treatment of mild hypertension in adults without cardiovascular disease has no effect on mortality, stroke and coronary heart disease but greatly increases withdrawal from the study due to adverse effects. But you are a smarter audience than this. The real crux is in the studies listed and examination of the confidence intervals.
Lets examine stroke closely. 3 studies were included that examined the treatment of mild hypertension on stroke outcomes. Two of the studies had no stroke outcomes at all. The majority of the data came from one study. The point estimate of effect was in fact a reduction of stroke by 49% but the confidence interval included 1.0 so not statistically significant. But the confidence interval ranged from 0.24-1.08- a potential 76% reduction in stroke up to an 8% increase. I would argue that a clinically important effect (stroke reduction) is very possible and had the studies been higher powered we would have seen a statistically significant reduction also. I think to suggest no effect on stroke is misleading. The same can be said for mortality.
Finally, what about withdrawals due to adverse effects. Only 1 study provided any data. It has an impressive risk ratio of 4.80 (almost 5 fold increased risk of stopping the drugs due to adverse effects). But the absolute risk increase is only 9% (NNH 11). We are not told what these adverse effects are to know if they were clinically worrisome or just nuisances for patients.
So, I don’t agree with Dr. Cundiff’s assessment that there is no evidence supporting treatment. I think the evidence is weak but there is no strong evidence to say we shouldn’t treat mild hypertension. The confidence intervals include clinically important benefits to patients. More studies are needed but will not be forthcoming. Observational data supports treating this group of patients and may have to be relied upon in making clinical recommendations.
Dr. La Rochelle published an article in BMJ EBM this month with a very useful figure in it (see below). It is useful because it can help our learners (and ourselves) remember the relationship between the type of evidence and its believability/trustworthiness.
Lets work through this figure. The upright triangle should be familiar to EBM aficionados as it is the typical hierarchy triangle of study designs, with lower quality evidence at the bottom and highest quality at the top (assuming, of course, that the studies were conducted properly). The “Risk of Bias” arrow next to this upright triangle reflects the quality statement I just made. Case reports and case series, because they have no comparator group and aren’t systematically selected are at very high risk of bias. A large RCT or systematic review of RCTs is at the lowest risk of bias.
The inverted triangle on the left reflects possible study effects, with the width of the corresponding area of the triangle (as well as the “Frequency of Potential Clinically relevant observable effect arrow) representing the prevalence of that effect. Thus, very dramatic, treatment altering effects are rare (bottom of triangle, very narrow). Conversely, small effects are fairly common (top of triangle, widest part).
One way to use this diagram in teaching is to consider the study design you would choose (or look for) based on the anticipated magnitude of effect. Thus, if you are trying to detect a small effect you will need a large study that is methodologically sound. Remember bias is a systematic error in a study that makes the findings of the study depart from the truth. Small effects seen in studies lower down the upright pyramid are potentially biased (ie not true). If you anticipate very large effects then observational studies or small RCTs might be just fine.
An alternative way to use this diagram with learners is to temper the findings of a study. If a small effect is seen in a small, lower quality study they should be taught to question that finding as likely departing from the truth. Don’t change clinical practice based on it, but await another study. A very large effect, even in a lower quality study, is likely true but maybe not as dramatic as it seems (ie reduce the effect by 20-30%).
I applaud Dr. La Rochelle for developing a figure which explains these relationships so well.