Just Do As I (Vaguely) Say: The Folly of Clinical Practice Guidelines

If you didn't care to know anything about finance, and you hired a financial adviser (paid hourly, not through commissions, of course) you would be happy to have him simply tell you to invest all of your assets into a Vanguard life cycle fund.  But you may then be surprised that a different adviser told one of your contemporaries that the approach was oversimple and that you should have several classes of assets in your portfolio that are not included in the life cycle funds, such as gold or commodities.  In light of the discrepancies, you may conclude that to make the best economic choices for yourself, you need to understand finance and the data upon which the advisers are basing their recommendations.

Making medical decisions optimally is akin to making economic decisions and is founded on a simple framework:  EUT, or Expected Utility Theory.  To determine whether to pursue a course of action versus another one, we add up the benefits of a course multiplied by their probability of accruing (that product is the positive utility of the course of action) and then subtract the product of the costs of the course of action and their probability of accruing (the negative utility).  If utility is positive, we pursue a course of action, and if options are available, we pursue the course with the highest positive utility.  Ideally, anybody helping you navigate such a decision framework would tell you the numbers so you could do the calculus.  Using the finance analogy again, if the adviser told you "Stocks have positive returns.  So do bonds.  Stocks are riskier than bonds" - without any quantification, you may conclude that a portfolio full of bonds is the best course of action - and usually it is not.

I regret to report that that is exactly what clinical practice guideline writers do:  provide summary information without any numerical data to support it, leaving the practitioner with two choices:

1. Just do as the guideline writer says
2. Go figure it out for herself with a primary data search

Once Bitten, Twice Try: Failed Trials of Extubation

“When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.”                                                                                   – Clark’s First Law

It is only fair to follow up my provocative post about a “trial of extubation” by chronicling a case or two that didn’t go as I had hoped.  Reader comments from the prior post described very low re-intubation rates.  As I alluded in that post, decisions regarding extubation represent the classic trade-off between sensitivity and specificity.  If your test for “can breathe spontaneously” has high specificity, you will almost never re-intubate a patient.  But unless the criteria used have correspondingly high sensitivity, patients who can breathe spontaneously will be left on the vent for an extra day or two.  Which you (and your patients) favor, high sensitivity or high specificity (assuming you can’t have both) depends upon the values you ascribe to the various outcomes.  Though these are many, it really comes down to this:  what do you think is worse (or more fearsome), prolonged mechanical ventilation, or reintubation?

What we fear today we may not seem so fearsome in the future.  Surgeons classically struggled with the sensitivity and specificity trade-off in the decision to operate for suspected appendicitis.  “If you never have a negative laparotomy, you’re not operating enough” was the heuristic.  But this was based on the notion that failure to operate on a true appendicitis would lead to serious untoward outcomes.  More recent data suggest that this may not be so, and many of those inflamed appendices could have been treated with antibiotics in lieu of surgery.  This is what I’m suggesting with reintubation.  I don’t think the Epstein odds ratio (~4) of mortality for reintubation from 1996 applies today, at least not in my practice.

The Therapeutic Paradox: What's Right for the Population May Not Be Right for the Patient

Bad for the population, good for me

An article in this week's New York Times called Will This Treatment Help Me?  There's a Statistic for that highlights the disconnect between the risks (and risk reductions) that epidemiologists, researchers, guideline writers, the pharmaceutical industry, and policy wonks think are significant and the risks (and risk reductions) patients intuitively think are significant enough to warrant treatment.

The authors, bloggers at The Incidental Economist, begin the article with a sobering look at the number needed to treat (NNT).  For the primary prevention of myocardial infarction (MI), if 2000 people with a 10% or higher risk of MI in the next 10 years take aspirin for 2 years, one MI will be prevented.  1999 people will have gotten no benefit from aspirin, and four will have an MI in spite of taking aspirin.  Aspirin, a very good drug on all accounts, is far from a panacea, and this from a man (me) who takes it in spite of falling far below the risk threshold at which it is recommended.

One problem with NNT is that for patients it is a gratuitous numerical transformation of a simple number that anybody could understand (the absolute risk reduction  - "your risk of stroke is reduced 3% by taking coumadin"), into a more abstract one (the NNT - "if we treat 33 people with coumadin, we prevent one stroke among them") that requires retransformation into examples that people can understand, as shown in pictograms in the NYT article.  A person trying to understand stroke prevention with coumadin could care less about the other 32 people his doctor is treating with coumadin, he is interested in himself.  And his risk is reduced 3%.  So why do we even use the NNT, why not just use ARR?