Three wrong reasons to dismiss the azithromycin data

There is a lively discussion over at KevinMD about the azithromycin study in the New England Journal of Medicine, which I blogged about here. A certain ethos has emerged in the comments that bears unpacking. There are 3 distinct points that I am feeling unsetlled by:

#1. As a retrospective cohort study it should be ignored.
This is an intellectually indefensible position in my mind -- it is a study that uses a set of methods developed and validated for this type of data. Yes, it's complex; yes, it's messy; no, it's not to be ignored. The discipline of pharmacoepidemiology relies heavily on observational data. To expect anything more is to indulge in misapprehensions that a). it is feasible to run a RCT to detect such rare signals, and 2). that a RCT like that would give us a definitive answer.

#2. They really had to add a lot of zeros to the denominator to make the numerator seem impressive.
This is a baseless accusation, since 1 million prescriptions is not that difficult to generate. Z-pak has been on the market for over a decade, and according to this article, last year 55 million prescriptions for azithro were handed out in the US. So, just a back-of-the-envelope calculation for excess deaths per year at this rate is well over 2,000. And this is just in one year! So, as a safety signal this is not something to be trivialized.

#3. A concern that this information will keep patients from doctors' offices and delay needed treatment.   
I find this to be rather a hollow concern (though I am sure that the person putting it forward believes it wholeheartedly). As another commenter pointed out, the overuse and misuse of antibiotics is completely out of control! And yes, cardiac deaths from azithromycin are but a small part of the issue, where the elephant in the room is the evolution of resistance. It is not just these latest data that should keep patients as far away as possible from unnecessary healthcare encounters, seeing as how these encounters are the third leading cause of death in the US. Why aren't we worried that this entire monster is keeping patients away? And quite frankly, why isn't it?

So, all in all, I am very glad that Rob Lamberts chose to blog the study, and the discussion has been worthwhile. The comments have really confirmed for me that it is not only the lay public, but also healthcare professionals, who have a hard time interpreting data. And when a study is somewhat challenging, it is generally easier to let our cognitive biases run amok.      

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Why I have a propensity to believe the azithromycin data

Before I take off for the Health Foo Camp in Cambridge, MA, where I will be rubbing elbows with such luminaries as Susannah Fox, Ted Eytan, e-Patient Dave deBronkart, Regina Holliday, Nick Christakis and Nancy Etcoff, I thought I'd say a few words about one of my favorite topics: antibiotics. In particular I want to talk about azithromycin. A lot has been said about it this week already in the wake of the NEJM study (alas, the full paper is behind a pay wall) indicating an increase in the risk of cardiac death among patients exposed to this drug compared to those either not getting antibiotics or those receiving a different class of antimicrobials. And a lot more will be said in the future as the FDA reviews this risk. This is not specifically what I wanted to talk about. I wanted to focus on the methods.

The study was done meticulously, as far as I can see. It was a large Medicaid database from Tennessee (this may present a generalizability problem, of course, though I cannot think of a specific reason why it would). The study design was a retrospective cohort, which, as we already know, sets the study up for all kinds of biases. So, why should we believe anything the study showed? And particularly given many people's distaste for invoking causality from observational data?

There are at least 2 reasons why we should pay attention to these results. The first is that we are talking about the ultimate harm: death. When it comes to harm, my philosophical approach leans in the direction of caution. What this means scientifically is that I accept a much lower threshold for the certainty that the data convey than when it comes to evidence of benefit. This is an extension of the precautionary principle, where the burden of proof of safety now lies with the drug.

But there is a second, possibly more important reason that I am inclined to believe the data. The reason is called succinctly "propensity scoring." This is the technique that the investigators used to adjust away as much as feasible the possibility that factors other than the exposure to the drug caused the observed effect. In my book I briefly discuss propensity scoring in Chapter 21. And here is what I say:

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, describing 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. We take advantage of the presence of some of these characteristics also in people who are non-smokers to yield a similar propensity score in the absence of this exposure. In turn, the outcome of interest can be adjusted in several ways for the propensity for smoking. One common way is to match smokers to non-smokers based on the same (or similar) propensity scores and then examine their respective outcomes. This allows us to understand the independent impact of smoking on, say, the development of coronary artery disease. 

And if you are able to access Table 1 of the paper, you will see that their propensity matching was spectacularly successful. So, although it does not eliminate the possibility that something unobserved or unmeasured is causing this increase in deaths, the meticulous methods used lower the probability of this.

One final word about azithromycin. There are data that suggest that macrolides (the class of drugs that includes erythromycin, clarithromycin and azithromycin) are actually associated with improved outcomes in the setting of community-acquired pneumonia, or CAP. This is why these drugs are in the CAP treatment guideline. The point is that again, as in everything, the benefit of using azithromycin in any individual case will have to be weighed against this newly-identified risk.

  

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