Aug 16, 2017

Are all disease models wrong?

In a recent article titled Eliminating Creatine Kinase–Myocardial Band Testing in Suspected Acute Coronary Syndrome for JAMA Alvin et al. write,

Once the cornerstone of AMI diagnosis, CK-MB has not yet been eliminated from practice despite considerable evidence supporting cTn as the preferred biomarker.8,11 Data published after distribution of the ACC/ESC/AHA3-5 recommendations show the these clinical practice guidelines have not succeeded in refining practice. Specifically, CK-MB is still used in many US clinical pathology laboratories and US EDs.12,13


Despite a longstanding, commonly held physician belief that CK-MB is more useful than cTn for detecting reinfarction, no study has shown that CK-MB levels are superior to cTn in this regard.32,33 Indeed, most data confirm that cTn provides far better estimates because it is less impacted by changes in the amount of marker released with reperfusion.34 Single values correlate closely with infarct size as determined by cardiac magnetic resonance imaging.35 Apple et al36 showed that both biomarker levels rise similarly with reinfarction. The most recent ACC/AHA/ESC guidelines support the use of cTn over CK-MB for diagnosing reinfarction.3-5


Creatine kinase–myocardial band testing provides no incremental value to patient care, and its elimination can lead to millions of healthcare dollars saved without adversely affecting patient care. This is backed by both strong evidence-based guidelines and experiences from multiple institutions.

Diseases for the most part are not directly observed and must be inferred from data that has been collected. These data, usually measured and validated, include but are not limited to signs, symptoms, lab and imaging studies. Although these measurements are not perfect they help and are more reliable than our perception. Medicine along with statisticians use measurement to validate their models, here is what George E. P. Box had to say about selection and testing of models,

In any feedback loop it is, of course, the error signal—for example, the discrepancy between what tentative theory suggests should be so and what practice says is so—that can produce learning. The good scientist must have the flexibility and courage to seek out, recognize, and exploit such errors—especially his own. In particular, using Bacon's analogy, he must not be like Pygmalion and fall in love with his model.


Since all models are wrong the scientist cannot obtain a “correct” one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity.


Since all models are wrong the scientist must be alert to what is importantly wrong. It is inappropriate to be concerned about mice when there are tigers abroad.

If medicine wants the benefit of science and not just its products it must also acquire a scientific attitude. The history of medicine has plenty of examples of how we corrected our false beliefs, I'm sure most involved having a scientific attitude.

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1. You should attempt to re-express your target’s position so clearly, vividly, and fairly that your target says, “Thanks, I wish I’d thought of putting it that way.
2. You should list any points of agreement (especially if they are not matters of general or widespread agreement).
3. You should mention anything you have learned from your target.
4. Only then are you permitted to say so much as a word of rebuttal or criticism.
Daniel Dennett, Intuition pumps and other tools for thinking.

Valid criticism is doing you a favor. - Carl Sagan