The practice of medicine changes slowly, at least more slowly than science changes our understanding of disease. There are some good reasons for this, and some bad. The good reasons are that science does not always proceed smoothly from lesser to greater understanding. Partial understanding of complex processes - and few are more complex than health and disease - can lead to actions and recommendations that are more harmful than helpful. One does not have to look far for examples: the classification of homosexuality as a disease, the use of general anesthesia during childbirth (my mother had to physically fight this off), and any number of misguided dietary recommendations. In many cases, doctors and their patients are well-served by a conservative and skeptical attitude toward modernization of medical practices. If a change is not clearly going to lead to better outcomes, then perhaps it is best to wait before adopting it. Scientists are free to proclaim the beauty and elegance of their new findings without having to deal with any messy unexpected consequences. Doctors have no such luxury.
Bad reasons to resist change can be described in many ways, but mostly comes down to ego and money. No one like to be told that they way they have been doing things is wrong. No one likes it, and some like it so little that they refuse to see the obvious. This has been the state of play in antibiotic prescribing practices for the last decade or so. Doctors, for the most part, have not treated patients; they have not treated specific pathogens; instead they have treated signs and symptoms. Urinary tract infection? Prescribe ciprofloxacin - even though 30% of E coli UTIs are resistant to fluoroquinolines. Child with a fever? Time for Augmentin. Respiratory tract infection? A Z-pack should take care of that. The names of the antibiotics have changed over the decades, but the approach has not: a certain set of symptoms triggers a corresponding prescription. No microbiological data are involved.
This system was unavoidable when it took 2-3 days to get a microbiology result. No one, doctor or patient, should wait that long for a treatment that is well-tolerated and highly likely to be effective. But rapid tests for microbiology have been available for close to a decade now. These tests certainly have their limitations, and they are more pricey than streaking bugs on a Petri dish and waiting. However, most doctors are very comfortable with the practice of evidence-free (empiric) prescription, and are not demanding data from rapid tests. If the doctors are not demanding rapid test results, the clinical microbiology lab is very unlikely to expend the time and money needed to provide them. The weak sales of existing rapid tests starves their developers of the capital needed to create better tests, creating a death spiral for their companies.
There are signs that this invidious dynamic may be beginning to change. Cohen et al recently published “A multifaceted ‘omics’ approach for addressing the challenge of antimicrobial resistance”, describing some of the changes that new technology is poised to make in the treatment of infectious disease. They include a wonderful infographic that succinctly summarizes the causes, challenges and costs of resistance:
Some of their predictions and recommendations for the use of “omics” technology are overkill: phenotypic testing of susceptibility and resistance will always be more reliable than molecular methods such as proteomic profiling. But that’s fine. These issues will work them selves out in due time.
The important point of the paper is not any specific recommendation or prediction. The important part is its emphasis on data-driven treatment of infectious diseases, as part of the “TAILORED-treatment” research program. Ultimately it doesn’t matter whether individualized treatment data comes from PCR, or mass-spec, or accelerated culture methods. All of these data can be used to improve public health by enabling the prudent use of antibiotics, and to improve patient health by providing the most effective treatment.
These data are sometimes available now, and will become more and more available as the technology gets better. The question is, how hard will it be to get doctors to use the data? Let’s hope that this generation of doctors is more open to positive change than their antecedents.
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