A lot of antibiotic treatment of infectious diseases is still “empirical” in nature. “Empirical” generally means “based on experience”, so to administer an empirical antibiotic means to give the antibiotic that is most likely to work based on previous experience with that infection.
Antibiotic treatment has traditionally been empirical because waiting for culture and susceptibility results was simply far too slow. Only a generation ago the average turnaround time for a microbiology culture test was 3 days and counting….
A lot of antibiotic treatment is still empirical in nature. If you go to your GP with a sore throat or a urinary tract infection, chances are high you will receive an empirical antibiotic. If you attend your local Sexual Health clinic with symptoms of gonorrhoea, you will likely be getting some empirical ceftriaxone long before the diagnosis is established. If you go to your local hospital with pneumonia, you will likely get a macrolide antibiotic (to cover atypical pathogens) as well as a beta-lactam.
However microbiology labs are improving all the time. We now have the potential in many areas to make empirical treatment redundant for certain infections. Check out the following examples:
- Rapid antigen and PCR tests are now available for the laboratory diagnosis of sore throat, with a turnaround time of minutes to a few hours.
- Bacteriology automation, such as Kiestra TLA, can reduce turnaround times for urine cultures to less than a day, and a day and a half for wound swabs. I suspect a lot of patients with straightforward UTIs and wound infections can wait that long for a result without outcomes being adversely affected.
- Rapid PCR tests for atypical respiratory pathogens (Legionella spp., Mycoplasma pneumoniae, Chlamydia pneumoniae) can mean that macrolide coverage for community acquired pneumonia can be stopped early, or not even started.
- Ultra-fast PCR tests for influenza can prevent any antibiotics being prescribed in patients who present with Upper Respiratory Tract Infection (URTI).
- PCR tests with genotypic antimicrobial susceptibility information (e.g. Neisseria gonorrhoeae, Mycoplasma genitalium) can avoid the use of empirical antibiotics and selection pressure for antibiotic resistance.
Costing silos, resistance to change, and a lack of vision are amongst the main reasons that it takes so long for treatment protocols to move to “directed therapy” wherever possible.
There are still areas where empirical antibiotics will continue to be necessary. The acutely septic patient presenting to hospital is one such example. However large swathes of infections, particularly in the community setting are still managed by empirical antibiotic therapy. This is the way it always has been. However that doesn’t mean it is the way it always should be…
The next big revolution in clinical microbiology labs should be to challenge the dogma of empirical antibiotic treatment. This would be a huge step forward towards counteracting the development of antibiotic resistance.
For several years now, the core empirical treatment for gonorrhoea has been intramuscular ceftriaxone. This wasn’t always the case, but the resistance rates for both penicillin and ciprofloxacin have crept up to levels that meant using them as empirical antibiotics was no longer a satisfactory option.
N. gonorrhoeae is particularly vulnerable to antibiotic resistance, essentially because it has no hiding place.
There are not many viable options left after ceftriaxone, so we end up using ceftriaxone on everybody with gonorrhoea or suspected gonorrhoea. And as a result we are starting to see ceftriaxone resistance…
The solution of course is to avoid using ceftriaxone on every patient for empirical treatment of gonorrhoea.
And this is now becoming achievable with the release of a commercial rapid diagnostic PCR assay, ResistancePlus® GC ,that not only detects the presence of N. gonorrhoeae (using both OPA and PorA targets), but also looks for the mutation conferring ciprofloxacin resistance (GyrA S91 F).
In the patients who have ciprofloxacin susceptible gonorrhoea, this will allow oral ciprofloxacin to be prescribed in a timely fashion, thus allowing the selection pressure of ceftriaxone on N. gonorrhoeae to be reduced.
This is a great example of how good diagnostic stewardship can lead to good antimicrobial stewardship. Hopefully such advances in molecular diagnostics will prevent the rather ugly scenario of “untreatable gonorrhoea”
At my laboratory we only test nasal Staphylococcus aureus susceptibility to mupirocin in the following two circumstances:
- Prior to joint replacement surgery as part of a Staphylococcal decolonisation bundle
- In patients where the clinical details state recurrent skin infections
In the patients about to get their joints replaced, our nasal Staph aureus resistance rate to mupirocin is 3%. Not surprising really. This is a generally older cohort, less likely to suffer from impetigo and skin boils etc., and thus less likely to have been exposed recently to mupirocin.
In the patients who have recurrent skin infections, our nasal Staph aureus resistance rate to mupirocin is 15%. This is not surprising either. This cohort is generally young, and due to their clinical history are much more likely to have been exposed to a lot of mupirocin. As a cohort, they potentially have a lot of physical contact with each other (in kindergartens, in the school playground, on the sports field, in cinemas, backs of cars etc..,) facilitating cross-transmission.
This is a good demonstration of how much antibiotic resistance can vary, depending on what population you are looking at. 3% is markedly different from 15% and management of these different rates might be very different from an antibiotic stewardship point of view.
It also reflects the difficulties in measuring antibiotic resistance and then how to report such results in a meaningful manner.
We like to simplify things, and have just one result regardless of what biases might be at play. Measuring resistance rates is complicated enough due to the sheer number of microbe-antimicrobial combinations that can be permutated. To add another level of complexity by calculating different values for any one microbe-antimicrobial combination is too much for most of us to handle!
But sometimes the difference in values between different population cohorts (as demonstrated above) is just too much to be ignored…