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.
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…
You can produce sophisticated and comprehensive antimicrobial resistance surveillance data.
You can adhere to the best infection control policies in the country.
You can have a “search and destroy” policy for multi-resistant organisms.
and you can even develop and bring out a new antibiotic every couple of years….
But unless you control antibiotic consumption (usage), you will always be fighting an uphill battle.
In order to control antibiotic consumption you need to know how many antibiotics are being used in the first place.
One of the problems is that antimicrobial resistance surveillance data is produced by microbiologists. Antibiotic consumption data is produced by pharmacists. Antibiotic consumption data even in this day and age can still be difficult to get hold of. Sometimes I wonder if the companies selling the antibiotics to the hospitals have a much better handle on consumption data than the microbiologists do!
Microbiologists and pharmacists need to talk to each other more. It is such a key relationship in the antimicrobial stewardship world.
Antibiotic usage needs to be surveyed and controlled not only at an individual level, but at a national level. Communities and hospitals, humans and animals. It all adds up… Too often I have sat in conferences and seen pretty graphs of antimicrobial resistance data, without complementary antibiotic consumption data to put the resistance data into context. I find it all a bit frustrating…
If reducing antibiotic usage was easy it would already have happened. It’s not easy , and there are good reasons for this. (See this article). This is where objective data is key to monitoring and measuring change. Feedback to the “prescriber” is critical.
Every antimicrobial stewardship committee in the world needs to be aware of their consumption data. Otherwise they are simply not doing their job. Surveillance of antibiotic consumption does not seem to get the same profile as resistance data. This is a shame. I would actually argue that it is the more important of the two….