Category Archives: Antimicrobial Resistance

“The dark art of antibiotic resistance surveillance”

This post is best read with a glass of wine…

As a profession, I think we are really not very good at measuring antibiotic resistance/antibiotic susceptibility patterns…

We are very happy to proclaim at the start of presentations “Antibiotic resistance is increasing”, or “In the era of increasing antibiotic resistance.” without providing any data to support this claim.

We need to move away from this type of talk. We are after all, scientists, not politicians.

However antimicrobial resistance surveillance is deceptively difficult. Here are a few reasons why high quality surveillance data is hard work, and requires a lot of thought and planning…

  • It is actually the trends that are critical:- There is a big difference between providing an annual antibiogram to clinicians, and presenting graphs which show changes in antimicrobial susceptibility over time. For example a GP looks at an annual antibiogram provided by the microbiology laboratory and sees that organism X has a resistance rate to antibiotic Y of 10%. Doesn’t sound too bad and certainly a viable treatment option. But if we knew that the resistance rate was 5% last year and 2% the year before that, then we have a problem. It seems obvious, but antimicrobial resistance surveillance is all about trends, not snapshots.
  • Too many permutations:-  There are more than 50 different commercially available antibiotics, and many hundreds of microrganisms identifiable on MALDI-TOF. So the number of antibiotic:microbe combinations is well in to the thousands. So which ones should be measured? The obvious ones are those that are commonly encountered and used, e.g susceptibility of E. coli to trimethoprim, ones that are clinically very important, e.g. susceptibility of Streptoccus pneumoniae to penicillin, or those of great public health importance, e.g. susceptibility of E. coli to meropenem. The ones to be avoided is where the combination falls outside these groups, particularly those where the numbers seen are insufficent to get meaningful data. e.g. Selenomonas spp. susceptibility to ciprofloxacin.  The important thing here is to decide on the microbe:antibiotic combinations to be measured before you start your surveillance program. Otherwise the data is open to exploitation, with people picking antimicrobial resistance surveillance data to suit their particular agenda. e.g. Antibiotic resistance is increasing because Microbe A is becoming more resistant to antibiotic B, and ignore the fact that Microbe C is becoming less resistant to antimicrobial D…
  • Different Definitions:- Because there are different antimicrobial testing standards out there, e.g. CLSI, EUCAST, CDS, etc., one person’s definition of resistant may not be the same as anothers… It is very important to ensure everyone has the same “definitions” of what is resistant and what is not resistant, before you even start. Otherwise you are on a hiding to nothing…
  • Politics:- Everybody has their own wishes and desires, and it is no different when it comes to measuring antibiotic resistance surveillance. Everybody wants to look at different antibiotic:microbe combinations, use different testing methodologies, present the data differently. This can cause problems with not only the accuracy of the data, but also in getting any surveillance data at all, when multiple laboratories are required to work together. When antibiotic resistance data requires the political co-operation of different countries then the difficulties move onto a whole new level altogether. Despite there being a willingness to work together, getting multi-national agreement on surveillance is a monumental task.
  • The goalposts get moved. Every so often the breakpoints get changed, for various reasons, so that an isolate that was once susceptible can become resistant (on paper), and vice versa. This is why using MIC values for surveillance purposes is so important, as it is an objective measurement which has no interpretation applied to it. This facilitates the acquisition of accurate surveillance data over many years.
  • Memory is erased. Sometimes when the laboratory information sytem (LIS) in a microbiology laoratory gets changed, a lot of the historical susceptibility data can get lost, either because it is not compatible with the new system, or not thought to be important enough to keep. Although electronic storage of laboratory data has been around for at least 20 years, as far as I am aware many microbiology laboratories do not have 20 years of data, for exactly this reason. It is a very important point to consider when considering a change of LIS.
  • Biases:- So many things can lead to bias in the surveillance data… Participation bias, sampling bias, patient cohort bias, testing bias, etc.. The list is virtually endless. All these things need to be considered and corrected for as best as possible when performing antimicrobial resistance surveillance.

So it is not easy, by any stretch of the imagination.

Good antimicrobial stewardship programmes should be based on having sound, standardised and objective baseline antimicrobial resistance data against which any interventions can be audited.

The other big area of surveillance which is essential to antimicrobial stewardship programmes is antimicrobial usage data. This data goes hand in hand with antimicrobial resistance surveillance.

Although it’s easy to talk about increasing antibiotic resistance, it is actually very difficult to measure properly…

Michael

“Too soft, too generous, too nice, and too slow…”

Guidelines for antimicrobial stewardship often include only a cursory mention of the role of the clinical microbiology laboratory, which is a shame, because in my opinion it is one of the key areas where real change to anti-microbial stewardship can be effected. (The other key area is in the writing of sensible narrow spectrum empiric antibiotic policies.)

But we don’t help ourselves…. Speaking generally, I think clinical microbiology laboratories are notoriously bad at antimicrobial stewardship.

Why?

Several reasons actually.

Because we are too soft: We often release antimicrobial susceptibilities from the laboratory even when we have no idea what is going on with the patient. I.e. no clinical details have been provided. Therefore we think nothing of releasing a range of antibiotics to the clinician when we don’t actually know what is wrong with the patient, whether they have an infection, and how severe it is.

Antibiotic susceptibilities should not be released unless the laboratory has reasonable evidence that they are required.

Because we are too generous: We are happy to test a whole range of antibiotics (often up to 20 for the one isolate!), “just in case” one of them might need to be used. This range often includes both narrow spectrum and broad spectrum agents. Probably over 95% of all the susceptibilities that we test and report are never utilised.

We need to dramatically reduce the range of antibiotics that we test for and we need to focus our reporting to the narrowest spectrum antibiotics that we can get away with.

Because we are too nice: We have a low threshold for releasing antibiotic susceptibilities on putative pathogens“. By doing this, we have just given the green light for the clinician to classify a putative pathogen as an actual pathogen, and therefore start/continue antibiotics.

If we have isolated a putative pathogen, let’s keep it putative. Report the organism, and ask the clinician to make a clinical assessment, and then to get back to the laboratory if susceptibilities are required.

Because we are too slow: We are certainly quicker than we used to be, thanks to MALDI-TOF, smart incubators, and increasingly rapid PCR platforms, but we need to be quicker still… We need to get rid of self-congratulatory, retrospective infectious serology testing and channel our test budgets into real-time diagnosis with PCR or similar, and on patients who fulfil well defined clinical criteria for testing. We need to get rapid molecular platforms for STDs into Sexual Health clinics so they are not required to prescribe an antibiotic for everybody who walks through the door. We need to increase Influenza and RSV testing during the winter season to try and reduce unnecessary antibiotic prescribing for viral infections.

Not only do we need to be quicker, we also need to be smarter…

The clinical microbiology laboratory doesn’t score very well in the antimicrobial stewardship report card. We need to be bold and innovative to change things for the better.

But it is entirely up to us…

Michael

Plasmids and Team Players

Let’s say you have a problem at your hospital with carbapenemases.

One of the obvious solutions would be to reduce the use of carbapenems in order to reduce the selection pressure.

However even if you stopped carbapenem usage altogether the carbapenemases would not necessarily disappear…

This is because carbapenemases are often plasmid borne, and there are often antibiotic resistance genes for other antibiotics, e.g. A, B & C sitting on the same plasmid.

As long as the (high) usage of antibiotics A, B & C continued then the selection pressure would favour plasmid retention in the bacterium, and thus allowing persistence of the carbapenemase.

Selection pressure by proxy.

Are we all doomed?

Not necessarily…

A gene expressing one antimicrobial resistance determinant comes at an energy cost to a bacterial cell. Plasmids expressing multiple resistance genes come at even more energy cost to the cell. You can be sure if it did not need the plasmid to ensure its survival, it would be mercilessly dumped, and probably sooner rather than later.

Therefore even a modest reduction in carbapenem usage, along with a reduction in antibiotics A, B & C may go a long way to solving your problem.

Advances in molecular methods and whole genome sequencing over the next decade will mean that it will become much easier to work out exactly which resistance genes are contained in the plasmids circulating in our local hospitals, and anti-microbial stewardship can thus be optimised accordingly.

Sounds space age?

Not really, we just need to be aware that resistant bacteria are very smart in an evolutionary sense, and we need to stay alert, and not give them the niches they are looking for…

Michael

Illustration courtesy of www.biologyfun.blogspot.co.nz