“Cohort Bias”

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…

Michael

“Permission microbiology”

One of the great things about having your own microbiology blog is that you don’t need to ask anybody for permission. You can write about whatever you want, even if it is only remotely related to microbiology! You have no deadlines to meet. If you want to post three articles in a day, you can. If you want to take a break for a couple of months, no problem.

Even though you don’t need permission, you do need to be ethically and professionally responsible for what you put out there into the ether.

Permission-no, responsibility-yes.

I have never been very good at asking for permission. This is probably due to the fact that I have a somewhat rebellious nature, and a healthy disrespect for authority. I have an inherent dislike of my personal agenda being at the mercy of someone else! I have always preferred begging for forgiveness than asking for permission.

Of course, sometimes you have to ask for permission. On the occasions where asking for permission is unavoidable, then the way you ask for it is extremely important in determining the chances of success…

I.e. “I am planning to do X & Y. Please let me know if there is any reasonable objection to this” is much preferable to “I am hoping to do X & Y. Is this ok with you??”

There is a subtle but critically important difference.

Within the practice of microbiology, there are lots of things you don’t need permission for… You don’t need permission to prepare a presentation for your colleagues, write a journal article, or even write a book. You don’t need permission to question a dubious result or a dodgy methodology, or to suggest a new idea. You don’t need permission to ask for a pay rise, a promotion, or to apply for a new job.

Permission is often something we wait for when it isn’t really needed…

Michael

“The uncertainty of certainty”

There is one thing certain in the microbiology laboratory, that the results will be uncertain. This has nothing to do of course with laboratory systems or the competency of staff members. Just an acceptance that there is no such thing as a certain result…

The other thing to note is that the degree of certainty of results will vary between different tests, not only for separate tests but even for multiple tests contained in the one assay, e.g. any multiplex PCR.

Take for example a multiplex respiratory PCR, containing 24 or so different targets. (Most labs will “demand manage” such expensive assays, allowing them only for immunocompromised patients or the seriously ill. Nevertheless, such assays are becoming increasingly popular.)

In a multiplex respiratory assay, a positive result for rhinovirus is almost certainly going to have a greater chance of being “the genuine article” than a positive result for bocavirus.

This is because each individual target pathogen has a different positive predictive value (PPV), based on both its specificity and its relative prevalence in the tested population. As a result, positive predictive values for individual pathogens within a multiplex can, and do, vary greatly.

But how do we relate such information to the clinicians? Quoting the calculated PPV for each target in a multiplex would make for a long and complex laboratory report. I would not go there… It is probably best to use an appropriate comment for certain results. I.e. “Bocavirus is uncommonly seen in population x, therefore the positive predictive value of this result may be sub-optimal. Close clinical correlation is required.”

Of course, clinicians can increase the degree of certainty by clarifying the “pre-test probability”. I.e. A positive bocavirus result in a 6 month old during the winter season is much more likely to represent a true result than a positive bocavirus result in an adult during the summer season.

With multiplex PCRs, sometimes you are “forced” to perform a test, when it would be better not to know…

Clinicians, in general,  tend to believe that all laboratory results are certain, until we produce one that is very clearly wrong! After that, they will believe all results are uncertain until that trust is rebuilt over time.

To understand certainty of testing, you first of all need to understand the laws of probability. All a laboratory result ever does is convert pre-test probability of disease X into post-test probability. 

It neither confirms nor excludes…

Michael