“Bias in the diagnostic microbiology laboratory”

We are all fundamentally flawed as humans. We just have to do the best we can given our limitations.

I attended an interesting clinical Grand Round at my local hospital last week. Whilst the case presented was intriguing, it was the presenters’ focus on the different types of cognitive bias which are seen in clinical medicine which really caught my attention. 

It got me thinking… Do the same types of bias apply when reading and interpreting microbial cultures on agar plates?

The answer is of course yes.

Below are examples of the main types of cognitive bias one might be subject to when reading agar plates:

  • Confirmation Bias – Scientists may interpret bacterial growth in a way that confirms their expectations or prior hypotheses. For example, if they expect a certain antibiotic to inhibit growth, they might unconsciously downplay colonies that appear resistant.

  • Anchoring Bias – The first observation or previous experience can heavily influence interpretation. If a scientist has seen a particular growth pattern before, such as satellitism of Haemophilus influenzae around Staphylococcus aureus they might assume it’s the same species or reaction without fully considering other possibilities. 

  • Availability Heuristic – The tendency to rely on readily available examples in memory. If a scientist recently encountered an unusual bacterial isolate, they might overestimate its likelihood when analysing new plates, leading to misidentification.

  • Observer-Expectancy Effect – The scientist’s expectations may subtly influence how they interpret ambiguous results. For instance, if they believe a sample contains Streptococcus pneumoniae, they might unconsciously interpret the MALDI-TOF result as such, and ignore the possibility of Streptococcus mitis.

  • Hindsight Bias – After identifying bacterial species via additional testing, e.g. MALDI-TOF, scientists might believe the identification was more obvious than it actually was when first observing the plate, leading to overconfidence in future interpretations.

…and then of course there is the classical clinical judgement bias, that of “Premature Closure” where an “easy” or quick diagnosis is made, and further investigations into a more challenging/important secondary diagnosis are withheld because of this. It happens not infrequently in clinical medicine. An example of this in the microbiology laboratory might be a patient with crystals in a synovial fluid sample leading to a diagnosis of gout, where sufficient duration of culture was not performed to pick up the secondary diagnosis of septic arthritis!

Now that we can simply google the key biochemical reactions of E. coli, maybe cognitive bias is the sort of thing we should be teaching microbiology students, so that they are aware of the sub-conscious ways we can slip up when reading agar plates, no matter how good our intentions… A nice exam question perhaps!

Michael

 

 

“Kiestra TLA and the impending Artificial Intelligence revolution”

We are now into our 10th year of having Kiestra TLA at the laboratory where I work in New Zealand. I think it is fair to say that once you have worked in a laboratory with bacterial culture automation (i.e. Kiestra TLA, WASPLab) in place, you would never go back! We certainly don’t intend to.

I am a firm believer in optimising the quality of results generated by the microbiology lab. From a quality perspective, the advantages of automated bacterial culture systems over traditional manual-based methodologies are very impressive.

Here are ten important benefits in terms of quality that result from having a Kiestra TLA in place:

  • Improved Standardization – Automates streaking, incubation, and imaging, reducing variability between technicians and ensuring consistent results.
  • Enhanced Sample Traceability – Uses barcoding and digital tracking to prevent sample mix-ups and ensure a complete audit trail.
  • Optimized Culture Conditions – Automated incubation ensures optimal temperature and humidity, leading to better microbial growth and more reliable colony morphology.
  • Higher Reproducibility – Robotics ensure that plating and streaking techniques are performed identically every time, minimizing human error.
  • Faster Turnaround Times – Automation accelerates the workflow by processing and incubating samples continuously, leading to earlier pathogen detection and reporting.
  • Advanced Digital Imaging – High-resolution imaging captures colony growth at multiple time points, allowing for early detection and remote review without disturbing culture plates.
  • Reduced Contamination Risk – Minimizes human handling of samples, lowering the risk of cross-contamination and false-positive results.
  • Integration with LIS (Laboratory Information System) – Enables seamless data transfer, reducing transcription errors and improving result accuracy.
  • Enhanced Quality Control – Automated processes ensure that each step is performed according to predefined parameters, improving compliance with laboratory standards (e.g., ISO, CLSI).
  • Improved Staff Efficiency and Safety – Reduces manual labor, decreases repetitive strain injuries, and allows microbiologists to focus on complex tasks like interpretation and antimicrobial susceptibility testing.

It is important to note that the list above is Artificial Intelligence (AI) generated. It would take me much, much longer to generate such a list myself! I have however reviewed it and agree with all the points mentioned.

And it is due to the impending AI revolution, that systems such as Kiestra TLA are really going to come into their own over the next 10 years.

The Kiestra TLA system generates thousands of images of cultured agar plates each day, which are ripe for machine learning approaches. AI assisted applications, such as for MRSA identification and identification of urine pathogens are already available on the BD Kiestra platform.

I have no idea what the researchers at BD Kiestra are currently up to (!), but one could envisage that there is a lot of development work going on to further extend these AI-assisted apps into pathogen identification for general wound swabs, sputum samples, etc.

I observe with interest what the Kiestra TLA will be capable of by 2035. One would think that a lot of the routine microbiology culture results will be generated with very little human intervention, leaving the laboratory scientists to focus on the more complex (and interesting) samples.

Undoubtedly, by 2035, we will have new Kiestra TLA hardware in place in our laboratory, but it is in the AI-assisted software where the real revolution is coming…

Michael

 

“Reporting susceptibilities on UTIs, not urinary isolates…”

Urines arrive at diagnostic microbiology laboratories in considerable numbers. My own lab in New Zealand processes a couple of thousand urines a week. A significant proportion of these will have positive cultures. Therefore, the potential for the laboratory to promote good antimicrobial stewardship with respect to urinary tract infection is considerable.

My mantra on this is as follows: “The microbiology lab should never release antibiotic susceptibilities on a positive culture from a urine sample unless there is reasonable evidence accompanying the request that the patient has a UTI.”

The fact that the urine sample has turned up at the microbiology lab is insufficient evidence per se that the patient has a UTI. Urines get sent to microbiology laboratories for all sorts of spurious reasons, see below for a few examples:

  • Urines often get sent “automatically” from acute receiving wards as part of a blanket laboratory screen, where the patient may have a diverse spectrum of symptoms such as chest pain, shortness of breath, collapse, etc.
  • Urines can get sent from Long Term Care Facilities when someone decides to dipstick all their patients and send the urine samples with positive dipsticks to the lab for culture. Yes, it happens, and a lot more often than you might think!
  • Urine from indwelling catheters can get sent when the patient has a blocked catheter, or the catheter bag is cloudy.
  • Urines from patients attending outpatient clinics should also raise a flag. With the exception of urology clinics, patients who attend a pre-planned elective clinic appointment generally do not have an acute UTI. The same principle can apply for patients who are in hospital wards for other reasons.
  • Urines where the clinicians are looking for other tests, i.e. albumin/creatinine ratio, and due to laboratory processes the urine ends up getting cultured as well…

So, my argument is that if a urine sample turns up at the laboratory without any clinical details or with inappropriate clinical details, the lab is under no obligation whatsoever to release antibiotic susceptibilities on any organisms grown. 

The best approach of course is not to process the sample at all unless relevant clinical details are received. I would regard all of the following clinical details as being unacceptable to justify proceeding to urine culture:

  • No clinical details
  • Cloudy urine
  • Concentrated urine
  • Dark urine
  • Smelly urine
  • Urine dipstick urinalysis results only
  • Routine/monitoring/screening urine
  • Fatigue
  • Increased CRP
  • Lots of other non-specific symptoms!

The easy option for the lab of course is just to accept the sample, report the organisms, and the accompanying susceptibilities. However, this is almost certainly not the best way…

Michael