Tag Archives: positive predictive value

“PCR: A Multiplicity of Multiplexes….”

The Science of Microbiology, , ,

PCR multiplexes seem to be all the rage just now….

Here is just a selection of what is currently available in New Zealand:

  • CSF Multiplex: HSV, VZV, Enterovirus, Parechovirus, N. meningitidis, S. pneumoniae
  • Respiratory Virus Multiplex: Influenza A&B, CMV, Adenovirus, RSV, hMPV, Rhinovirus, Coronavirus, Parainfluenza 1-3, Bocavirus.
  • Atypical Pneumonia Multiplex: Legionella, Mycoplasma pneumoniae, Chlamydia, Pneumocystis, Bordetella pertussis & parapertussis.
  • Enteric Virus Multiplex: Norovirus, Rotavirus, Adenovirus, Astrovirus.

However there are some downsides to multiplex PCRs, both clinical and technical. These are as follows:

  •  Cost: The clinician may not want to test for all the assays within a multiplex PCR, therefore the cost may be more than with other individual assays that are required. For example, it is usually easy to differentiate between a viral and a bacterial meningitis based on initial CSF findings. However if the “CSF multiplex” includes both bacteria and viruses, then it may lead to unnecessary cost as well as problems with positive predictive value as described below.
  • Expertise: Carrying out a multiplex PCR still requires a reasonable amount of expertise, particularly if the reagents are being prepared “in-house” The expertise level increases further when troubleshooting is required.
  • Controls: Controlling each assay within the multiplex.
  • Test Volumes: Because of the amount of controls required per batch, significant numbers of tests are often required to make it cost-effective. Therefore may restrict some multiplexes to the larger centres.
  • Optimisation: Optimising each assay and avoiding competitive inhibition between the different reagents.
  • Positive predictive Value: If you have 5 tests in a multiplex PCR, then it is likely that at least one of these tests has a very low pre-test probability making interpretation of positive results difficult. For example, during the Influenza season, it may be prudent to test for Influenza first and then worry about other diagnoses if this test is negative.
  • More than one positive result: For example if you are doing a multiplex PCR with 7 or 8 respiratory viruses, it is not uncommon for 2 or even 3 assays to be positive. You then need to decide which one is causing the problem….
  • Only diagnoses what is tested for in the multiplex: I.e. It is not a catch-all method.

 Multiplex PCR can clearly be very useful in some situations. However it is important to be aware of the limitations as described above and have other testing options available. Otherwise the skill of utilising laboratory tests in a cost effective and clinically appropriate manner will be lost….

Michael

For a really simple walk through the basics of the PCR reaction, check out this website. I will go into a bit more detail next week on detection of PCR product, Real-Time PCR etc.

 

“Measuring the odds….”

The Science of Microbiology, , ,

“Medicine is a science of uncertainty and an art of probability.” William Osler

This article is an extension of a previous article on sensitivity, specificity, and positive predictive value, this time looking at pre- and post-test probability.

Take the following hypothetical scenario:

A 15 yr old teenager and an 85 yr old Rest Home resident both present to their doctor with a sore throat and cervical lymphadenopathy.

An Epstein Barr Virus (EBV) screening test (with sensitivity and specificity both at 97%) is positive in both patients.

Without knowing any other information, what is the likelihood of each person having Infectious Mononucleosis/Glandular Fever due to EBV?

Let’s look at it before the EBV test is performed. The prevalence of glandular fever in 15 year olds with sore throats is many times greater than in 85 year olds. Thus the chance of the 15 yr old having glandular fever is much, much higher than the 85 yr old. (This is called pre-test probability).

After the test result is known, the post-test probability of glandular fever is extremely high in the teenager, but still relatively low in the 85 yr old, as the very low prevalence in this age cohort will lower the positive predictive value of the test. Therefore the chances of the EBV result being a false positive in the 85 year old is relatively high.

In conclusion, exactly the same result in different patients needs to be interpreted differently.

So what is the lesson from this?

I suspect that a lot of laboratory users don’t really think enough about pre and post-test probability when they see the laboratory result. They may well take the result at face value and diagnose the patient on the basis of it. (I have seen this happen many many times…)

It is our job in the laboratory to convince the requestors that on the basis of the factors described above, the results are not always perfect, however much we would like them to be……

Michael

p.s. I have added a quick powerpoint on the basics of Norovirus Infection to the website.

“The Power of Prevalence..”

The Science of Microbiology, ,

A serious disease is not much fun for the patient, their doctor, or even the government funding the treatment. However having a lot of the one disease in a population can actually favour laboratarians.

Let me explain….

Let’s say you have a test for Disease X which has 99% sensitivity (the proportion of patients with the disease who have a positive result) and 99% specificity (the proportion of people without the disease who have a negative result). You then apply that test to a population which has a prevalence of Disease X of 1%. A hypothetical 2×2 table using these figures on a 10,000 population is demonstrated below.

  Disease X No Disease X  
+ve test 99 99  
-ve test 1 9801  
  100 9900 10000

 Thus the Positive Predictive Value (PPV) of the test (the proportion with a positive result who truly have the disease) is 50% (99/198). Half of the positive tests are false positives.

 

Now let’s keep the test sensitivity and specificity the same but increase the prevalence of Disease X to 10%. Here is a 2×2 table modelled on these figures for a 10,000 population.

  Disease X No Disease X  
+ve test 990 90  
-ve test 10 8910  
  1000 9000 10000

With the prevalence increasing to 10% the positive predictive value (PPV) of the test (the proportion with a positive result who truly have the disease) has increased to 91.6%, and the number of false positive results has decreased to 8.4%.

Now if disease X was something like HIV or Gonorrhoea, you would be keen that your laboratory produced as few false positive results as possible (in the interest of your career!). Thus if diagnosing an important disease with low prevalence it is essential you have a test with as high a specificity as possible.

Note that sensitivity and specificity are functions of the test and doesn’t change wherever you use the test. Positive and negative predictive values can only be calculated when you know what population you are applying the test to. Test manufacturers should not be giving you positive and negative predictive values. It is nothing to do with the manufacturers. That is the job of the laboratarians depending on who they are using the test on.

Whichever field of laboratory medicine you are involved with, understanding the concepts of sensitivity, specificity, positive and negative predictive value are critical. If I was an examiner (thankfully not), I wouldn’t care if you knew all the steps of the Krebs cycle, but I would care that you had an understanding of the above….

Michael

 p.s. Here is a link to a powerpoint on this topic produced by the WHO