For clinical genomics testing laboratories, validation — and to a certain extent, revalidation — is a fact of life. It’s written into the CAP/CLIA regulations.
The regulations say you must validate:
- Any new FDA-cleared test your lab introduces.
- Any modifications you make to an FDA-cleared test.
- Any laboratory-developed (LDT) test not subject to FDA regulation.
In the case of the first one, validation means you can replicate the performance specifications determined by the manufacturer of the test. In the second two, however, validation involves determining the performance specifications yourself (in seven areas, as outlined by CLIA).
How do you go about testing the accuracy of a test your lab purchased or determining the accuracy of a test your lab designed?
According to a CAP checklist, you’re aiming for “agreement between test result and ‘true’ result.” In other words, you must run known negative and known positive controls through your test. CLIA also notes that your lab must “document all activities specified in this section.”
For an expert walkthrough of how one lab developed and validated a cancer hotspot assay, click here.
So far, so good. But as soon as your lab makes a change to a test — adding variants, for example — you have to do the whole thing all over again.
The cost of constant revalidation can add up. It consumes precious time, space, and human resources. It adds to your lab’s workload and decreases your output of tests you get paid for. (Read our recent post on preventing downtime in your clinical genomics testing laboratory.)
Luckily, there are a few ways to streamline your lab’s revalidation process to save your team’s valuable time and resources for more lucrative activities. Here are three tips:
1. Develop Standard Procedures
You shouldn’t have to reinvent the wheel every time you need to revalidate a test. Develop standard procedures and use templates so the process becomes as automatic as possible and you can get new team members up to speed quickly.
Your standard procedures should include:
- A protocol template.
- A report template.
- Dedicated resources for analysis of the data.
2. Use Highly-Multiplexed Controls
To validate that your test works for all the different mutation variants it’s supposed to — and at the level of sensitivity it’s supposed to — you need to generate a large amount of data. Your “homebrew” reference materials may not work for all the variants you require, forcing you to revalidate, and revalidate, and revalidate again as you cycle through different samples.
With highly-multiplexed reference material, you can produce more data, faster. For example, you might be able to generate the same amount of data in 10 runs that would take you 100 runs with remnant patient samples.
3. Use Software to Track Validation Data
Having all your data archived in one easily-accessible place will give you a robust historic data set. This will allow you to compare new tests against the baseline of old tests, increasing the efficiency of your validations and reducing the number of sample runs needed.
There are many different methods labs use to track validation data; everything from hard copy records, to spreadsheets, to laboratory information management systems, to a hodgepodge mixture of all of the above.
What method works best? We compare each one and break down the seven quality control data management must-haves in our recent whitepaper, “2 Tools For Overcoming Your Clinical Lab's Toughest Quality Control Challenges.” Click below for your free copy.
