By Amari Henderson MLS(ASCP), MBA
When introducing a new lot of quality control (QC) material in the clinical laboratory, it is crucial to establish new control limits to ensure the accuracy and reliability of your assays. This process involves comparing the new QC lot with the current one through a crossover study. Below, we’ll break down this process and explain key statistical concepts like mean, standard deviation (SD), and coefficient of variation (CV) to help you confidently perform QC crossover studies.
Why Perform a QC Crossover Study?
When transitioning to a new lot of QC material, it’s essential to ensure that the new lot performs consistently with the current lot. This ensures continuity in your lab’s quality control and prevents unexpected shifts in assay performance.
Step 1: Parallel Testing
Start by testing the new QC lot alongside the current QC lot. This should be done for at least ten consecutive days, with one measurement taken each day. This side-by-side testing allows you to directly compare the performance of the two lots under the same conditions.
Step 2: Calculate the Mean for the New QC Lot
After you’ve collected at least 10 data points, calculate the mean (average) value for the new QC lot. The mean is your data set’s central value and represents the QC material’s typical performance.
Understanding the Mean
- Mean: The mean is calculated by adding all the data points and dividing by the number of data points. It represents the average value and is a measure of central tendency. In quality control, the mean indicates the expected value of the QC material.
For example, if your ten measurements for the new QC lot are 98, 100, 101, 99, 100, 98, 99, 100, 101, and 100, the mean would be:
Step 3: Use the Current CV to Calculate the New SD
Next, use the coefficient of variation (CV) from the current QC lot to calculate the standard deviation (SD) for the new lot. The CV measures the relative variability in your data and is expressed as a percentage.
Understanding SD and CV
- Standard Deviation (SD): SD measures how the values in a data set are spread around the mean. A smaller SD indicates that the data points are close to the mean, while a larger SD suggests more variability.
- Coefficient of Variation (CV): The CV is calculated by dividing the SD by the mean and multiplying by 100 to get a percentage. It normalizes the variability, making comparing variability across different data sets with different means easier.
To calculate the new SD for the QC lot, you can use the formula:
For example, if the current QC lot has a CV of 2% and the mean of the new QC lot is 99.6 mg/dL:
Step 4: Establish Control Limits and Monitor
After calculating the mean and SD for the new QC lot, establish the control limits, which typically are set at ±2 SD from the mean. This will help you determine if future measurements are within an acceptable range. It’s important to periodically re-evaluate the mean and SD over the following months, especially as more data becomes available, to ensure the ongoing stability of the new QC lot.
Updating Guidelines and Data Point Considerations
The CLSI guidelines have evolved. Historically, at least 20 data points were recommended for establishing a new QC lot’s mean and SD. However, updated guidance from 2016 now allows for a minimum of 10 data points to calculate the mean, provided there is a stable history of QC data from similar formulations or target concentrations. This update facilitates quicker transitions between QC lots without compromising quality.
Final Thoughts
Performing a QC crossover study is essential to maintaining the accuracy and reliability of clinical assays. By following these steps and understanding the key statistical measures involved, you can ensure that your laboratory continues to produce high-quality results, even as QC materials change. Regularly revisiting these calculations and monitoring QC performance over time is critical to upholding your lab’s quality standards.
References
- Clinical and Laboratory Standards Institute (CLSI). “Statistical Quality Control for Quantitative Measurement Procedures: Principles and Definitions; Approved Guideline—Fourth Edition.” CLSI document C24-Ed4, 2016.
- LabCE. Mean and Standard Deviation. Available at: https://www.labce.com/spg113775_mean_and_standard_deviation.aspx
3 thoughts on “Mastering QC Crossover Studies: A Step-by-Step Guide for Medical Laboratory Scientists”
A crossover study should be performed before current QC materials are used. Again, before test sites runs out QC materials a crossover study should be run on current lot of QC materials.
hello
with 10 points to mean sd cv.
which rules of quality control i can use?
if westgard which rules i can use?
thank you
Hi Alexandre!
Are there specific guidelines or standards your laboratory must follow (e.g. CLSI, ISO, CAP) that constrain which rules you can use?
If you have 10 QC results from a 10-day crossover, I recommend using those to set provisional mean/SD and ±3 SD limits. During this provisional phase, use 1-3s as your main reject rule; treat 1-2s as a warning only. If you’re running two control levels per day, also apply 2-2s and R-4s (within-run). Once you accumulate ≥20–40 points, recalculate your mean/SD and adjust your Westgard rules. Use minimal but meaningful rules until limits stabilize. It also matters which analytes you are testing. Certain rule sets can be tailored for specific tests.