- Performing quality control (QC) trend analysis when the laboratory mean is not established from the very first day requires a slightly different approach. Here’s how you can approach QC trend analysis in such situations:
- Initial Setup:
- Begin by implementing your testing process and instrument.
- Start running your QC samples alongside patient samples from the very first day of testing.
- Collect Data:
- Run multiple replicates of the QC material for each testing session
- Record the QC results in a structured format, including the date and time of testing.
- Calculate Moving Range (MR):
- Since you don’t have an established mean, you can calculate the moving range (MR) for each set of replicates.
- The MR is the absolute difference between consecutive QC values.
- Calculate the average MR over a defined period. This will give you an idea of the typical variability in your QC measurements.
- Set Initial Acceptance Range:
- Based on the calculated average MR, set an initial acceptance range. This can be a multiple of the average MR (e.g., ±2 or ±3 times the average MR)
- QC values falling within this range can be considered acceptable
- Start Trend Analysis:
- Begin monitoring the QC results over time using the initial acceptance range.
- Plot your QC data on a control chart. The X-axis represents time (days, weeks, etc.), and the Y-axis represents the QC values.
- Add the initial acceptance range as control limits on the chart.
- Observe Trends:
- As you continue testing and collecting data, observe the trends of QC values on the control chart.
- Look for patterns such as consecutive values outside the initial acceptance range, trends upward or downward, or sudden shifts.
- Update Acceptance Range:
- Based on the trends you observe, periodically reassess and update your acceptance range.
- If you notice consistent shifts or trends in the QC values, adjust the acceptance range accordingly.
- Data Accumulation:
- As you accumulate more data over time, you can calculate a preliminary mean and standard deviation of your QC values.
- Use these preliminary statistics to refine your acceptance range and control limits.
- Establishing Mean and Control Limits:
- Over a longer period, as you accumulate sufficient data, you can establish a more stable mean and standard deviation for your QC values.
- Once you have a well-defined mean and standard deviation, update your control limits accordingly.
10 Regular Review:
- Continuously review and analyze QC trends. Update your control limits whenever necessary to reflect the laboratory’s improved understanding of the testing process.
Here are hypothetical examples of data analysis for quality control (QC) trend analysis using moving ranges (MR) and control charts. These examples illustrate how you might analyze QC data over time when the laboratory mean is not established from the beginning
Example 1: Blood Glucose Testing
Let’s say you’re performing blood glucose testing in a laboratory, and you’re using a control material to monitor the accuracy and precision of your results. Here’s how you might analyse the data:
- Data Collection:
- Run the control material replicates every day for the first two weeks
- Record the blood glucose values for each replicate
- Calculate Moving Ranges:
- Calculate the moving range (MR) for each set of consecutive replicates.
- MR1: |Glucose1 – Glucose2|
- MR2: |Glucose2 – Glucose3|
- And so on…
- Calculate Average MR:
- Calculate the average moving range over the two-week period:
- Average MR = (MR1 + MR2 + … + MR14) / 14
- Initial Acceptance Range:
- Set an initial acceptance range as 3 times the average MR.
- Control Chart:
- Plot the blood glucose values on a control chart.
- Add the upper and lower control limits based on the initial acceptance range.
- Trend Analysis:
- Observe the QC values’ trends on the control chart.
- If any values fall outside the control limits or show consistent upward/downward trends, investigate further
Example 2: Molecular Assay
Let’s consider a scenario where you’re running a molecular assay to detect a specific gene mutation. Here’s how you might analyze the data:
- Data Collection:
- Perform the molecular assay on control material replicates every week for a month
- Record the presence or absence of the mutation (0 or 1) for each replicate.
- Calculate Moving Ranges:
Since you’re dealing with binary data (mutation present or absent), calculate the moving range as the absolute difference between consecutive 0s and 1s.
- Calculate Average MR:
- Calculate the average moving range over the four-week period.
- Initial Acceptance Range:
Set an initial acceptance range as 3 times the average MR.
- Control Chart:
- Plot the presence/absence data on a control chart.
- Add the upper and lower control limits based on the initial acceptance range.
- Trend Analysis:
- Monitor the presence/absence trends on the control chart.
- Investigate any patterns such as consecutive values outside the control limits or sustained trends in one direction.
Remember that this approach relies on gradually building your understanding of the testing process and variability over time. As your data accumulates, you’ll be able to refine your QC practices and establish more accurate mean values and control limits. Regular review, documentation, and adjustment of your QC practices will be key to maintaining the quality of your testing process
About the author
Dr. Sambhu Chakraborty is a distinguished consultant in quality accreditation for laboratories and hospitals. With a leadership portfolio that includes directorial roles in two laboratory organizations and a consulting firm, as well as chairmanship in a prominent laboratory organization, Dr. Chakraborty is a respected voice in the field. For further engagement or inquiries, Dr. Chakraborty can be contacted through email at director@iaqmconsultants.com and info@sambhuchakraborty.com. Additional resourcesand contact information are available on his websites, https://www.quality-pathshala.com and https://www.sambhuchakraborty.com, or via WhatsApp at +919830051583