Trend Analysis

Overview of Trend Analysis and Interpretation

Definition: Trend analysis involves the systematic examination of quality control (QC) data over time to identify patterns, trends, and shifts in instrument performance or assay stability
Purpose:
- Early Detection: To detect subtle changes in instrument performance or assay stability before they lead to significant errors
- Predictive Maintenance: To predict when instrument maintenance or reagent replacement is needed
- Process Improvement: To identify areas for improvement in the flow cytometry process
Key Components of Trend Analysis:
- Data Collection
- Data Visualization
- Statistical Analysis
- Interpretation and Action

What Data to Collect:
- QC Values: Record the values of the QC parameters for each run, such as the mean fluorescence intensity (MFI) of control beads, the percentage of cells in specific gates, or the absolute counts of cell populations
- Instrument Settings: Record the instrument settings used for each run, such as laser power, PMT voltages, and compensation values
- Reagent Information: Record the lot numbers and expiration dates of the reagents used for each run
- Environmental Factors: Monitor and record temperature, humidity, and other environmental factors that may affect instrument performance
How to Collect Data:
- Use a LIMS (Laboratory Information Management System): LIMS software can automate the process of data collection and storage
- Use Electronic Spreadsheets: Spreadsheets can be used to manually collect and track QC data
- Ensure Accurate and Consistent Data Entry: Use standardized forms and procedures to minimize errors in data entry
Make sure that you are consistent in data collection

Control Charts:
- A graph that shows the QC values over time, along with upper and lower control limits
- Control limits are typically set at ±2 or ±3 standard deviations from the mean
- Used to identify trends or shifts in instrument performance
Histograms:
- A graph that shows the distribution of QC values
- Used to assess the shape and spread of the data
Scatter Plots:
- A graph that shows the relationship between two QC parameters
- Used to identify correlations or patterns in the data
Run Charts:
- A graph that shows the QC data points run-to-run
- Used to assess if a test is “in control.”
How to Visualize Data:
- Use software tools: GraphPad Prism, Microsoft Excel, or other software packages
- Choose appropriate graph types: Select the graph types that best illustrate the trends and patterns in the data
- Label Axes Clearly: Label the axes of the graphs clearly and accurately

Mean:
- Calculate the mean of the QC values over a specified time period
- Use the mean to establish a baseline for instrument performance
Standard Deviation:
- Calculate the standard deviation of the QC values over a specified time period
- Use the standard deviation to set control limits
Coefficient of Variation (CV):
- Calculate the CV of the QC values over a specified time period
- Use the CV to assess the relative variability of the data
Trend Analysis:
- Use statistical tests to detect trends or shifts in the QC data over time
- Common statistical tests include linear regression, moving averages, and control chart rules
Troubleshooting:
- In the event of a data deviation, review all instrument and reagent conditions to remedy any issues

Out-of-Control Points:
- QC value that falls outside of the control limits
- May indicate a problem with the instrument, the reagents, or the assay protocol
- Investigate the cause of the out-of-control point and take corrective action
Trends:
- A gradual increase or decrease in the QC values over time
- May indicate a slow degradation of instrument performance or reagent stability
- Schedule preventive maintenance or replace reagents
Shifts:
- An abrupt change in the QC values
- May indicate a sudden change in instrument settings or reagent performance
- Recalibrate instrument
- Replace reagents
Rules for Interpretation
- Westgard Rules - set of multirules that can be used to further define what is and isn’t in control for a given assay
Establish Trigger Points:
- Set specific parameters such as number of consecutive runs in one direction, a specific number of values outside of 2 standard deviations, or a single value outside of 3 standard deviations as trigger points for action

Instrument Recalibration:
- Perform a full instrument recalibration to ensure that the lasers, detectors, and fluidics system are properly aligned and calibrated
Reagent Replacement:
- Replace reagents that are known to be unstable or that have expired
Protocol Review:
- Review the sample preparation, staining, and data acquisition protocols
- Identify any steps that may be contributing to the problem
Maintenance:
- Ensure that the manufacturer’s recommended maintenance is occurring

Record all QC data:
- Record the date, time, instrument settings, reagent information, and QC values for each run
Document all actions taken:
- Document any maintenance, troubleshooting, or corrective actions that are performed
Review QC Data Regularly:
- Establish a schedule for regular review of the QC data
- Review the data for trends, shifts, or out-of-control points

Difficulty Identifying Trends:
- Possible Causes:
  - Too much variability in the data
  - Inadequate data visualization
- Troubleshooting Steps:
  - Reduce variability, and choose appropriate graph types
False Alarms:
- Possible Causes:
  - Inappropriate control limits
  - Random variation
- Troubleshooting Steps:
  - Adjust control limits to account for random variation
Failure to Detect Problems:
- Possible Causes:
  - Inadequate QC measures
  - Infrequent monitoring
- Troubleshooting Steps:
  - Implement more comprehensive QC measures
  - Increase the frequency of monitoring