ELISA Test Results Analysis

Process Control: Quality Control for Quantitative Tests

• Quantitative tests measure the quantity of a substance in a sample
• Quality control for quantitative tests ensures accurate and reliable patient results
• A quality management system includes organization, personnel, equipment, purchasing and inventory, process control, information management, documents and records, occurrence management, assessment, process improvement, customer service, and facilities and safety
• Learning objectives include differentiating accuracy and precision, selecting control material, establishing acceptable control limits, explaining Levey-Jennings charts, and describing how to correct "out of control" problems.

What is a Control?

• Control material contains the substance being analyzed
• It's included with patient samples during testing
• Controls validate the test system's reliability
• Controls are run after instrument calibration and periodically during testing

Calibrators vs. Controls

• Calibrators are substances with a specific concentration
• Calibrators set the measuring points on a scale
• Controls are substances similar to patient samples with established concentrations
• Controls ensure the procedure works properly

Characteristics of Control Materials

• Control materials are appropriate for the diagnostic sample
• Control values cover medical decision points
• Control materials are similar to test samples (matrix)
• Controls are available in large quantities, ideally enough for one year
• Controls can be stored in small aliquots

Types of Control Materials

• Control materials may be frozen, freeze-dried, or chemically preserved
• Reconstitution may be required for certain types

Sources of Control Materials

• Control materials can be commercially prepared
• Control materials can be made in-house
• Control materials can be obtained from other laboratories (usually central or reference)

Control Materials: Assayed vs. Unassayed

• Assayed controls have predetermined target values and are verified before use
• Unassayed controls do not have predetermined target values and require full assay before use
• In-house pooled sera are unassayed controls

Choosing Control Materials

• Control values cover medical decision points
• Control materials are similar to patient samples
• Controls are typically available in high, normal, and low ranges

Preparation and Storage of Control Material

• Adhere to manufacturer's instructions
• Maintain an adequate amount of the same lot number
• Store controls correctly

Steps in Implementing Quantitative QC

• Obtain control materials
• Run each control 20 times over 30 days
• Calculate the mean and ±1, 2, and 3 standard deviations

Measurement of Variability

• Variability in control tests is normal
• Variability can be affected by the operator, environmental conditions, and performance characteristics of the measurement
• The goal is to differentiate between variability from chance and that due to error

Measures of Central Tendency

• Data sets cluster around a central value (or mean)
• The three are Mode, Median, Mean

Symbols Used in Calculations

• ∑ represents the sum of all data points
• n represents the number of data points
• Xi represents each individual measurement

Calculation of Mean

• The mean is calculated by summing all measurements and dividing by the total number of measurements

Calculation of Mean: ELISA Tests

• Run controls 20 times in 30 days
• Record OD (optical density) and cut-off values
• Divide OD by CO
• Sum the ratios and divide by the number of measurements

Data Showing Outlier

• Data showing an outlier are provided (numeric values)

Normal Distribution

• All values are distributed symmetrically around the mean
• A characteristic "bell-shaped" curve
• Assumed for all quality control statistics

Quality Control

• Quality control is used to monitor the accuracy and precision of the assay
• Accuracy is the closeness of measurements to the true value
• Precision is the amount of variation in measurements
• Bias is the difference between the expectation of a test result and an accepted reference value

Accuracy and Precision

• A graphic representation of accurate and precise, precise but biased, and imprecise results is provided

Standard Deviation and Probability

• For a normally distributed set of data, a random measurement will fall within:
  • ±1 SD 68.3% of the time
  • ±2 SD 95.5% of the time
  • ±3 SD 99.7% of the time

Standard Deviation (SD)

• SD is the principle measure of variability
• Formula for calculating SD provided

Coefficient of Variation (CV)

• CV is the SD expressed as a percentage of the mean

Levey-Jennings Chart

• A graphical representation of control ranges

Statistics for Quantitative QC

• Assay control materials at least 20 points over 20–30 days
• Ensure procedural variations are represented
• Calculate the mean and ±1, 2, and 3 standard deviations

Draw lines for Mean and SDs

• Provide an example of a chart with lines for mean and SDs (and a calculation)

Levey-Jennings Chart

• Plot daily control measurements

Number of Controls

• Interpretation of QC depends on the number of controls run
• Good: One control - accept results within ±2SD unless shift or trend
• Better: Two levels of controls - apply Westgard multirule system

Detecting Error

• Random error: variation in QC results with no pattern—only a cause for rejection if outside 2SDs.
• Systematic error: not acceptable; correct the source of error
• Examples of systematic error:
  • Shift—control on one side of the mean 6 consecutive days
  • Trend—control moving in one direction, heading toward “out of control” value

Solving Out-of-Control Problems

• Identify the problem
• Refer to established policies and procedures

Possible Problems

• Degradation of reagents/kits
• Control material degradation
• Operator error
• Failure to follow instructions
• Outdated procedure manuals
• Equipment failure
• Calibration error

Summary

• A quality control program is essential for quantitative tests
• A program should monitor all quantitative tests, have written policies and procedures followed by lab staff, and provide for troubleshooting and corrective action
• A quality control manager should monitor and review QC data; use statistical analysis; provide for good records

Key Messages

• A quality control program differentiates normal variation from error
• QC programs monitor accuracy and precision
• Do not release patient results if QC doesn’t meet target values

Questions?

• A section indicating space for questions or comments

This quiz tests your knowledge on the statistical concepts and calculations relevant to analyzing ELISA test results. You will explore the steps in calculating the mean, understand the distribution of values, and evaluate quality control metrics. Test your understanding of how to interpret specific values in the context of ELISA data.