Quality Control in Quantitative Tests

Process Control: Quality Control for Quantitative Tests

• Quantitative tests measure the quantity of a substance in a sample
• Quality control for quantitative tests is designed to ensure patient results are accurate and reliable
• The Quality Management System encompasses organization, personnel, equipment, purchasing & inventory, process control, information management, documents & records, occurrence management, assessment, process improvement, customer service, and facilities & safety.

Learning Objectives

• Participants will be able to distinguish between accuracy and precision
• Participants will be able to select appropriate control materials for the laboratory
• Participants will be able to establish acceptable control limits for a method when only one level of control material is available
• Participants will be able to explain the use of Levey-Jennings charts
• Participants will be able to describe how to correct "out of control" problems

What is a Control?

• A control material contains the substance being analyzed
• Controls are included with patient samples during testing
• Controls are used to validate the reliability of the test system
• Controls should be run after calibrating the instrument and periodically during testing

Calibrators vs. Controls

• Calibrators are substances with a specific concentration used to set measuring points on a scale
• Controls are substances similar to patient samples with an established concentration. They ensure the procedure is working correctly.

Characteristics of Control Materials

• Control materials must be appropriate for the diagnostic sample
• Control values should cover medical decision points
• Controls should be similar to test samples (matrix)
• Controls should be available in large quantities, ideally sufficient for a year
• Controls should be storable in small aliquots

Types of Control Materials

• Controls can be frozen, freeze-dried, or chemically preserved
• Reconstitution, if necessary, requires precise technique.

Sources of Control Materials

• Controls can be commercially prepared
• Controls can be prepared "in-house"
• Controls can be obtained from another laboratory, typically a central or reference laboratory

Control Materials: Assayed vs. Unassayed

• Assayed controls have predetermined target values
• Unassayed controls lack predetermined target values and require a full assay before use
• In-house pooled sera are a type of unassayed control requiring a full assay

Choosing Control Materials

• Control values should cover medical decision points
• Controls are usually available in high, normal, and low ranges, reflecting patient samples' potential characteristics

Preparation and Storage of Control Materials

• Adhere to manufacturer's instructions
• Maintain sufficient quantities of the same lot number
• Store controls correctly to maintain quality

Steps in Implementing Quantitative QC

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

Measurement of Variability

• Variability is a normal occurrence when a control is repeated
• Variability can be influenced by the operator, environmental conditions, and characteristics of the measurement

Measures of Central Tendency

• Data sets are generally distributed around central values.
• Measures of central tendency include mean, mode, and median

Symbols Used in Calculations

• The summation notation, ∑, signifies summing data points.
• n represents the number of data points
• X₁ - X_n = each data point/measurement (first through last)
• X is the mean/average

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, recording OD and cut-off (CO) values.
• Divide OD by CO to standardize data
• Average the standardized results to get the mean

Data showing outliers

• Outliers are data points significantly different from the rest of the data set.
• A potential example of data is provided

Normal Distribution

• Values are symmetrically distributed around the mean.
• The distribution is often "bell-shaped".
• Normal distribution is assumed in many quality control statistics

Quality Control Is Used To Monitor…

• Quality control monitors the accuracy and precision of assays.
• Accuracy is the closeness of measurements to the true value
• Precision is the amount of variation in measurements
• Bias is the difference between expectation of a result and the accepted reference value

Accuracy and Precision

• Accurate = precise but not biased.
• Precise, but biased measurements group together but not around the true value.
• Imprecise measurements are scattered.

Standard Deviation (SD) and Probability

• SD is the principle measure of variability in the lab.
• 68.3% of data fall within ± 1 SD
• 95.5% fall within ± 2 SD
• 99.7% fall within ± 3 SD

Coefficient of Variation (CV)

• CV is the SD expressed as a percentage of the mean
• CV is used to monitor precision and compare methods
• CV ideally should be less than 5%

Levey-Jennings Chart

• Graphically represents control ranges
• Used to plot daily control measurements

Statistics for Quantitative QC

• Assay 20 control samples over a 20-30 day period
• Calculate mean and standard deviations (±1, ±2, and ±3SD)

Draw Lines for Mean and SDs

• Plot mean and ±1, ±2, and ±3SD for a given number of controls

Levey-Jennings Chart: Plotting Daily Control Measurements

• Use the chart to visually represent daily control measurements

Number of Controls

• Interpretation of control data depends on the number of controls run. Some tests may only use one level of control
• With one control, accept results if the control measures fall within ±2SD, unless a trend or shift is present
• With two quality control levels, use Westgard systems for interpretation

Detecting Error

• Random error: Variation in QC results with no pattern, rejection only if outside ±2SD.
• Systematic error: unacceptable outcome that requires correction of errors. Common examples include shift (6+ consecutive values on one side of the mean) and trend (consecutive results moving in the same direction)

Levey-Jennings Chart: Shift

• A shift occurs in the plot of daily control results when values are consistently outside the expected range toward one direction.

Levey-Jennings Chart: Trend

• A trend occurs in the plot of daily control results when there is a gradual upward or downward change in the values over several days

Measurement Uncertainty

• Measurement uncertainty is a range of values that represents where the true value is reasonably expected to fall
• It's usually estimated at 95% confidence/coverage
• For most cases, a range of ±2 SD is acceptable as measurement uncertainty

If QC is out of Control

• Stop testing, identify the problem, and correct it
• Repeat testing on patient samples and controls
• Do not release patient results until the problem is solved and controls show proper performance

Solving out-of-control problems

• Identify the problem
• Refer to established policies and procedures to correct it immediately

Possible Problems

• Reagent/kit degradation
• Control material degradation
• Operator error
• Failure to follow manufacturer's instructions
• Outdated procedure manual
• Equipment failure
• Calibration error

Summary

• A quality control program is crucial for quantitative tests
• The program should monitor tests, have written policies & procedures, utilize statistical analysis, and provide a quality manager
• The QC program should provide tracking/records and trouble-shooting

Key Messages

• QC programs distinguish between normal variation and errors in assays
• QC programs monitor accuracy, and precision in lab assays
• Patient results should not be released if QC results do not meet laboratory target values

Questions and Comments

• An area for questions and comments about the quality control program.

Test your knowledge on the principles and practices of quality control for quantitative tests. This quiz covers various aspects such as control materials, implementation steps, and statistical calculations relevant to maintaining quality in testing environments.