concepts in quality control data management - avera health · • frequency of qc • qc must be...
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Concepts in Quality Control Data ManagementBeth Lewis MT (ASCP)
Quality System Specialist - Midwest
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Objectives
At the conclusion, the participant should be able to:
• Describe how laboratory quality is achieved• Identify common causes of random and
systematic error • List the steps used to plan a statistical QC
procedure • Evaluate QC using Westgard rules and graphs
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Reference Material Clinical and Laboratory Standards Institute
Statistical Quality Control for Quantitative Measurement Procedures: Principles and Definitions; Approved Guideline – Third Edition (C24-A3, Vol.26 No. 25)
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Quality control is one of the most important and least often appreciated activities in the clinical lab.
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Patient CareLaboratoryCredibility
Cost
•Quality control decisions are based on:
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•Laboratory quality is achieved through the reporting of accurate patient results by controlling:
Pre-Analytical Post-Analytical
Errors
Analytical
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Pre-Analytical
•Incorrect or incomplete patient instruction & preparation
•Patient sample not properly collected
•Sample taken from wrong patient•Patient sample not properly
preserved
Errors
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•Wrong result reported or charted – transcription errors
•Computer error – LIS integrity•Incorrect interpretation of test
results provided to clinician•Incorrect patient normal ranges•Results reported from an
instrument not used to establish the normal range
Post-Analytical
Errors
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• Instrument malfunction• Lack of maintenance• Inadequate environmental control• Inadequate electrical supply• Inadequate water supply• Inadequate operator training• Incorrect calibration• Use of compromised reagents
and/or calibrators
Analytical
Errors
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QC Strategy
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QC Strategy
1. Define QC material
2. Decide the number of measurements and the location of QC
3. Set the control limits
4. Define Error – random/systematic
5. Decide on the QC rules for acceptable run
6. Outline the response to the data acceptance decision (out-of-control)
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Select Control Materials
• Menu• Shelf life/open-vial stability• Vial to vial variability• Medical relevant levels• Challenge range of instrument• Matrix/preservatives• QC material different from calibrator
material• Number of levels and concentration
• Sufficient to determine proper method performance over the measuring range of interest
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Frequency and Sequence
• Frequency of QC• QC must be analyzed once during a run• Run is defined as interval that the method is
considered stable
• Sequence of QC• QC results evaluated before reported patients• Placement immediately after calibration may
give false low imprecision and shift during run
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Establishing Data Parameters
•Determination of valid mean and standard deviation values are crucial to successful data rejection or acceptance by error detection schemes[s]
[x]
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Set Control Limits
Product Inserts vs.
Old Lot New Lot
Parallel Testing
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CLIA Regulations
CLIA 88, Final RuleFederal Register, February 2, 1992
Standard 493.1218
“The stated values of an assayed control material may be used as the target values provided the stated values
correspond to the methodology and instrumentation employed
by the laboratory and are verified by the laboratory.”
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CLSI Recommendations
Clinical and Laboratory Standards InstituteC24-A3 Volume 26 Number 25
2006
“If assayed controls are used, the values stated on the assay sheets
provided by the manufacturer should be used as guides in setting
the initial control limits for testing new materials. Actual values for the mean and standard deviation
must be established by serial testing in the laboratory. The
observed mean should fall within the range published by the
manufacturer.”
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QC Statistics
Standard Deviation
[s]Coefficient of Variation
[cv]Mean
[x]
•The first step in defining decision limits
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Mean
• Arithmetic average of a set of data points
• Sum of values divided by the number of values
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Standard Deviation
• A measure of variability of the data points
• The degree of dispersion around the mean
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Coefficient of Variation
•Measure of variability•Expressed as a percent
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• CLSI Recommendations• Standard • Provisional
Calculation a Mean/Standard Deviation
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Things to Remember When Calculating QC Statistics
• Do I have a sufficient number of data points for the calculation?
• Have I used a sufficient number of analytical runs to account for random variables?
• Multiple calibrations• Multiple reagent lots• Maintenance• Technologists• Environmental conditions
• Did I follow “NORMAL” procedure on each analytical run?
• Have I appropriately excluded outliers?
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• Low value – arrange high to low• High value – arrange low to high
Outlier Test
X20 X21 X22
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Outlier Test
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• Use the 0.05 column (5% of values may be outside 2sd)
Outlier Test
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• Use the number of data points
Outlier Test
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Outlier Test
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Outlier Test
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• Reject if the value is ≥ table value• Accept if the value is ≤ table value
Outlier Test
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• CLSI Recommendations• Standard • Provisional
• 95% Confidence Interval
Calculation a Mean/Standard Deviation
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Estimation of 2s Range
• Estimation of a 2s Range: • For a small sample by calculating a 95% confidence
Interval• Standard Error of the Mean (SE) =
• n = 10• x = 398.2• s = 5.41• SE = 1.71• LL = 398.2 – (1.71)(2.262)• UL = 398.2 + (1.71)(2.262)• 2s Range (est.) = 394.3-402.1
• n = 5 • x = 401.2 • s = 4.97 • SE = 2.22 • LL = 401.2 – (2.22)(2.776) • UL = 401.2 + (2.22)(2.776) • 2s Range (est.) = 395.0-407.4
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Calculation of A 95% CI
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• Target SDI is 0.0, indicating that the lab’s mean value is the same as the peer’s
Standard Deviation Index (SDI)
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Coefficient of Variation Ratio (CVR)
• Ideally, CVR ≤ 1.0, since your values are from a single lab
• While the peer CV is from several
• If CVR = 1.5 to 2.0, the lab is 50-100% less precise than its peer group, usually requiring investigation
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Arriving at Decision Limits
Freq
uenc
y of
Occ
urre
nce
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SystematicRandom
Types of Analytical Errors
• Accuracy• Shifts• Trends
• Precision• Inherent to the test
system• Always present• Minimize any increase
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Set Quality Control RulesWestgard Rules
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Westgard Rules Basics
• Error Detection • Random • Systematic
• False Rejection • Multi-rule improves error detection with a low
probability of false rejection• Minimum of 2 controls per run• Not all rules are necessary for all tests
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1 - 2s Rule• Violated when a single
control value is outside ±2 standard deviations from the mean
• Usually a warning rule• Applied in a single run• Does not usually
require run rejection
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1 - 3s Rule• Violated when a single
control value is outside ±3 standard deviations from the mean
• Detects principally random error, but may indicated the beginning of a large systematic error
• Applied within a single run
• May be cause for run rejection
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2- 2s Rule
Across Run
• Violated when two consecutive control values are: • Greater than ±2
standard deviations from the mean
• On the same side of the mean
• Indicates systematic error potentially affecting only a single portion of the analytical curve
• May be cause for run rejection
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2- 2s Rule
Within Control Material
• Violated when two consecutive control values are: • Greater than ±2
standard deviations from the mean
• On the same side of the mean
• Indicates systematic error potentially affecting the entire analytical curve
• May be cause for run rejection
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2 of 3-2s Rule
Within Run
• Violated when any two of all three levels of control in a run are greater than ±2 standard deviations on the same side of the mean
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R - 4s Rule• Violated when there is
a 4 standard deviation difference between two control values within the same run
• Indicates random error• Applied within a single
run• May be cause for run
rejection
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4 - 1s Rule
Within Control Materials
• Violated when 4 consecutive QC values are:
• Greater than ±1 standard deviation from the mean
• On the same side of the mean
• Indicates systemic bias in a single area of the method curve
• Usually not clinically significant bias
• Run rejection is not usually required
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4 - 1s Rule
Across Control Material
• Violated when 4 consecutive QC values are:
• Greater than ±1 standard deviation away from the mean
• On the same side of the mean
• Indicates systematic error over a broader range of concentrations
• Usually not clinically significant bias
• Run rejection is not usually required
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Within Control Material
10x Rule• Violated when 10
consecutive control values within one level are on the same side of the mean
• Indicates systematic error in a single area of the method curve
• Usually not clinically significant bias
• Run rejection is not usually required
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Across Control Material
10x Rule• Violated when 10
consecutive control values across levels are on the same side of the mean
• Indicates systematic error over a broader range of concentrations
• Usually not clinically significant bias
• Run rejection is not usually required
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MULTIRULE FLOW CHARTWhen a rule is violated, stop and investigate
Troubleshooting & Westgard Rules
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TroubleshootingOut of Control Situations
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Troubleshooting Random Error
•Was there a deviation from procedure?•Was the correct equipment used?•Is there potential for random malfunction in the
test system?•Is the test sensitive to technique?•Is this error a manifestation of pre-analytical error?•Is the test system vulnerable to electrical power
fluctuations?•Is this truly random error or the initial stages of
systematic error?
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Sources of Random Error
•Power supply•Double pipetting of control sample•Misplacement of control sample
within the run•Air bubbles in water supply•Random air bubbles in reagent or
sample pipette system
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Sources of Systematic Error
• Improper alignment of sample or
reagent pipettes
• Drift or shift in incubator chamber
temperature
• Inappropriate temperature/humidity
levels in the testing area
• Change of reagent or calibrator lot
• Deterioration of reagent while in
use, storage or shipment
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• Deterioration of the control product while in use, storage or shipment
• Incorrect handling of the control product (e.g., freezing when not recommended)
• Inappropriate storage of control products in frost-free freezers
• Dirty filter wheel• Failing light source• Use of non-reagent grade water
in the test system• Recent calibration• Change in test operator
Sources of Systematic Error
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Troubleshooting the Control Product
• Is the product being aliquoted and frozen?
• Is the product being properly stored before and after reconstitution?
• Is the reagent being reconstituted with volumetric pipettes?
• Is reagent-grade water used for reconstitution?
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Troubleshooting with Graphics
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Troubleshooting Using Graphical Techniques
Levey-Jennings Charts
Frequency Histograms
Youden Plots
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All 3 charts have same mean, SD, & CV
Levey-Jennings
Chart
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Look For a Pattern
Expected
Cyclical
Shift Up
Levey-Jennings
Chart
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Dual Population
FrequencyHistogram
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Accuracy and Precision
YoudenPlot
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Accuracy and Precision
YoudenPlot
![Page 64: Concepts in Quality Control Data Management - Avera Health · • Frequency of QC • QC must be analyzed once during a run • Run is defined as interval that the method is considered](https://reader031.vdocuments.net/reader031/viewer/2022041412/5e19293fcfea014d291c85e9/html5/thumbnails/64.jpg)
Accuracy and Precision
YoudenPlot
![Page 65: Concepts in Quality Control Data Management - Avera Health · • Frequency of QC • QC must be analyzed once during a run • Run is defined as interval that the method is considered](https://reader031.vdocuments.net/reader031/viewer/2022041412/5e19293fcfea014d291c85e9/html5/thumbnails/65.jpg)
Accuracy and Precision
• Both levels have positive bias• If random error, where would points be?
YoudenPlot
![Page 66: Concepts in Quality Control Data Management - Avera Health · • Frequency of QC • QC must be analyzed once during a run • Run is defined as interval that the method is considered](https://reader031.vdocuments.net/reader031/viewer/2022041412/5e19293fcfea014d291c85e9/html5/thumbnails/66.jpg)
Accuracy and Precision
• Both levels have positive bias• If random error, where would points be?
YoudenPlot
![Page 67: Concepts in Quality Control Data Management - Avera Health · • Frequency of QC • QC must be analyzed once during a run • Run is defined as interval that the method is considered](https://reader031.vdocuments.net/reader031/viewer/2022041412/5e19293fcfea014d291c85e9/html5/thumbnails/67.jpg)
Action Plan for Rule Violations
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Good Laboratory Habits
• Check rule violations to determine the type of error
• Relate the type of error to possible causes
• Consider factors in common on multi-test systems
• Relate the problem to recent changes
• Verify the solution and document the remedy
• Perform a regular review of the Quality System to assess effectiveness
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• Consider these questions• What are the statistical out-of-control events?• What is the frequency of outliers?• What is the amount of bias present?• Are the rules in effect specific to the
methodology or technology and TEa?• Would more stringent single rule or more multi-
rule improve error detection?• Should the mean be adjusted?• How much imprecision is present?• How much bias is present?
Keys to Productive Review of Lab Quality System
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Keys to Productive Review of Lab Quality System
• Consider these questions• Are you using the appropriate peer group to
estimate the lab’s comparative bias?• Are the performance goals for the test,
imprecision and bias, set appropriately?• How frequently do Westgard errors occur?• How frequently is the test recalibrated?
(Continued)
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One last Question to Ask Yourself
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Questions?
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Contact Information: Beth Lewis
Quality System SpecialistMidwest Region
Bio-Rad LaboratoriesCell # (708) 497-5723
E-mail: [email protected]