chem II 2019.ppt

Laboratory Quality assurance
and quality Control

QA
 Quality Assurance - is defined as the overall
program that ensures that the final results reported
by the laboratory are correct.
 describes the steps taken in and outside the lab to
achieve reliable results right from the preparation
of the patient and ending with the correct
interpretation of the results
 Systematic Application of optimum procedures to
ensure valid, reproducible, and accurate results.

QC
 Quality Control - QC refers to the measures that must be included
during each assay run to verify that the test is working properly.
 This involves the technique and precautions taken by laboratory
personnel to ensure that tests are performed correctly.
 Lab QC = Running controls and statistically analyzing the data before
releasing patient results
 “The aim of quality control is simply to ensure that the results
generated by the test are correct. However, quality assurance is
concerned with much more: that the right test is carried out on the right
specimen, and that the right result and right interpretation is delivered
to the right person at the right time”

Quality assessment
 Quality Assessment - quality assessment (also known as
proficiency testing) is a mean to determine the quality of
the results generated by the laboratory. Quality assessment
is a challenge to the effectiveness of the QA and QC
programs.
 Quality Assessment and Quality Control measures must
include a means to identify, classify, and limit error.
 Quality Assessment may be external or internal, examples
of external programs include NEQAS, HKMTA, and Q-
probes. UVRI,EAQAS

Standards
• Highly purified substance, whose exact composition is
known.
• Non- biological in nature
• Uses
 Run with pt. sample to validate the run
• Ex. With each run of a Urine Osmolality a Std. is
often run to determine the accuracy and precision of
the run
 Generate Calibration Curve
 Different concentrations of the Std. are used to plot
a graphic curve
 Patient samples are compared to the calibration
curve and the concentration of the analyte is
quantified.

Reference Solutions
• Biological in nature
• Have an ‘assigned’ value
• Used exactly like a standard

Controls
• Resemble the patient sample
 Have same characteristics as patient sample, color
viscosity etc.
• Can be purchased as
 ‘assayed’ – come with range of established values
 ‘un-assayed’ - your lab must use statistical
measures to establish their range of values.
• The results of any run / analysis must be
compare to the ‘range of expected’ results to
determine acceptability of the analysis.

Controls, cont’d.
• Depending on the test 1 or more levels of control
will be required.
• Control within expected range = IN CONTROL=
accept the QC and report patient results
• Control outside of expected range= OUT of
CONTROL=address

Comparing Results to the
Appropriate Range
• Control results - compared to their own range of
expected results determined by the control
manufacturer or individual laboratory
• Patient values – compared to published reference
values or patient population reference ranges
established within the laboratory.

 Accuracy; the extent to which measurements agree with the true
value of the quantity being measured
 This can be aided by the use of properly standardized procedures,
statistically valid comparisons of new methods with established
refraince methods, the use of controls and participate in
proficiency testing programmes.
 Precision; the degree of the reproducibility of the test results. It
can be ensured by the proper inclusion of standards reference
samples or control solutions.
 Reliability; this is when the method used maintains a steady state
of accuracy and precision over a considerable period of time.

Precision and Accuracy
Low Accuracy,
High Precision
High Accuracy,
Low Precision
High Accuracy,
High Precision

Sensitivity and specificity
 Sensitivity; This is the minimum amount of a substance
in a biological medium that can be determined with the
accuracy and precision and specific by a particular
method
 The clinical sensitivity of an assay is the fraction of those
subjects with a specific disease that the assay correctly
predicts.
 Specificity; this the exclusive measurements of a
compound for which the method has been design
 The clinical specificity is the fraction of those individuals
without the disease that the assay correctly predicts.

Elements of QA
 Technical competence; the service provider must
have the right knowledge, skills and attitude to
perform laboratory test.
 Effectiveness; the lab personnel shd follow the
norms and guidelines for the procedures
 Continuity; it means providing a range of services
within the means of the lab
 Efficiency an efficient services provider shd produce
the test results within available resources

Continues…..
 Validation checks whether the test procedure or
any equipment satisfy the set standard
 Safety; shd prevent hazards in the laboratory, the
service provider, pts, other health workers or any
other persons who enters and use lab
 Facility; the lab shd be of a suitable size construction
and location to meet the requirements of the range of
tests offered.

QA phases.
 Pre-analytical phase of quality assurance ensures
quality in everything before testing process both
within the lab and outside the lab
 Proper selection and evaluation of the testing
procedures
 Correct ordering of the tests
 Proper preparation of the patient for the test.e.g
fasting sugar, OGTT

Continues…
 Proper identification of the patient
 Proper collection of the samples i.e in a correct containers and
under condition specified by the test procedure.
 Timely transportation of the samples to the lab,
 Proper handling of the sample from time of transportation to
the time of analysis
 Proper handling of the sample in the lab, includes proper
documentation of the sample, identification within the lab
correct centrifugation technique

Analytical phase
 Proper labeling and use of reagnts must labeled with
conc, date of preparation, expiry date, initials of the
person who prepared.
 Periodic calibration of pipetting devices and careful
maintenance of instruments
 Using control samples to check for bias
 Training lab workers
 Establishing performance std for each test

18
Examination/analytical
Laboratory Analysis
Examination Phase
Examples:
established algorithm followed
correct timing of test
reported results when control
results pass or within the range
proper dilution and pipetting of sample or
reagents
 appropriate storage of reagent

Post analytical phase
 This is the process of verifying quality once the sample
has been analyzed
 Verification of the calculations of final report
 Review of test results for possible errors
 Writing reports that are easy to read and interpret
 Procedures for informing the clinicians about the tests
that requires immediate attention
 Verification of correct interpretation of lab test by the lab
personals and physicians.

Variables that affect the
quality of results
 The educational background and training of the
laboratory personnel (incompetency)
 The condition of the specimens
 The controls used in the test runs
 Reagents
 Equipment
 The interpretation of the results
 The transcription of results
 The reporting of results

Errors in clinical laboratory
 True value - this is an ideal concept which cannot be
achieved.
 Accepted true value - the value approximating the
true value, the difference between the two values is
negligible.
 Error - the discrepancy between the result of a
measurement and the true (or accepted true value).

Sources of error
 Input data required - such as standards used, calibration values, and
values of physical constants.
 Inherent characteristics of the quantity being measured - e.g. CFT
and HAI titre.
 Instruments used - accuracy, repeatability.
 Observer fallibility - reading errors, blunders, equipment selection,
analysis and computation errors.
 Environment - any external influences affecting the measurement.
 Theory assumed - validity of mathematical methods and
approximations.

Error
• Error is the discrepancy between the result
obtained in the testing process and its ‘True
Value’ / ‘Accepted True Value’
• Pre-Analytical Errors=40%
• Analytical Errors=20%
• Post-Analytical Errors=20%

Pre-Analytical Errors
• Before the specimen is run
• Examples: Clerical, patient ID, specimen selection
and contamination, improper storage of reagents
and specimen, improper transport, etc.
• Through Quality Assurance measures, the
laboratory tries to maintain control over these
factors
 Well trained phlebotomy staff, nurses, and physicians
 Use of easy patient & specimen identification methods,
such as bar code identification.

Analytical error
• Testing errors
• Random or indeterminate
 Hard or impossible to trace
 Examples: Electricity surge, One-time events, etc
• Systematic or determinant
 Identifiable cause
 Examples: Specimen carryover, contaminated reagents,
instrument component malfunction, dirty electrodes, etc.
• Through Quality Control measures, such as always
running controls, the laboratory limits these
errors.

Post-Analytical Errors
• After testing
• Examples: Clerical, result reported on wrong patient,
instrument to host computer errors, etc.
 transcription error in reporting
 report illegible
 report sent to the wrong location
 report not sent
 QA measures such as comprehensive and easily read Report
sheets for manual tests must be implemented when problems
are identified.

Random Error
 An error which varies in an unpredictable manner, in magnitude
and sign, when a large number of measurements of the same
quantity are made under effectively identical conditions.
 Random errors create a characteristic spread of results for any test
method and cannot be accounted for by applying corrections.
Random errors are difficult to eliminate but repetition reduces the
influences of random errors.
 Examples of random errors include errors in pipetting and changes
in incubation period. Random errors can be minimized by training,
supervision and adherence to standard operating procedures.

Continues…..
 This is where results differ from the correct results by varying
amounts. The common causes includes;
 Incorrect and variable pipetting
 Inadequate mixing of the sample with the reagents
 Incubation at inconsistence temp or of in correct length of time
 Dirty test tubes, pipettes or other glass wares used in the test
 Fluctuating colorimeter reading due to unreliable main voltage
supply
 Incomplete removal interfering substances in serum e.g RBCs
or proteins
 Use of dirty or finger marked cuvettes or reading samples
containing air bubbles.

Random Errors
x
x x
x x
True x x x x
Value x x x
x x x
x
x
x

Systematic Error
 An error which, in the course of a number of measurements of
the same value of a given quantity, remains constant when
measurements are made under the same conditions, or varies
according to a definite law when conditions change.
 Systematic errors create a characteristic bias in the test results
and can be accounted for by applying a correction.
 Systematic errors may be induced by factors such as variations in
incubation temperature, blockage of plate washer, change in the
reagent batch or modifications in testing method.

Systematic Errors
x
x x x x x x x
True x
Value

Qualitative QC vs. Quantitative QC
• Quantitative tests
• Measured quantity/concentration of analyte in the
control
• Data must be graphed and evaluated by
numerical statistics
 Examples: WBC count, Glucose, quantitative HCG
• Qualitative tests and Semi-Quantitative
• Qualitative=Pos/Neg or Present/Absent
• Semi-Quantitative=Small, Medium, Large
• Generally not statistically analyzed
 Examples: Clinitest, Acetest, qualitative HCG

QC Data Analysis:
Measures of Central tendency
• Measures of Central tendency
( how numerical values can be expressed
as a central value )
• Mean - Average value
• Median - Middle observation
• Mode - Most frequent observation

QC Data Analysis:
Variance and Standard Deviation
• An important tool in the statistical analysis
is determining:
• Variance =
• Standard Deviation (SD) - a measure of
the scatter around the arithmetic average
(mean) in a Gaussian distribution.
• SD=
• or Square Root of variance

How to Manually Calculate Variance
and Standard Deviation:
1. Subtract the mean from each score.
2. Square each Result
3. Sum all of the squares
4. Divide the sum of the squares by the
number of data points (N)
5. The result is the VARIANCE
6. Take the square root of the variance.
7. The result is the SD.

Quality Control
• 95% confidence limit (± 2 SD) - 95% of all
the results in a Gaussian distribution

How many points fall within
1SD?
• Another way of reviewing data
• Dispersal / or how the individual data points are
distributed about the central value

Levey-Jennings QC Graph (no
points)

Levey-Jennings QC Chart for 1 month

Statistical concepts
 Shift – when there are 6 consecutive data results on
the same side of the mean

Statistical concepts
 Trend – when there is a consistent increase OR
decrease in the data points over a period of 6 days.
(A line connecting the dots will cross the mean.)

Shewhart Control Charts
A Shewhart Control Chart depend on the use of IQC specimens and is
developed in the following manner:-
 Put up the IQC specimen for at least 20 or more assay runs and record
down the O.D./cut-off value or antibody titre (whichever is applicable).
 Calculate the mean and standard deviations (s.d.)
 Make a plot with the assay run on the x-axis, and O.D./cut-off or
antibody titre on the y axis.
 Draw the following lines across the y-axis: mean, -3, -2, -2, 1, 2, and 3
s.d.
 Plot the O.D./cut-off obtained for the IQC specimen for subsequent assay
runs
 Major events such as changes in the batch no. of the kit and instruments
used should be recorded on the chart.

Westgard rules
 The formulation of Westgard rules were based on statistical
methods. Westgard rules are commonly used to analyse data in
Shewhart control charts.
 Westgard rules are used to define specific performance limits for a
particular assay and can be use to detect both random and systematic
errors.
 There are six commonly used Westgard rules of which three are
warning rules and the other three mandatory rules.
 The violation of warning rules should trigger a review of test
procedures, reagent performance and equipment calibration.
 The violation of mandatory rules should result in the rejection of the
results obtained with patients’ serum samples in that assay.

0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Shewhart Chart
+3 sd
-3 sd
+2 sd
-2 sd
-1 sd
+1 sd
VZV IgG ELISA: Target Value = 49 U/ml
Antibody
Units
Target value
Assay Run

13s Westgard Rule
 13s
 A single control measurement exceeds three
standard deviations from the target mean
 : It is violated when the IQC value exceeds the mean
by 3SD. The assay run is regarded as out of control
 Action - Reject

12s Westgard Rule
 12s
 It is violated if the IQC value exceeds the mean by
2SD. It is an event likely to occur normally in less
than 5% of cases
 Action – must consider other rule violations (trend,
shift, etc)
 This is a warning

22s Westgard Rule
 22s
 It detects systematic errors and is violated when two
consecutive IQC values exceed the mean on the same side
of the mean by 2SD.
 Action – Reject

R4s Westgard Rule
 R4s
 One control measurement in a group exceeds the
mean plus 2S and another exceeds the mean
minus 2S.

41s Westgard Rule
 41s
 It is violated if four consecutive IQC values exceed
the same limit (mean  1SD) and this may indicate
the need to perform instrument maintenance or
reagent calibration

Continues.
 Mandatory 10x
 This rule is violated when the last 10 consecutive IQC values
are on the same side of the mean or target value.

0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Westgard Rules: 10X
+3 sd
-3 sd
+2 sd
-2 sd
-1 sd
+1 sd
VZV IgG ELISA: Target Value = 49 U/ml
Antibody
Units
Target value
Assay Run

If QC FAILS?
One Possible Plan of Action: (your lab may advise another)
1. Look at vial of control. If using the last drops—reconstitute or open
new vial.
2. If plenty of QC left in vial, mix well, repour, rerun. Most issues
resolve by now. However…
3. If the QC fails again, check the volumes and expiration dates of the
reagents. Change out if necessary.
4. Calibrate the instrument, run cleaning sequence, or perform
maintenance as needed.
5. Rerun control.
6. If controls fail repeatedly after multiple efforts, call technical
support.
Use discretion keeping in mind that controls, calibrators, and
reagents are expensive.

Other QC Checks
• Delta checks
• Compares a current test result on a patient to last
run patient test, flagging results outside expected
physiological variation.
• Many False positives, but DO NOT ignore--
investigate
• MCHC=Hgb / Hct * 100 (expect 32-36)
• Rule of 3=Hemoglobin x3 = hematocrit
• Compare patient BUN / creatinine (10/1 –
20/1)

Internal Quality Control Program
for Serological Testing
An internal quality control program depend on the use of
internal quality control (IQC) specimens, Shewhart Control
Charts, and the use of statistical methods for interpretation.
Internal Quality Control Specimens
IQC specimens comprises either (1) in-house patient sera
(single or pooled clinical samples), or (2) international serum
standards with values within each clinically significant ranges.

Follow-up action in the event of a
violation
There are three options as to the action to be taken in the event of a
violation of a Westgard rule:
 Accept the test run in its entirety - this usually applies when
only a warning rule is violated.
 Reject the whole test run - this applies only when a
mandatory rule is violated.
 Enlarge the greyzone and thus re-test range for that particular
assay run - this option can be considered in the event of a
violation of either a warning or mandatory rule.

chem II 2019.ppt

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