Quality Control

Lucila B. Rabuco, PhD

Objectives of Laboratory Quality Assurance

OBJECTIVES OF LABORATORY QUALITY ASSURANCE

• Primary Goal

the production of high quality data through

measurement instruments

measurement techniques

that are accurate, reliable, & adequate for its intended purpose

Should be accomplished in a cost effective manner under a laboratory quality assurance program with the following objectives:

Objectives:

1. To measure the accuracy and precision of results within and between laboratories.

2. To continuously assess the reliability of data generated by the laboratory.

3. To detect training needs of the laboratory staff.

4. To identify weak methodology and provide a continuing source of research problems.

5. To provide a permanent record of instrument performance as a basis for validating data and projecting repair or replacement needs.

6. To upgrade the overall quality of laboratory performances.

7. To improve record keeping and report generation.

8. To ensure the integrity of the samples submitted to the laboratory.

9. To help ensure that the laboratory results will withstand legal scrutiny in regulatory actions.

8. Aesthetics

DIMENTIONS OF QUALITY

1. Performance

2. Features

4. Conformance to specifications

3. Reliability

5. Durability

6. Serviceability

7. Perceive quality

DEFINITIONS OF QUALITY

1. Quality is a distinctive inherent feature.

2. Quality is a distinguished attribute.

3. Quality is a degree of excellence.

4. Quality is the totality of features and

characteristics of a product or service that bear on its ability to satisfy given needs.

5. Quality is fitness for use.

7. Quality is freedom from deficiencies.

6. Quality is fitness for purpose.

14. Quality is the loss imparted to society by products.

8. Quality is conformance to specifications.

9. Quality is conformance to requirements.

10. Quality is zero defects,

11. Quality is customer satisfaction.

12. Quality is uniformity around a target.

13. Quality is the opposing force to lower cost and higher productivity.

QUALITY CONTROL

Definition

• Process of monitoring laboratory

analyses to ensure accuracy of results.

Good Quality Control Program

Monitors test performance

Helps identify problems

Helps in assessing reliability of results

Quality Assurance

Encompasses every aspect of the laboratory operation from patient identification, sample acquisition to the clear reporting of the final laboratory result.

Specificity = TN x 100

All w/o disease

( FP + TN )

Specificity

A measurement of the incidence of negative results in persons known to be free of disease (true negative)

Sensitivity = TP x 100

All with disease

( TP + FN )

Sensitivity

A measure of the incidence of positive results in persons known to have a condition ( true positive )

Basic quality control involves the analysis of specific control fluids (serum, urine, CSF) at the same time patient samples are analyzed for the constituents of interest

Terms used in Quality Control

• Accuracy

An indication of how close the answer obtained to the true value

Precision

Indicates how close the single values are to one another

Interassay vs intra-assay reproducibility

Quality Control Materials

drawn from a pool either from human or animal source

marketed commercially or may be prepared in the laboratory; Freeze-dried for long term stability

2 levels : “normal range”

“abnormal range”

may be obtained either “assayed” or “non-assayed”

Uses of quality control data

To identify if:

1. The assay is malfunctioning & no data obtained are reliable.

2. A problem with the assay is developing and needs to be corrected

1. (2S) One control value exceeds

± 2sd from the mean

2. (3S) One control value exceeds

± 3sd from the mean

Levy-Jennings Plot (Shewhart Plot)

Trend analysis of quality control data

Westgard Rules for Quality Control

A set of criteria developed to improve quality of monitoring, decrease subjectivity in data analysis, and provide some help in troubleshooting.

3. 2 (2S) Two consecutive control values exceed the same limit, either ± 2sd

4. R (4S) The numerical difference between two control values within the same run exceeds 4sd

5. 4 (1S) Four consecutive values (control) exceed either ± 1sd

6. 10 (x) Ten consecutive results all lie on the same side of the mean

Relationship between accuracy and precision

The Levy – Jennings Plot

Use of the Westgard rules

Violations of the 1 (2S) and 1 (3S) Westgard rules

Violation of the R (4S) Westgard rule.

Violations of the 2 (2S) and 4 (1S)

Westgard rules

Violation of the 10 () Westgard rule

Laboratory Quality

Control

An Overview

Definitions (1)

Quality Control - QC refers to the measures that must be included

during each assay run to verify that the test is working properly.

Quality Assurance - QA is defined as the overall program that

ensures that the final results reported by the laboratory are correct.

“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”

Definitions (2)

Quality Assessment - quality assessment (also known as

proficiency testing) is a means 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 may be external or internal, examples

of external programs include NEQAS, HKMTA, and Q-

probes.

Variables that affect the quality of results

The educational background and training of the

laboratory personnel

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 measurement

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.

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.

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

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.

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

An

tibo

dy U

nits

Target value

Assay Run

Warning rules

Warning 12SD : 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.

Warning 22SD : It detects systematic errors and is violated

when two consecutive IQC values exceed the mean on the

same side of the mean by 2SD.

Warning 41SD : 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.

Mandatory rules

Mandatory 13SD : It is violated when the IQC value exceeds

the mean by 3SD. The assay run is regarded as out of control.

Mandatory R4SD : It is only applied when the IQC is tested in

duplicate. This rule is violated when the difference in SD

between the duplicates exceeds 4SD.

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: 1 3SD

+3 sd

-3 sd

+2 sd

-2 sd

-1 sd

+1 sd

VZV IgG ELISA: Target Value = 49 U/ml

An

tibo

dy U

nits

Target value

Assay Run

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

An

tibo

dy U

nits

Target value

Assay Run

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.