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THE BASICS OF LABORATORY QUALITY CONTROL

Author: Nicolene Naidu

Bachelor of Biological Science (Cellular Biology), Bachelor of Medical Science (Medical Microbiology) (Honours)

Quality control (QC) in a medical laboratory refers to the statistical process that is used to detect, evaluate, reduce and correct deficiencies in the laboratories analytical process before patient results are released.

The aim of quality control is to ensure that the patient results released by a laboratory are reliable. This is done by running QC material (that approximates the same matrix as patient specimens) at specified intervals (eg. at the change of a shift) to ensure that the test system can produce reproducible results within pre-defined limits. If this can be achieved, it can be inferred that patient results are reliable, as QC material and patient specimens should be run in the same manner.

To understand Quality control, there are a few definitions it is essential to understand:

Accuracy: How close the result you get is to the actual result ie. the closeness of measurements obtained to the true value. Precision: is a measure of the reproducibility of results. It is concerned with the closeness of agreement between repeated measurements. Mean: this is the arithmetic average of a group of values (or set of data points). It is obtained by dividing the sum of all the values by the number of values. Mean = ∑xn/n where ∑ = sum xn = each value in the data set n = number of values in the data set Standard Deviation (S): this is a statistic that quantifies the degree of dispersion about the mean.

Where: s = standard deviation

x = mean (average) of the QC values n = the number of values in the data set

Σ (xn - x) 2 = the sum of the squares of differences between individual QC Values and the mean.

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Range: is the simplest measure of dispersion and refers to the difference between the highest and lowest values observed. Co-efficient of Variation (CV): is a measure of variability. It is the ratio of standard deviation to the mean and is expressed as a percentage. Signifies random error or imprecision.

CV = (S ÷ ) 100 where S = standard deviation

= mean Random error: refers to the dispersion of independent test results under specified

conditions.

Systematic error: the expressed difference between the average result obtained by

a procedure under specified conditions and an accepted reference value.

Internal quality control (ie.QC within an individual laboratory) has 3 main purposes:

1. It provides a mechanism by which the accuracy and precision of the entire

analytical process can be monitored.

2. It allows for errors that do occur in the laboratory, either due to failure of the

test system, environmental conditions or operator performance, to be

detected immediately.

3. Allows for the accuracy and precision of the test system to be monitored over

time. This could be influenced by changes in system performance,

environmental variations and inconsistent operator performance.

As mentioned, laboratories use QC material to ensure that the results released by

the lab are reliable.

Quantitative QC Materials

1. Calibrator: a solution that has a known concentration of analyte

(usually a pure substance). It is used to test and adjust the test system,

to provide a known relationship between the measurement received

and the value of the substance being measured.

2. Control: refers to the material used to monitor the stability of the

system being tested (within predetermined limits). Types of control

material include: Assayed (mean calculated by the manufacturer),

Unassayed (the lab must perform its own data analysis-cheaper control

material), In-house QC material (usually consists of pooled serum).

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QC material should have the following properties:

The analyte concentration of the QC material should be at medically

significant levels and cover the analytes clinically important range.

It should resemble the human sample and be tested in the same manner as

the patient specimens.

The material matrix (ie. the base material) should be as similar to the human

sample as possible.

QC constituents should be stable for a relatively long period of time.

Should require minimum preparation before it is ready to use.

And once opened, it should be stable for at least the period of use.

Controls independent of the manufacturer of the analyser and calibrator

should be used as the analytical values set by the manufacturer may not

provide an independent assessment of the testing system.

If QC material is obtained from the manufacturer of the analyser, then

information on its production (eg. Source, traceability, etc) should be obtained

to determine the extent of the independence of QC material from the kit

calibration process.

QC material is generally available in liquid or lyophilized (freeze-dried) forms.

HOW OFTEN SHOULD QUALITY CONTROL BE TESTED?

It depends on how long a test is stable for, but in general good laboratory practice

requires that a lab run QC at least once daily. Both “normal” and “abnormal” controls

should be run. Each lab has its own IQC protocol along with how often QC is run.

Ideally controls should be processed with each analytical run.

By regularly testing for quality control, the lab is able to create a QC database

comprising of all the QC values obtained. Daily QC results are compared to an

acceptable range of QC values that have been pre-determined by the lab (by

collecting QC data from normal and abnormal controls) to validate the test system.

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THE STATISTICAL PROCESS....

Quality control is a statistical process.

Regular testing of quality control material creates a QC database.

The laboratory uses this QC database to calculate QC statistics for each level

of QC run for each analyte.

The mean and Standard Deviation, are the most fundamental statistics used

in the lab environment.

The Clinical and Laboratory Standards Institute recommends that at least 20

data points (collected from 20 or more different runs) be used to establish statistical

target values for QC materials. In the lab, the mean helps identify a QC’s target

value from a set of QC data points.

While each manufacturer supplies their own assay values, these should be used by

the laboratory as guidelines only. Each lab should use its own QC database, to get

QC target values for each analyte specific and relevant to the individual lab.

Standard Deviation provides an estimate of the consistency of a test at specific

concentrations. The same data used to calculate the QC mean is used to calculate

standard deviation. It quantifies how close QC values are in relation to each other.

Good precision should be strived for.

Thus, the standard deviation statistic, can be used to define limits for acceptable QC

data.

Low standard deviation = consistent results

Thus a lab should strive for an analyser or QC material that provides a low

standard of deviation.

The range for the 1s limit is calculated as .... Mean ± 1s

The range for the 2s limit is calculated as .... Mean ± 2s

The range for the 3s limit is calculated as .... Mean ± 3s

For each new lot of QC material, QC statistics (ie. mean, standard deviation and co-

efficient of variation) should be calculated at data intervals of at least 20 points to

define analytic imprecision, where imprecision is a term used to express how far

apart QC values are from each other.

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All QC results should be recorded and readily available to any personnel performing

quality control. This should be accompanied or as part of a QC log, where

unacceptable QC is also documented along with the action taken to resolve outlying

QC.

QC data points are often plotted onto a graph or chart to make it easier to detect

malfunctions either due to the instrument or analytical reasons. In the medical

laboratory a Levey-Jennings chart is used to evaluate a quality control run.

Figure1 : Showing a typical Levey-Jennings Chart. This type of chart can be plotted

manually, however most analysers are programmed to plot these charts

automatically (http://www.gigawiz.com/qc.html)

The Levey-Jennings (L-J) chart plots successive QC values and commonly makes

use of the standard deviation statistic. Ranges are calculated and together with the

mean used to construct the chart. A separate L-J chart is created for each analyte

tested and each level of control. In a normal or Gaussian distribution of QC points

around the mean:

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Figure 2: Showing a “normal” bell-shaped distribution of QC points about the mean.

(http://hqcp.rndsystems.com/cgi-bin/hqCGI.cgi?select=inConcepts)

Approximately 68% of all QC values fall within ± 1s

95.5% of all QC values fall within ± 2s of the mean

99.7% of all QC values will be found to be within ± 3s of the mean.

0.3% of all QC values will fall outside ± 3s limits

ANY QC VALUE OUTSIDE THE ± 3s LIMIT IS ASSOCIATED WITH A

SIGNIFICANT ERROR – thus when QC falls outside the ± 3s limit, patient results

should not be reported.

NOTE: Westgard rules states that patient results should not be rejected if a

single QC value is outside the ± 2s limits but within the ± 3s limits (approximately

4.5% of all valid QC values fall within these limits. But it is important to follow the

rules as stated by the company’s rules. (approximately 4.5% of all valid QC

values fall within these limits).

HOW CAN ANALYSING A L-J CHART HELP US PICK UP POSSIBLE

ERRORS IN THE QC SYSTEM?

We have already mentioned systematic and random error. A gradual or abrupt

change in the mean is indicative of systematic error.

More definitions....

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Trend: refers to a sustained increase or decrease in QC values over a period of 4

or more days. The last QC value is at or outside the ± 2s limit.

Shift: refers to a sudden change in the mean of accumulated QC values. QC

data points stay consistently to one side of the calculated mean. This indicates a

shift in the distribution of control values with a new mean. Precision is not

affected.

Systematic errors can be picked up by noticing a trend or shift in mean control

values. The type of change noted (shift or trend) on the L-J chart, can help lab

personnel troubleshoot and narrow down the possible causes. Systematic errors

occur in one direction and cause all test results to be either high or low (displaces

distribution of data points away from the mean).

Figure 3: Showing examples of shifts and trends on an L-J chart (http://shs-

manual.ucsc.edu/sites/shsmanual.ucsc.edu/files/Shifts%20and%20trends%20in

%20quality%20control.pdf)

A trend may be caused by any of the following:

A problem with the analysers light source

Accumulation of debris in the sample tubing, reagent tubing or on the

electrode surfaces of the analyser

Old reagents

Deterioration of calibration

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Deterioration of QC materials

The integrity of the analysers light filter may have deteriorated

Gradual deterioration of incubator temperature in enzyme related assays.

Note that trends pick up gradual processes.

A shift may be caused by any of the following:

Changes in humidity or room temperature

Inaccurate calibration or re-calibration

Change in reagent lot or reagent formulation

Sudden change in the incubator temperature in enzyme related assays

A change in the light source or sudden failure of the light source.

Failure in the analyser’s reagent dispensing system or sampling system.

Major instrument maintenance.

Random error is basically any deviation away from the calculated result (positive or

negative). Acceptable random error refers to deviations away from the mean that

are quantified and defined by standard deviation. Unacceptable random error refers

to any QC value outside the ± 3s limits. The direction and exact magnitude of a

random error cannot be predicted (usually due to pippetting error).

Figure 4: Graphically depicting the difference between systematic and random error.

(https://www.e-education.psu.edu/natureofgeoinfo/c5_p5.htm)

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COMMON CAUSES OF RANDOM ERROR

air bubbles in the reagent

reagent lines, sampling, or reagent syringes

improperly mixed/dissolved reagent

imprecise pipetter

the power supply may be faulty/power fluctuations.

No article on QC is complete without mentioning Dr James Westgard and

Westgard rules.

Dr James Westgard published an article in 1981 about laboratory quality control.

His article introduces a system of ‘multi-rule’ QC. It consists of basic rules that are

used to increase the labs ability to detect errors faster and also reduce the incidence

of false rejection.

Westgard Multi-rules help the lab maintain a high level of certainty that its analytical

process is properly functioning.

Westgard 12s rule

A warning rule – shouldn’t reject run based on it

One of 2 QC results falls outside ± 2s

Must then evaluate the 13s rule.

The rule warns that random or systemic error may be present.

http://www.westgard.com/westgard-rules-and-multirules/pdf.htm

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Westgard 13s rule

One of 2 QC values falls outside ± 3s

Idicates random error or the beginning of a large systematic error.

Must reject run.

http://www.westgard.com/westgard-rules-and-multirules/pdf.htm

Westgard 22s rule

2 consecutive QC values for the same level fall outside the ± 2s in the same

direction OR

Both controls in the same run exceed ± 2s

Requires corrective action and patient results should not be reported.

Identifies systematic error only.

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http://www.westgard.com/we

stgard-rules-and-multirules/pdf.htm

Westgard R4s rule

One QC value exceeds the mean by – 2s and another QC value exceeds

the mean by +2s

The range between the 2 will exceed 4

Points to random error, and the run must be rejected.

http://www.westgard.com/westgard-rules-and-multirules/pdf.htm

Westgard 41s rule

Either 4 consecutive QC results for one level of control are outside ±1SD OR

Both levels of control have consecutive results that are outside ±1SD

Detects systematic error (either within or across QC material).

Warning or a rejection rule depending on the accuracy of your instrument.

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http://www.westgard.com/westgard-rules-and-multirules/pdf.htm

Westgard 10x Rule

Requires control data from previous runs

Ten consecutive QC results for one level of control are on one side of the

mean OR

Both levels of control have five consecutive results that are on the same side

of the mean

Detects systematic error.

http://www.westgard.com/westgard-rules-and-multirules/pdf.htm

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REFERENCES

1. Clinical and Laboratory Standards Institute, C24 Statistical Quality Control for

Quantitative Measurement Procedures: Principles and Definitions, Wayne PA

2. http://www.westgard.com/tqm-forl-labs-a-european-point-of-view.htm

3. http://en.wikipedia.org/wiki/Laboratory_quality_control