1. Laboratory Quality Control Prof.Dr.Moaed E.AlgazallyBy
Prof.Dr.Moaed E.Algazally
The concept of quality control in the Clinical Chemistry Laboratory is well established, and it is now a routine requirement to include quality control samples in each batch of tests performed.   
Laboratory staff must be conscious of how the quality of their work affects the medical diagnosis and treatment of patients.
Every biochemical analysis should provide the answer to a question which the clinician has posed about the patient.
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2. Laboratory Quality Control
The quality of laboratory work affects the medical diagnosis and the treatment of patients
It is a routine requirement to include quality control samples in each batch of tests performed in the lab

3. Definition of Quality Control
The process of detecting errors
Errors can occur even in the best of laboratories
Good quality control will provide the clinician with a high degree of confidence in the clinical data generated by the lab
By definition, Quality Control, is the action of detecting errors - and these errors can occur even in the best of laboratories.
Good quality control will provide the clinician with a high degree of confidence in the clinical data which forms the basis of the diagnosis.
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4. Definition of Quality Assurance
The systems or procedures in place to prevent errors occurring
Should not be confused with Quality Control
The term Quality Control is often confused with Quality Assurance - which is in effect the quality systems and procedures in place to avoid these errors occurring in the first instance.
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5. Quality Control and Quality Assurance
2 complementary systems
A good laboratory will have both these systems working together…
…to ensure the reliability of the test results to give the best patient care!
A good laboratory will have both these complementary systems working together to ensure the reliability of the test results and ultimately to give best patient care.
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6. Unreliable Performance?
Potential consequences include:
patient misdiagnosis
delays in treatment
increased costs
From avoidable follow up tests
In the US alone, avoidable retests cost $200million USD per year
From administration of inappropriate drug therapy
So what are the potential consequences of unreliable performance ?
The patient could be misdiagnosed, or there could be delays in treatment.
The cost to the health care system increases as repeat or other confirmatory tests will have to be run.
In the US alone the cost of avoidable follow-up tests could be as high as 200 million USD per year.
Studies have indicated that even a small calibration bias can have a dramatic effect on the diagnostic process.
For example, a 1% change in bias in the running of a cholesterol test could result in a 5% change in the number of patients exceeding the 200mg/dL cut-off value.
This could rise to 15% with a bias of 3%.
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7. Unreliable performance?
Even a small calibration bias can effect treatment rates:
1% +ve bias in cholesterol result
5% increase in patients exceeding the treatment cut-off (200 mg/dl)
3% +ve bias
15% increase in patient treatment.

8. Error Classification
Quality Assurance considers diagnostic errors under 3 main headings:
Pre-analytical:-
errors before the sample reaches the laboratory
Analytical:-
errors during the analysis of the sample
Post-analytical:-
errors occurring after the analysis
There are a huge variety of potential errors which can affect the quality of the laboratory results.
Effective Quality Assurance will consider them under the three main headings:-
pre-analytical
analytical
post-analytical
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9. Sample Receipt and Accessioning Quality Control Testing
CPHL/QCU
Complexity of a Laboratory System
50%
Preexamination
Reporting
Patient/Client Prep
Sample Collection
Personnel Competency
Test Evaluations
*Data & Laboratory *Management
Safety
*Customer *Service
30%
Postexamination
Sample Receipt and Accessioning
Record Keeping
Sample Transport
Quality Control Testing
Examination
20%

10. Pre - Analytical Errors
Although they occur before the sample reaches the lab, they directly affect the quality and usefulness of the result
There are many types of pre-analytical error
Pre - Analytical errors can occur before the sample ever reaches the laboratory but directly affect the quality and clinical usefulness of the final result.
These could include:-
Improper Preparation of the Patient:-
For example, a glucose test provides more useful data following a period of fasting prior to blood collection.
Patient stress or anxiety may affect certain parameters such as urinary protein levels.
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11. Pre-analytical Errors
Improper preparation of the patient
Patient fasting
A glucose test provides a more useful result after a period of fasting
Stress and anxiety
Urinary protein levels will be affected

12. Pre - Analytical Errors
Improper collection of the blood sample
Sample haemolysis
Will affect tests such as LDH, potassium and inorganic phosphate
Insufficient sample volume
The lab may not be able to carry out all tests requested
Collection timing
Collecting an accurately timed volume of urine is extremely important when looking at analyte levels in a 24 hour urine sample
Improper Collection of the Blood Sample:-
For example, if the sample is haemolysed, this will effect many tests such as LDH, potassium or inorganic phosphate.
Insufficient sample, may make it impossible for a laboratory to measure all of the tests requested.
Errors in collection timing.
For example, the biggest error in the measurement of any analyte in a 24 - hour urine specimen is collecting an accurately timed volume of urine.
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13. Pre - Analytical Errors
Incorrect specimen container
Serum or plasma
Serum is obtained from clotted whole blood & plasma from unclotted blood
Sample collection for plasma must be done into a tube containing anticoagulant such as EDTA or heparin
Fluoride tubes for glucose
To inhibit glycolysis
Otherwise, the time taken to reach the lab will have a significant effect on the results
EDTA unsuitable anti-coagulant for calcium
EDTA binds calcium
Incorrect Specimen Container:-
For example the choice of the correct tube for the collection of serum or plasma.
Samples for glucose should be collected in a tube containing fluoride which inhibits continued glycolysis.
Otherwise the time taken for the sample to reach the laboratory before analysis will seriously affect the results.
Samples collected with EDTA as an anticoagulant should not be used to perform calcium assays, because of its calcium binding properties.
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14. Pre - Analytical Errors
Incorrect specimen storage
Sample left overnight at room temperature
Falsely elevated potassium, phosphate and red cell enzymes (e.g. AST & LDH)
Due to leakage of the intracellular fluid into the plasma
Delay in sample delivery
Falsely lowered levels of unstable analytes such as NEFA
Unstable analytes require fast handling and analysis
Incorrect Specimen Storage:
A blood sample left overnight before being sent to a laboratory will result in falsely elevated potassium, phosphate and red cell enzymes such as AST and LDH due to time dependent leakage of the intracellular fluid into the plasma.
Similarly, a delay in sample delivery may cause falsely lowered values for a particularly unstable analyte such as NEFA.
Unstable analytes therefore require fast handling and analysis.
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15. Other Factors The sex of the patient The age of the patient
male or female
The age of the patient
new born / juvenile / adult / geriatric
Dietary effects
low carbohydrate / fat
high protein / fat
When the sample was taken
early morning urine collection pregnancy testing
Patient posture
urinary protein in bed-ridden patients
Other factors and patient information that may effect interpretation of results include:-
The Sex of the patient.
Age of the patient.
Dietary effects.
When the sample was taken, for instance an early morning urine
Patient Posture.
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16. Other Factors Effects of exercise Medical history Pregnancy
creatine kinase / CRP
Medical history
heart disease / diabetes / existing medication
Pregnancy
hormonal effects
Effects of drugs and alcohol
liver enzymes / dehydration
Effects of Exercise.
Medical History.
Effects of Pregnancy.
Effects of Alcohol or Drug Abuse.
It may appear on the surface, that these errors have little to do with quality control or quality assurance within the laboratory.
However the quality of the final result, will be seriously affected by these outside factors.
Therefore it is the responsibility of the laboratory to minimise such risks, by collating adequate information, establishing effective standard operating procedures and providing training for the people using the laboratory service.
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17. How do these factors come under the banner of Quality Assurance?
The quality of the final result will be seriously affected by these outside factors
The lab must minimise these risks
Establishing effective standard operating procedures (SOPs)
Providing training for people using the laboratory service

18. Analytical Errors The sample: Incorrect labelling
Barcoding / aliquoting
Incorrect preparation
Centrifugation / aspiration
Incorrect storage
Short-term refrigeration
Medium term freezing at -20ºC
Long term freezing at -80ºC
Correct test selection
Laboratory Information Management System (LIMS)
Once the sample arrives in the laboratory a wide variety of potential analytical errors during the performance of the test may affect the quality of the results obtained.
The incorrect preparation, storage or labelling of samples.
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19. Analytical Errors Glassware / pipettes / balances: Used incorrectly
Contaminated
Poorly calibrated
Reuse of pipette tips
Errors may arise in conjunction with the use of supplementary analytical equipment such as glassware, pipettes, and balances.
Are these being used incorrectly, are they properly washed or poorly calibrated ?
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20. Analytical Errors Reagents / calibrators / controls: Poor quality
Inappropriate storage
Incorrect temperature
Poorly maintained fridges or freezers
Use of domestic freezers for storage of frozen control materials
Stability
Use outside the shelf-life / working stability period
Incorrect preparation
E.g. reconstitution of lyophilised materials
The quality of the reagents used to perform the tests together with the relevant calibrators and controls will have a critical influence on the reliability of the data generated.
When in use are they being correctly prepared, stored properly when not in use, or are they being used outside the recommended shelf-life or working stability.
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21. Analytical Errors The application: Incorrect analytical procedures
Poorly optimised instrument settings
The above will lead to errant results with even the best quality reagents
No matter how good the reagents are, if they are not used in accordance with the recommended analytical procedures or are run on an analyser with poorly optimised settings, errors will occur.
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22. Analytical Errors The instrument: Operational limitations
Temperature control
Read times
Mixing
Carry-over
Lack of maintenance
Worn tubing
Optics
Cuvettes
Probes
The analytical instrumentation used to perform the tests will obviously have an important effect on potential errors.
The instrument’s design may limit it’s operational capabilities.
The analyser will not perform at its best if it is not looked after and regularly maintained.
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23. Other Factors Calculation errors: Transcription errors
incorrect factor / wrong calibration values
Transcription errors
Dilutions errors:
Dilutions may be done when a sample value exceeds the assay linearity
incorrect dilution or dilution factor used
Lack of training
The human factor:
tiredness / carelessness / stress
Again various other factors can result in the generation of mistakes.
These could include errors in the calculation or transcription of results.
When sample dilutions are required due to linearity limitations, is the dilution performed accurately and results multiplied up by the correct factor.
Has the technician undergone sufficient training and gained enough experience to allow him to perform the analysis or use the instrument with confidence.
The human factor can also be a problem with tiredness, carelessness and stress all influencing performance.
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24. Post - Analytical Errors
The prompt and correct delivery of the correct report on the correct patient to the correct doctor
How the Clinician interprets the data to the full benefit of the patient
Once the tests have been performed potential post - analytical errors can come into play.
These are essentially concerned with the prompt and correct delivery of the correct report, on the correct patient, to the correct doctor.
And finally how the clinician interprets the data he has received to the full benefit of the patient.
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26. Pre-analytical processes
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27. Analytical processes
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28. Post-analytical processes
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29. Definitions…
There are several common terms used when analysing laboratory performance
Accuracy
Precision
Specificity
Sensitivity
Quality Control has a 'language' all of it's own and there is terminology and basic statistical parameters which must be clearly understood by all laboratory staff.
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30. How correct your result is
Accuracy?
How correct your result is
Accuracy refers to the agreement between your value and the 'true' value, that is how correct your result is.
Accuracy is generally measured by direct comparison to a reference value or more commonly by using assayed quality control serum, with an accurate value assigned by the manufacturer.
When analysed the closer your result obtained is to this target value, the greater your accuracy.
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31. Accuracy? The agreement between your value and the ‘true’ value
Determined absolutely by direct comparison to a reference value
More commonly assessed by using an assayed control serum, with accurate values assigned by the manufacturer
The closer your result to the target value, the greater your accuracy

32. The reproducibility of your results
Precision?
The reproducibility of your results
Precision refers to the reproducibility of your results, or the agreement between replicate measurements.
The closer your results, are to each other, for the same analyte in the same serum, the better your precision.
When evaluating a method, precision should be assessed in terms of within run performance (Intra-assay precision) and between run performance (Inter-assay precision).
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33. Precision?
The reproducibility of your results (i.e. the agreement between replicate measurements)
The closer your results are to each other, for the same analyte, in the same serum, the better your precision
There are 2 ways in which precision is assessed:
Within run performance (intra-assay precision)
Between run performance (inter-assay precision)

34. Accuracy & Precision - Example 1
Accurate and precise
The ideal situation
Repeat results are close to one another
Mean is close to the ‘true’ value
Lab can have confidence in single test results
No need to continually repeat tests
Ideally a laboratory should be striving for both good accuracy and precision.
In other words, you should be able to get your repeat results close to one another, and the mean of those results should be close to the 'true' value.
If a laboratory has confidence in both its accuracy and precision then it should be able to rely on single test analysis and not have to continually repeat tests until they feel they have a correct result.
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35. Accuracy & Precision - Example 2
Imprecise but accurate
Results are widely spread, giving poor precision
The mean is close to the ‘true’ value, giving apparently good accuracy
An unacceptable situation
Labs cannot waste resources on repeat runs to get an acceptable level of accuracy
However, you may have a situation where your results are widely spread giving you poor precision, but the mean of your results is close to the 'true' value giving you apparently good accuracy.
In a busy laboratory this is obviously unacceptable as time and resources cannot be wasted on having to run each sample several times to get an acceptable level of accuracy.
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36. Accuracy & Precision - Example 3
Precise but inaccurate!
Results are close together, giving good precision
Mean is not close to the ‘true’ value, giving poor accuracy
You could also get a situation where you have good precision yet poor accuracy:
Your results are close together giving you good precision but the mean of your results is not close to the 'true' value, thus accuracy is poor.
Generally poor accuracy is relatively easy to solve and would tend to reflect a calibration problem.
If, for example, you find that you are always low on albumin by 2 g/L, the standard or calibrator should be reassessed, to bring everything into line.   
Poor precision, however, is often more difficult to solve.
If you find that the spread of values is wide, for a particular analyte it may be due to a variety of problems, such as poor quality of reagents, instrument condition, technologist training etc. as previously discussed.
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37. Solving precision and accuracy problems
Poor accuracy relatively easy to solve
Often a calibration problem
Poor precision more difficult
A variety of causes
Poor quality reagents
Badly maintained instruments
Inadequate training

38. Specificity?
The ability of a method to measure solely the component of interest
A lack of specificity will affect accuracy
The test is measuring components other than the analyte of interest
The specificity of the assay has an important effect on the accuracy of the results obtained.
Specificity refers to the ability of a method to measure solely the component of interest.
A lack of specificity could lead to a falsely elevated result where the test is measuring components other than the analyte of interest.
This is a particular problem when trying to distinguish structurally similar hormones or drugs.
Similarly a falsely low result could be obtained where the test does not measure the analyte completely 100%.  
For example the lack of specificity of the bromocresol purple method (BCP) for measuring bovine albumin in quality control serum.  
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39. Specificity? Consequences of a lack of specificity
Falsely elevated values may occur
Structurally similar hormones
FSH, LH, TSH & hCG all have an identical alpha-subunit
Drugs (both therapeutic drugs and Drugs of Abuse)
Falsely low values may also occur
Bromocresol Purple (BCP) method with bovine albumin
The test does not measure the analyte 100%
Bovine QC serum cannot be used with this method
The specificity of the assay has an important effect on the accuracy of the results obtained.
Specificity refers to the ability of a method to measure solely the component of interest.
A lack of specificity could lead to a falsely elevated result where the test is measuring components other than the analyte of interest.
This is a particular problem when trying to distinguish structurally similar hormones or drugs.
Similarly a falsely low result could be obtained where the test does not measure the analyte completely 100%.  
For example the lack of specificity of the bromocresol purple method (BCP) for measuring bovine albumin in quality control serum.  
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40. Sensitivity?
The ability to detect small quantities of a measured component
Will affect both precision and accuracy at the bottom end of the clinical range
How is sensitivity established?
By determining at what point an assay’s precision reaches an unacceptable level
Sensitivity is the ability to detect small quantities of a measured component, and will subsequently affect both precision and accuracy, when attempting to measure levels at the bottom end of the clinical range.
The sensitivity of an assay is established by determining at what point its precision and ultimately the accuracy of the test reaches an unacceptable level.
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41. Statistics… Normal distribution Standard Deviation
Coefficient of Variation
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42. Normal Distribution (Gaussian Curve)
Values fall randomly about a mean value
The mean (x) is the average of the set of values
Equal numbers of results lie above and below the mean
If a particular parameter is analysed repeatedly, and the values obtained obey the laws of probability, you will find that the values fall randomly about a mean value.
Where the mean (X) is the average result of the set of values.
When the results obtained are plotted against the frequency, we get the classical Gaussian Curve or Normal Distribution, with equal numbers of results above and below the mean.
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43. Precision? How disperse the values are
How is precision quantified statistically?
By measuring the Standard Deviation (SD) of the set of results
The Precision of the method, is expressed in terms of how disperse the values are.
Statistically this is quantified by the measurement of the standard deviation (SD) of the set of results.
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44. Standard Deviation (SD)
SD is defined as the square root of the sum of the squares of the single value deviations from the mean, divided by the number of the values minus one (and is quoted in the same unit of measurement)
It is defined as the square root of the sum of the squares of the single value deviations from the mean, divided by the number of the values minus one.
This is in effect the average deviation for the set of results.
 A standard deviation is quoted in the same unit of measurement and the lower the SD the better the precision.     
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45. What does Standard Deviation tell us?
The average deviation for the set of results
The lower the SD, the better the precision

46. Standard Deviation example
Mean result (x) = 100 mmol/L
Standard deviation (SD) = 1.0 mmol/L
Number of results (n) = 100
If we use the following data as an example. 
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47. Mean ±1 SD
By the laws of statistical probability, 68% of all results should fall within ± 1 SD of the mean
In this example, 68% of results fall within the range 99 – 101 mmol/l

48. Mean ± 2 SD
By the laws of statistical probability, 95% of all results should fall within ± 2 SDs of the mean
i.e. 19 out of 20
In this example, 95% of results fall within the range 98 – 102 mmol/l
Statistically, it is acceptable for 5% of results to fall outside this range

49. Which is more Precise? Potassium SD = 0.1 mmol/L
Sodium SD = 2.0 mmol/L
It’s impossible to say from this information alone!
What other information is required?
The magnitude of the results
From the information given, is it possible to determine which of these two methods is performing more precisely ?
What other information do we require ?
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50. Coefficient of Variation
A %CV takes into consideration the magnitude of the overall result
The standard deviation does not take into consideration the magnitude of the overall results, which in this case are quite different.
By calculating the coefficient of variation (%CV), which in effect expresses the SD as a percentage of the mean, it is possible to compare the precision of different sets of data. 
The lower the %CV the better the precision.
The %CV expresses the SD as a percentage of the mean
The lower the %CV, the better the precision
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51. Potassium (mean = 5.0 mmol/l) %CV = (0.1 / 5.0) x 100% = 2.0%
Example:
Potassium (mean = 5.0 mmol/l)
%CV = (0.1 / 5.0) x 100% = 2.0%
Sodium (mean = 140 mmol/l)
%CV = (2.0 / 140) x 100% = 1.4%
Assuming a mean of 5.0 mmol/L for potassium and a mean of
140 mmol/L for sodium, the CVs would be as follows:
Thus although potassium has a smaller SD, sodium has the better CV and in this example, is performing better than potassium.
Sodium has the better CV, and in this case is performing better than potassium
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52. Why use a %CV for analysis of results?
It allows comparison of precision on different sets of data, with different magnitudes