1. Laboratory quality control is important to ensure accurate medical diagnoses and treatment of patients. It involves detecting errors through quality control samples and providing confidence in clinical data. 2. Errors can occur at various stages of testing including pre-analytical (before testing), analytical (during testing), and post-analytical (after testing). Inaccurate results can mislead diagnoses and treatments. 3. Quality control and quality assurance work together to minimize errors. Quality control detects errors while quality assurance prevents errors through proper systems and procedures. Together they aim to ensure reliable test results for optimal patient care.

1. Laboratory Quality
Control
By
Prof.Dr.Moaed E.Algazally

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
• 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

4. Definition of Quality Assurance
• Quality Assurance:
– The systems or procedures in
place to prevent errors
occurring
– Should not be confused with
Quality Control

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!

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

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

9. Complexity of a Laboratory System
*Data &
Laboratory
*Management
Safety
*Customer
*Service
Patient/Client Prep
Sample Collection
Sample Receipt and
Accessioning
Sample Transport
Quality Control
Testing
Record Keeping
Reporting
Personnel Competency
Test Evaluations
50%
20%
30%
CPHL/QCU

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

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

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

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

15. • The sex 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

16. • Effects of exercise
– creatine kinase / CRP
• Medical history
– heart disease / diabetes / existing medication
• Pregnancy
– hormonal effects
• Effects of drugs and alcohol
– liver enzymes / dehydration
Other Factors

17. How do these factors come under the
banner of Quality Assurance?
• The lab must minimise these risks
– Establishing effective standard
operating procedures (SOPs)
– Providing training for people using
the laboratory service
•The quality of the final result will be
seriously affected by these outside factors

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)

19. Analytical Errors
• Glassware / pipettes / balances:
– Used incorrectly
– Contaminated
– Poorly calibrated
– Reuse of pipette tips

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

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

22. Analytical Errors
• The instrument:
– Operational limitations
• Temperature control
• Read times
• Mixing
• Carry-over
– Lack of maintenance
• Worn tubing
• Optics
• Cuvettes
• Probes

23. Other Factors
• Calculation 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

24. • How the Clinician interprets the data to the full
benefit of the patient
Post - Analytical Errors
•The prompt and correct
delivery of the correct report
on the correct patient to the
correct doctor

26. Pre-analytical
processes

27. Analytical processes

28. Post-analytical
processes

29. Definitions…
• There are several common terms used
when analysing laboratory performance
– Accuracy
– Precision
– Specificity
– Sensitivity

30. Accuracy?
How correct your
result is

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. Precision?
The reproducibility of
your results

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

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

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

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

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

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

41. Statistics…
• Normal distribution
• Standard Deviation
• Coefficient of Variation

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

43. Precision?
• How disperse the values are
• How is precision quantified statistically?
– By measuring the Standard Deviation (SD)
of the set of results

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)
)
1
-
n
x)
-
(xi
(
=
SD
2


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

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

50. Coefficient of Variation
A %CV takes into consideration the
magnitude of the overall result
100%
x
(x)
Mean
SD
=
CV
The %CV expresses the SD as a percentage
of the mean
The lower the %CV, the better the precision

51. Example:
Sodium has the better CV, and in this case is
performing better than potassium
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%

52. Why use a %CV for analysis of results?
• It allows comparison of precision on
different sets of data, with different
magnitudes