Quality control in clinical laboratories

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Notes on Quality control in Clinical laboratories.. By Dr. Ashish V. Jawarkar
Contact: pathologybasics@gmail.com... Facebook: www.facebook.com/pathologybasics
Web: pathologybasics.wix.com/notes

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OVERVIEW
1. Introduction
2. Terms and Definitions
1. Statistical quality control
2. Errors and mistakes
3. Preanalytic, analytic and post analytic stage
4. Precision and accuracy
5. Types of analytic errors
1.Random error
2.Systematic error
3.Total analytical error
4.Allowable total analytical error
6. Internal and external SQC
7. Control materials
8. Calibrators
3. Internal Quality control
1. Normal distribution
2. Calculation of control limits
3. Levy Jennings chart
4. The Westgard rules
5. The average of Normals method
6. Bull’s algorithm
7. Delta check method
4. External Quality control
1. Basics
2. EQAS charts and statistics
3. Precision index and coefficient of variation ratio
4. EQC normal distribution charts
5. Youden plots
6. Yundt chart
5. Quality specifications
6. Criteria for acceptable performance
7. Appendix

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 Introduction
1. The purpose of a clinical laboratory is to evaluate the patho-physiologic
condition of an individual patient to assist with the diagnosis and / or to monitor
therapy.
2. To have value for clinical decision making, an individual laboratory test result
must have total error small enough to reflect the biological condition being
evaluated.
3. Moreover nowadays, the overwhelming majority of laboratory results are being
generated by automated analysers.
4. These analysers are developed by integration of technologies; analytical
chemistry, computer science and robotics.
5. This has diminished the routine laboratory work significantly; the role of
technologists and pathologists has been shifted to ensuring that the results that
these machines give are accurate.
6. This is achieved by doing proper maintenance, quality control and calibration and
data management.

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 Terms and definitions
1. Statistical quality control / Statistical process control
1. For centuries, manufacturers have checked the quality of their products to
find out defects. At that time, every product was checked one by one, without
exception.
2. But with large scale production, it became impossible to check each and every
product manufactured.
3. Modern quality control aims to check the quality of a minimum number of
samples from the total production. This procedure is called Statistical quality
control.
4. It would also be wiser to define SQC as the process that focuses on
revealing any deviation from well defined standards.
2. Errors and mistakes
Errors: All “wrong” laboratory measurements due to “non human” actions.
They have a statistical significance.
Mistakes: All “wrong laboratory measurements due to “human” actions.
They have no statistical significance.
3. Pre analytical / analytical and post analytical stage
1. Errors and mistakes can be classified according to the time and stage which
they occur in laboratory practice –
Pre analytical stage – encompasses all procedures that occur before the
analysis of patients samples on automated analyzers (e.g. Blood drawing,
sample transportation, centrifugation, dilution etc.
Analytical stage – Includes analytical methods

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Post-analytical stage – refers to all procedures after the analysis of
patient’s samples (e.g. Transmission of data from analysers to LIS,
validation of results, printing of results, and communication of results to
clinicians/patients etc.)
MAJORITY OF PRE AND POST ANALYTICAL OUTLIERS ARE
‘MISTAKES’
MAJORITY OF ANALYTICAL STAGE OUTLIERS ARE ‘ERRORS’
2. Most of the laboratory QC processes are directed towards these analytical
stage errors – this is because
a. The analytical errors can be attributed to the laboratory staff
b. These errors can be detected by statistical methods
c. Statistical limits for analytical errors can be established
3. Examples
Preanalytical stage Analytical stage Post analytical stage
1. Inappropriate
specimen (wrong
anticoag, wrong tube,
insufficient specimen)
2. Improper preservation
3. Inappropriate patient
preparation
4. Mistake in patient
identification
1. Expired/denatured
reagents
2. Expired/denatured
control/calibrators
3. Calibration curve
time-out elapsed
4. Failure in sampling
5. Failure in reagent
aspiration
6. Change in analyser
photometric unit/flow cell
7. Analyser failure
1. Wrong matching
between lab result and
patient
2. Wrong copy from
anlyser to laboratory
report
3. Delay in delivering
result to patient/clinician
4. Loss of results
4. Precision and accuracy
1. Precision
1. Precision means repeatability or reproducibility of test results
2. In other words it is the closeness of agreement between repeated
measurements of the same sample (in a very short time, usually on the
same day)
3. PRECISION = xi - x̅
Where xi is a single measurement
x̅ Is average of successive measurements

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2. Accuracy
a. Closeness of agreement between the value obtained by analyser and true
value of the sample
5. Types of analytical errors
1. Random error
a. Result of measurement minus the mean that would result from infinite
measurements of the same sample (see precision – infinite is random
error)
b. RE = xi - x̅ I , where x̅ i is mean of infinite number of measurements
c. Random error affects precision
d. RE is always greater than zero
e. RE can be decreased by increasing the number of measurements
f. It can be attributed to undetermined reasons (inherent error)
2. Systematic error
a. Result of mean that would result from infinite number of
measurements minus the true value of the sample
b. SE = x̅ I - µ , where µ is the true value of the sample
c. Systematic error cannot be decreased by increasing the number of
measurements.
d. It can be attributed to certain reasons and therefore can be eliminated
much easier than random error i.e. it can be zero.

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3. Total analytical error
TE = RE + SE
a. As we have seen from definition of RE, it can never be zero, hence TE can
never be zero.
4. Total allowable analytical error (aTE)
a. Since TE>0 is unavoidable, TE of every single determination must be
lower than a specified limit. This limit is called aTE.
6. Internal and external SQC
a. Random and systematic errors should be detected at an early stage
b. Two statistical methods can help in detection of these errors
1. Internal SQC
a. Performed every day by the laboratory personel with control materials
b. It detects basically the precision
2. External SQC
a. Performed periodically, usually by a third party
b. It checks primarily the accuracy
7. Control materials
a. Control samples are pools of biological fluids
b. They contain analytes which are determined by the laboratory in
concentrations which are close to decision limits where medical action is
required
c. They are usually prepared by the equipment manufacturer / reagent
manufacturers
d. They are available in different levels (concentrations) – these check
performance of laboratory methods over their entire range

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8. Calibrators
1. Calibration is the process of evaluating and adjusting the precision and
accuracy of measurement equipment.
2. For this purpose, reference standards with known values for selected
points covering the range of interest are measured with the instrument in
question. Then a functional relationship is established between the values
of the standards and the corresponding measurements.
3. Enlisting exact steps of calibration is beyond the scope of this document.

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 Internal Quality control
1. Normal Distribution
1. Normal or Gaussian distribution (N) is the basis of SQC theory. Distribution chart is a
biaxial diagram (x/y).
2. X-axis represents the values of a variable’s observations and y-axis the frequency of
each value (the number of each value’s appearance).
3. It has a bell-shaped form with its two edges approaching asymptomatically the x-axis.
4. The highest point of normal distribution corresponds to the value with the higher
frequency (mode value). It is always on the top of every
distribution curve.
5. Median value (M) is the value which divides the variable’s observations in two equal
parts. It represents the “center” of the distribution.
6. Mean value or average value (μ or x is equal to the value which all the observations
should have if they were equal. The mean value (µ) or average can be calculated by
the next formula:
Where: xi = Single value, Σxi = Sum of values, N = Total number of values
7. In a normal distribution, mean, median and mode coincide.

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8. Variance – The length of distribution curve defines the variance of the variable. The
most common measure of variance is Standard deviation (SD)(s).
Standard deviation can be calculated by the following formula –
The distance between the upper (UL) and lower limit (LL) of a normal distribution is
six standard deviations (6s). Since mean value is in the center of normal distribution, the
total range of a normal distribution is μ ± 3s (to be more exact, not all, but nearly all
(99.73%) of the values lie within 3 standard deviations (3SD) of the mean).
9. Z Score - Mean and SD allow for calculation of distance of each observation from the
centre (mean). The distance is called the Z score.
It can be calculated by the following formula:
For example we are looking at a distance of value xi = 80 from the mean of the normal
distribution N~(100,5)
Here Z score = 80-100 = -4
5
10. Every normal distribution can defined as N~(μ, s).
For instance N~(76, 2.3) means a normal distribution with mean value = 76 and
standard deviation = 2.3.
11. The distance between upper limit and lower limit of a normal distribution is six
standard deviations (6s). Since mean value is in the center of normal distribution, the total
range of normal distribution is (µ +/- 3s).

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12. The empirical rule of normal distribution
µ+/- s Contains 68.26% of observations
µ+/- 2s Contains 95.46% of observations
µ+/- 3s Contains 99.73% of observations
13. Coefficient of variation (Cv)
Standard deviation depicts variation in the same units as the mean. Hence it
cannot be used to compare distributions with different mean.
In such cases Cv can be used. It is the ratio of standard deviation to mean
expressed as a percentage.

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2. Calculation of normal limits
1. The automated analyzer is calibrated with reference material (calibrator) and
validation of calibration is done with External quality control / Inter Lab
comparison.
2. In internal SQC two or more control level samples are assayed every day at least
once per day before the patient’s samples. Then laboratory checks if all control
values lie within the control limits. If at least one of the two control limits is
outside of one of the two control limits, then further actions may be required until
random or systematic errors are under control.
3. The laboratory staff collects 20-30 successive measurements from any control
level.
4. Standard deviation (s) and mean value (µ) are calculated. Range (µ+/- 3s) is
considered as trial limits (any outlier is rejected).
5. This calculated mean value is then taken as true value of daily controls. Their
standard deviation is the inherent error of the system.
3. Levy Jennings Chart
1. Rotate the normal distribution curve clock wise
2. Draw seven lines from the points µ+ 3s, µ+ 2s, µ+ s, µ, µ - s, µ- 2s, µ- 3s.
3. Values obtained are plotted in the chart date wise.
4. For every different parameter and different level, different LJ chart is plotted.
Random and systematic error in LJ chart
1. If any of the daily control values exceeds 3SD limits, it is a random error.
2. Detection of systematic error is more complicated.
3. In systematic errors two or more successive control values exceed the control limits
which can respectively be 3SD, 2SD or 1SD.

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4. The Westgard Rules
1. Westgard rules are a type of quality criteria used for error detection.
2. They are denoted as AL where A is the number of control values and L is the
control limit.
RULE GRAPH INTERPRETATION
13S One control value lies over
or under 3SD
- Random error
Patient results should be
blocked, and root cause
analysis should be done.
22S Two successive control
values lie between 2SD
and 3SD
- Systematic error
12S One control values lie
between 2SD and 3SD
- Random error
Patient results need not be
blocked, only caution is
warranted.
101S Ten consecutive control
values are on the same side
of the mean
- Systematic error

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5. Average of normals method (AON)
1. LJ chart and Westgard methods are based on analysis of control samples, but
even control samples determination has some disadvantages like –
a. It is costly
b. It is time consuming
2. These disadvantages can be minimized by some other methods – like AON
method.
3. AON method is based on the principle that mean value of all normal results
fluctuate between well defined limits. LJ and Westgard detect random and
systematic errors. AON method detects only systematic error.
4. This method is mostly used for biochemistry analysers.
Method
1. The laboratory collects data for an anaylate from a fixed number of healthy
persons. Its mean value and standard deviation is calculated. This value will be
used as control value.
2. The standard error of these normal samples run daily is calculated with the
following formula.
s = standard deviation, N is number of samples
3. The confidence interval is calculated as follows
4. This confidence interval will be used for definition of control limits of the
method.
5. Every day laboratory calculates the mean value of N normal results, this mean
value is symbolized as AON and is calculated by the following formula –
6. If AON exceeds the control limits, the anaylate’s determination has a systematic
error.

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7. In daily practice AON method has its own control chart which detects only
systematic errors. In AON chart each dot represents a daily mean value of the
same anylate.
6. Bull’s Algorithm
1. The statistical quality control carried out in hematology analyzers has many
important differences from the corresponding techniques in the clinical
chemistry analyzers.
2. These differences are due to reasons such as the high stability of cytometry
technology, the small biological variation of some hematology parameters, the
big reagent vials and the small time lasting of the hematology controls.
3. Because of the above reasons Levey-Jennings charts in hematology analyzers
are different from corresponding charts in clinical chemistry. For instance the
hematology Levey-Jennings charts have only three lines (upper and lower limits
and central line). The reason is that these Levey-Jennings charts are not created
statistically from a normal distribution of former quality control data, which is
not possible because of the very small variation of hematology quality control
values. In hematology analyzers the upper and lower control limits act as the
“specification’s limits” in industry quality control.
4. The small biology variation of many hematology parameters made many
researchers to established quality control methods based only on patients results.
Such suitable parameters are the erythrocyte indexes (MCV, MCHC, MCV)
with the smaller biological variation (due not only to biology but mostly to the
hematology analyzer’s technology).
5. These attributes of them inspired Brian Bull (an American Hematologist) to
establish a new quality control method widely known as “Bull’s algorithm”.
6. Bull’s algorithm (also known as method) detects systematic errors in MCV,
MCHC and MCV and consequently in HgB, Hct and RBC. His method is a kind
of moving average. Its main idea is to estimate the mean value of the last twenty
patients’ values, including in them the mean value of the batch of the previous
twenty values.
7. The algorithm itself is a quite complicated equation which eliminates the outliers
and estimates the moving average of the last twenty values. Bull’s algorithm has

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been proved quite effective in detecting small systematic errors (almost 1%) not
only in erythrocyte indexes but also in almost all the hematology parameters. It
uses all patients’ data without exception. The last fact made Bull’s algorithm the
cheapest quality control method in laboratory medicine.
8. Hematology quality control samples last only 20 – 30 days and are very
expensive, when, on the other hand, whole blood samples are stable in the
refrigerator for 24 hours.
9. These facts led some researchers to find methods which are based on the
repetitive analysis of patient samples. These methods are known as “retained
patient specimens”.
10. In 1988 Cembrowski (Canadian clinical chemist) established the most effective
“retained patient specimens” method. It was based on the repetitive analysis of
the same patient samples between two successive days. His method is known as
“m/nlim”
- “Lim” stands for the quality control limit. It is equal to the double of the
standard deviation of the repetitive analysis (2 x SD).
- “n” stands for the number of patients’ samples which will be analyzed twice.
- “m” stands for the portion of “n” number of samples which is permitted to be
out of limits (“lim”).
Statistical simulations created by Cembrowski proved the effectiveness of his
method. According to him the best combination of “m”, “n” and “lim” is 2, 3, 2 or
2/32s.
Concluding, three different methods are in the disposal of the laboratory in order to
detect the analytical errors in hematology laboratory.
Levey-Jennings detects systematic and random errors. On the contrary, Bull’s
algorithm and “retained patient specimens” detect only systematic errors, but they
have the advantage of the low cost. Laboratory can choose the best combination of
the three
7. Delta check method
1. The AON and Bull’s algorithm detect systematic errors using a group of
patients.
2. Delta check method is used for detecting random errors using previous values of
individual patients.
Delta check = (current value) - ( previous value)
Delta check % = (Current value – Previous value) * 100
Current value

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3. The delta check values should vary between two limits which are called ‘Delta
check limits’. In order to calculate them we must take into consideration the
reasons for delta differences –
a. Intra individual biological variation of the analyate CV1
b. The analytical variation (Smeas) can be easily estimated by the control
values
c. The pre-analytical variation (CVpre-analytical)

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 External Quality Control
1. Basics
1. EQC refers to the process of controlling the accuracy of an analytical method
by interlaboratory comparisons.
2. The EQAS co-coordinator prepares a sample pool and sends to the
participants of the scheme one or two samples from the same pool.
3. The samples are assayed by the laboratories using the same equipment and
reagents as they do in routine for patient determinations.
4. The EQAs coordinator gathers all the results and they group them (peer
groupas) according to laboratory analytical methods, analyzers or any other
criteria.
5. Then the coordinator calculates the target value (consensus mean) and its
total variation (standard deviation) of the laboratories results.
6. If any of the laboratories have values outside of the control limits, then this
laboratory is considered ‘out of control’.
7. The out of control laboratories get an indication that there is some problem
with their analysis.
2. Understanding EQAS charts and statistics
a. Standard Deviation Index
1. Standard Deviation Index calculates the distance of laboratory
results from the consensus mean.
2. It quantifies the inaccuracy of the analytical method.
3. It is similar to Z score and calculated by the following formula –
SDI = laboratory result – Mean value of peer group
Standard deviation of peer group
4. The SDI value of each laboratory can be located on proper SDI chart
as follows:

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5. Control limits of SDI are zero+/- 2SDI
6. Four rules are usually employed for SDI evaluation:
a. 2/51SDI – Two from five successive control limits exceed
1SDI, it is a warning rule
b. x̅ 1.5SDI – The mean value of five SDI values exceeds the limits
+/- 1.5SDI. It reveals a lasting systematic error.
c. 13SDI – One value exceeds 3SDI
d. R4SDI – The range between the lower and higher SDI values
exceeds +/- 4SDI
b. Precision index (PI) and coefficient of variation ratio (CVR)
Precision index = Standard deviation of laboratory
Standard deviation of peer group
CVR = CV of laboratory/month
CV of peer group / month

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c. EQC normal distribution charts
1. More often than not, EQAS coordinators represent graphically the
total performance of all the laboratories in a normal distribution
chart.
2. This chart is usually a histogram.
3. The EQAS coordinators usually group the laboratories according to
their analytical methods and their automated analyzer. Histograms
containing two or more peer groups have bars with two or more
different colors respectively.

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d. Youden plot
1. Many EQAS schemes use control samples of different levels in
order to check the performance of the analytical methods.
2. Youden plot is a rectangular chart of which the four angles
correspond to the control limits of the two control levels (-4SD and
+4SD).
3. The acceptable part is the gray zone and the rejected part has
different colors.
4. Each dot represents a different laboratory and therefore Youden plot
describes the whole EQAS scheme.
e. Yundt chart
1. Yundt chart helps to illustrate performance of an analytical method
across all its measuring range.
2. It needs atleast three control levels to be plotted.
3. If the line across the dots of three levels is a straight one, then
laboratory has very good linearity.

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4. If the line across the dots is not straight, the linearity of the method
has several issues.

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 Quality Specifications
1. SQCs goal is to detect random and systematic errors or better, to ensure that
total error is lower than total allowable error.
2. aTE depends on certain characteristics of analyate and analytical method like
imprecision, bias, biological variation etc.
3. According to these characteristics some analyates need more or less rigorous
SQC rules than others.
There are two common practices:
United states: depending of analytical performance of the method. This
practice is followed in US. The laboratories follow CLIA regulations.
Europe: aTE depends biological variation of the analyate.

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 Criteria for acceptable performance
CV1 - intra individual variation
CV2 – inter individual variation
CVw-day – within day variation
CVb-day – between days variation
CV2
total = CV2
wday + CV2
bday
1st
Criterion of acceptable performance
CVtotal </= 0.5 CV1
2nd
Criteria for acceptable performance
CVwday </= 0.25TE%
3rd
criteria for acceptable performance
CV total </= 0.33 TE%
4th
Bias% < 0.25 CV2
1 + CV2
w
5th
TE% </= k 0.5 CVw + 0.25 CV2
1 + CV2
w
For k =1.65
TE% </= 1.65 *RE* SE

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 APPENDICIES

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Quality control in clinical laboratories

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