The document discusses the procedures and metrics involved in quality control within medical laboratories, emphasizing the importance of monitoring analytical processes to avoid reporting incorrect patient results. It defines key terms such as accuracy, precision, bias, and various error types, while also detailing how to compute and interpret statistical measures like mean, standard deviation, and coefficient of variation. Additionally, it outlines specific Westgard rules that assist in identifying systematic and random errors in quality control data.

1. INTERNAL QUALITY CONTROL
PRESENTED BY

2. WHAT IS QUALITY CONTROL
 Quality control in the medical laboratory is a statistical process used
to monitor and evaluate the analytical process which produces patient
results. The purpose of quality control system is to monitor analytic
processes, detect analytic errors during analysis and prevent the
reporting of incorrect patient results.
 Trend: A gradual, often subtle, increase or decrease in control values
and possibly patient values.
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3. QUALITY CONTROL
 Accuracy of Measurement: Closeness of agreement
between the result of a measurement and a true value of a
measurand.
 Analyte: A substance or constituent for which the
laboratory conducts testing.
 Bias: The systematic deviation of the test results from the
accepted reference value.
 Calibration: Calibration is the set of operations that
establish, under specified conditions, the relationship
between reagent system/instrument response and the
corresponding concentration/activity values of an
analyte.

4. QUALITY CONTROL
 Calibration Verification: Denotes the process of confirming that
the current calibration settings remain valid for a method.
 Coefficient of Variation (CV %): The Coefficient of Variation is
the ratio of the standard deviation to the mean and is expressed as
percentage.
 Imprecision: The random dispersion of a set of replicate
measurements and/or values expressed quantitatively by a statistic
such as standard deviation or coefficient of variation.
 Mean: The mean of a group of data points is simply their
arithmetic average.
 Precision: Closeness of agreement between indications or
measured quantity values obtained by replicate measurements on
the same or similar objects under specified conditions.

5. QUALITY CONTROL
 Random Error: The results of a measurement minus the mean
that would result from an infinite number of measurements of
the same repeatability conditions.
 Repeatability: Measurement precision under a set of
repeatability conditions of measurement
 Standard deviation (SD): The standard deviation measures a
test's precision or how close individual measurements are to
each other. The standard deviation provides an estimate of how
repeatable a test is at specific concentrations.
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6. QUALITY CONTROL
 Systematic error: The mean that would result from an infinite
number of measurements of the same measurand carried out under
repeatability conditions, minus a true value of the measurand.
 Shift: A sudden and eventually stable change in control values and
possibly patient values.
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7. Mean or average
 The simplest statistic is the mean or average. Years ago, when laboratories
were beginning to assay controls, it was easy to calculate a mean and use that
value as the "target" to be achieved. For example, given the following ten
analyses of a control material - 90, 91, 89, 84, 88, 93, 80, 90, 85, 87 - the
mean or X bar is 877/10 or 87.7. [The term X bar refers to a symbol having a
line or bar over the X,, however, we will use the term instead of the symbol in
the text of these lessons because it is easier to present.]
 The mean value characterizes the "central tendency" or "location" of the data.
Although the mean is the value most likely to be observed, many of the actual
values are different than the mean. When assaying control materials, it is
obvious that technologists will not achieve the mean value each and every
time a control is analyzed. The values observed will show a dispersion or
distribution about the mean, and this distribution needs to be characterized to
set a range of acceptable control values.

8. Standard deviation
 The dispersion of values about the mean is predictable and can
be characterized mathematically through a series of
manipulations, as illustrated below, where the individual
x-values are shown in column A.
Column A Column B Column C
X value X value-Xbar (X-Xbar)2
90 90 - 87.7 = 2.30 (2.30)2 = 5.29
91 91 - 87.7 = 3.30 (3.30)2 = 10.89
89 89 - 87.7 = 1.30 (1.30)2 = 1.69
84 84 - 87.7 = -3.70 (-3.70)2 = 13.69
88 88 - 87.7 = 0.30 (0.30)2 = 0.09
93 93 - 87.7 = 5.30 (5.30)2 = 28.09
80 80 - 87.7 = -7.70 (-7.70)2 = 59.29
90 90 - 87.7 = 2.30 (2.30)2 = 5.29
85 85 - 87.7 = -2.70 (-2.70)2 = 7.29
87 87 - 87.7 = -0.70 (-0.70)2 = 0.49
X = 877 (X-Xbar) = 0 (X-Xbar)² = 132.10

9. Standard deviation
 The first mathematical manipulation is to sum (∑) the individual
points and calculate the mean or average, which is 877 divided by 10,
or 87.7 in this example.
 The second manipulation is to subtract the mean value from each
control value, as shown in column B. This term, shown as X value –
X bar, is called the difference score. As can be seen here, individual
difference scores can be positive or negative and the sum of the
difference scores is always zero.
 The third manipulation is to square the difference score to make all
the terms positive, as shown in Column C.
 Next the squared difference scores are summed.
 Finally, the predictable dispersion or standard deviation (SD or s) can
be calculated as follows:

= [132.10/(10-1)]1/2 = 3.83

10. Coefficient of Variation (CV)
 The coefficient of variation is the ratio of the standard deviation to
the mean. Express CV as a percentage. Use the following formula
to calculate the CV:
Using the CV makes it easier to compare the overall precision of two analytical
systems.The CV is a more accurate comparison than the standard deviation as
the standard deviation typically increases as the concentration of the analyte
increases. Comparing precision for two different methods using only the
standard deviation can be misleading.

11. Standard Deviation Index (SDI) or z-score
 CV% = (SD/Xbar)100
 The standard deviation index is a measurement of bias (how close
your value is to the target value). The Bio-Rad Unity™
Interlaboratory Program uses the consensus group value as the target
value. Use the following formula to calculate the SDI:
 Interpreting the SDI
 The target SDI is 0.0, which indicates there is not any difference
between the laboratory mean and the consensus group mean. A SDI
±1 indicates a possible problem with the test.
 The SDI expresses bias as increments of the standard deviation. A
SDI of -1.8 indicates a negative bias of 1.8 standard deviations from
the consensus group mean. This is not favorable.
 Bias increases or decreases the percentage of patients outside the
defined reference limit. For example, a positive bias decreases the
percentage of patients normally outside the lower limit and increases
the percentage of patients normally outside the upper reference limit.
This creates an increase in false positive test results. Negative bias
has an opposite effect and decreases true positives and creates false

12. Standard Deviation Index (SDI) or z-
score
 Use the following guidelines to interpret the SDI:

13. FLOW CHART

14. WESTGARD RULES (12s )
 1-2s:This is a warning rule
which is violated when a
single control observation is
outside the ±2s limit. If one
control measurement
exceeds the mean ± 2
standard deviations, you
should evaluate other
controls in the run (within
control material) and in
previous runs (across control
material) before accepting
the run and reporting the
results.

15. WESTGARD RULES (12.5s )
 1-2.5s: Violation of this rule
indicates random error and
may also point to systematic
error. The run is considered
out of control when one
control value exceeds the
mean ± 2.5s. This rule is
applied within control
material only.

16. WESTGARD RULES (13s )
 13s refers to a control rule
that is commonly used
with a Levey-Jennings
chart when the control
limits are set as the mean
plus 3s and the mean
minus 3s. A run is rejected
when a single control
measurement exceeds the
mean plus 3s or the mean
minus 3s control limit.

17. WESTGARD RULES (13.5s )
 1-3.5s: Violation of this rule
indicates random error and
may also point to systematic
error. The run is considered
out of control when one
control value exceeds the
mean ± 3.5s. This rule is
applied within control
material only.

18. WESTGARD RULES (14s )
 1-4s: Violation of this rule
indicates random error and
may also point to
systematic error. The run
is considered out of
control when one control
value exceeds the mean ±
4s. This rule is applied
within control material
only.

19. WESTGARD RULES (15s )
 1-5s: Violation of this rule
indicates random error and
may also point to systematic
error. The run is considered
out of control when one
control value exceeds the
mean ± 5s. This rule is
applied within control
material only.

20. WESTGARD RULES (22s )
 22s - reject when 2
consecutive control
measurements exceed the
same mean plus 2s or the
same mean minus 2s
control limit.

21. WESTGARD RULES (R4s )
 R4s - reject when 1 control
measurement in a group
exceeds the mean plus 2s
and another exceeds the
mean minus 2s.
 This rule identifies
random error only, and is
applied only within the
current run. If there is a 4s
difference between control
values within a single run,
the rule is violated for
random error.

22. WESTGARD RULES (31s )
 3-1s: This rule detects
systematic bias and is applied
both within and across control
materials. They are violated
within the control material if
the last 3 values of the same
control level are within the
"same" (mean + 1s or mean -
1s) limit. They are violated
across the control material if
the last 3 consecutive control
values for different control
levels are within the "same"
(mean + 1s or mean - 1s) limit.

23. WESTGARD RULES (41s )
 4-1s: This rule detects systematic
bias and is applied both within
and across control materials.
They are violated within the
control material if the last 4
values of the same control level
are within the "same" (mean + 1s
or mean - 1s) limit. They are
violated across the control
material if the last 4 consecutive
control values for different
control levels are within the
"same" (mean + 1s or mean - 1s)
limit.

24. WESTGARD RULES (7-x)
 7-x: This rule detects
systematic bias and is applied
both within and across control
materials. It is violated across
control materials if the last 7
consecutive values, regardless
of control level, are on the
same side of the mean. The
rule is violated within the
control materials if the last 7
values for the same control
level are on the same side of
the mean.

25. WESTGARD RULES (8-x)
 8-x: This rule detects
systematic bias and is applied
both within and across control
materials. It is violated across
control materials if the last 8
consecutive values, regardless
of control level, are on the
same side of the mean. The
rule is violated within the
control materials if the last 8
values for the same control
level are on the same side of
the mean.

26. WESTGARD RULES (9-x)
 9-x: This rule detects
systematic bias and is
applied both within and
across control materials. It is
violated across control
materials if the last 9
consecutive values,
regardless of control level,
are on the same side of the
mean. The rule is violated
within the control materials
if the last 9 values for the
same control level are on the
same side of the mean.

27. WESTGARD RULES (10X)
 10-x: This rule detects
systematic bias and is
applied both within and
across control materials. It
is violated across control
materials if the last 10
consecutive values,
regardless of control level,
are on the same side of the
mean. The rule is violated
within the control
materials if the last 10
values for the same
control level are on the
same side of the mean.

28. WESTGARD RULES (12X)
 12-x: This rule detects
systematic bias and is
applied both within and
across control materials. It is
violated across control
materials if the last 12
consecutive values,
regardless of control level,
are on the same side of the
mean. The rule is violated
within the control materials
if the last 12 values for the
same control level are on the
same side of the mean.

29. Poor LJ Chart
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30. To Technologist
 Make a practice to monitor a Quality control data
on daily basis it will be useful for month end
review for evaluation of Quality control data
points & LJ chart.
 Right now we have a Unity web software which is
very useful to evaluate our Internal Quality
control data.
 example: Monthly evaluation sheet, Laboratory
comparison data, Laboratory histogram &
Laboratory performance overview.
 Note: Please utilize this unity software which is
helpful in your future as well as to our Laboratory
quality

31. THANK YOU
 SYED BASHEER UDDIN