Control of Analytical Variables
Dr. Roula Hamid
MSc Clin Biochem
Central Puplic Health
Laboratory
QC Chemistry
Today is not the golden age of
quality in healthcare
laboratories.
We can & should be doing the
better.
James O Westgard 2003
Introduction
“Nice to Know”
Quality
Planning (QP(
Quality
Improvement
(QI(
Quality
Laboratory
Process (QLP(
Goals, Objectives,
Quality Riquirements
Quality
Assessment
(QA(
Quality
Control (QC(
Total Quality Management framework for management
of quality in healthcare laboratories
QLP
QC & QA
(standard process
for the way things
are DONE)
(CHECK)
QI
QP
(PLANING steps)
PDCA
(mechanism
through which to
ACT on those
measures)
Plan, do,
check & act
The “five-Q” framework defines how quality is
managed objectively with the “scientific
method” or the PDCA cycle
Control of Analytical
Variables
Analytical Variables must be controlled
carefully to ensure accurate measurements
by analytical methods
 Documentation of analytical protocols
 Monitoring technical competency
 Statistical control of analytical methods
 EQA
 New quality initiatives
Documentation of Analytical Protocols
 CLSI defines a process as a set of interrelated
or interacting activities that transform input
into output.
CLSI document describes the following section
to be included in a laboratory procedure :
A. Title: clear & concise
B. Purpose or principle: e.g. this process describes
how ….,
C. Procedure instructions: how to do
D. Related documents: listing of other procedure
E. References: source of information
F. Appendixes or attachments
G. Auther(s): author(s) of document
H. Approved signatures
Monitoring Technical Competency
 Proper training of laboratory personnel to
establish uniformity in technique is important.
Statistical control of analytical
methods
 Control materials
 General principles of control chart
 Performance characteristics of a control
procedures
 Westgard multirule chart
 Identifying sources of analytical errors
Control materials
 Specimens that are analyzed for QC purposes
are known as control materials
 They need to be available
1) In a stable form
2) In vial or aliquots
3) & for analysis over an extended period of
time
General principles of control chart
 A common method used to compare the
values observed for control materials with
their known values is the use of control charts
Figure: Gaussian frequency distribution
a) Stable
performance
Control limit
b) Accuracy problem;
shift in mean
c) Precision
problem; increase
in standard
deviation
Control limit
Figure: Conceptual basis of control charts. Frequency distributions of
control observations for different error conditions
1.5
1
0.5
0
0
5
10
15
20
25
30
-0.5
-1
-1.5
Figure: Conceptual basis of control charts. Frequency
distributions of control observations for different error
conditions
35
Performance characteristics of a control
procedures
The knowledge of performance characteristics
of control procedures is necessory to select
the control rules that detect relevent
laboratory problems without causing too
many false alarms
Westgard multirule chart
The probability of false rejection is kept low
through selection of only those rules with low
The probabilities for error detection is
improved through selection of those rules that
are particularly sensitive to random &
systemic errors.
The use of multirule procedure is similar to
the use of a Levey-Jennings chart, but the data
interpretation is more structured.
Figure: Decision path for QC program
To use the multirule procedure, the following steps are
used :
A- 20 days, 2 different materials, mean & SD are
calculated for both.
B- computer software; control values on y-axis ±4s,
horizontal lines for 1s,2s & 3s, & x-axis for days
C- 2 control samples are introduced into each
analytical run
DControl observations fall within 2s
limits
Analytical run is accept & patient
results reported
One of control observations exceed 2s limits
Patient results are held
Additional rules are applied e.g. 13s.2 2s,R 4s & 10x
If any is out
The analytical run is rejected &
the patients results are not reported
EThe type of error is determined based on the control rule that
have been violated
Looking for the source of that error
The problem is corrected
The analysis of the entire run repeated including both control &
patient samples
High Concentration Control Material
170
Observed Control Concentration
165
160
155
150
145
140
135
130
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Run Number
Figure : Westgard multirule chart with control limit drawn at the
mean ± 1s, 2s & 3s. Chart for high concentration
28
29
30
Low Concentration Control Material
116
Observed Control Concentratio
112
108
104
100
96
92
88
84
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
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17
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20
21
22
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25
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29
Figure : Westgard multirule chart with control limit drawn at the mean ± 1s, 2s &
3s. Chart for low concentration
30
Identifying sources of analytical errors
Alerted to a control problem
Inspection OR Checklist
Analytical
methods
Equipments
Reagents
Specimens
ERRORS
Systemic
Random
Systemic Errors
Random Errors
Impure calibration
materials
Improper preparation of
calibrating solution
Erroneous set points &
assigned values
Unstable calibrating
solutions
Lack of reproducibility in the
pipetting of samples & reagents
Dissolving of reagents tablets &
mixing of sample & reagents
Lack of stability of temperature
baths, timing regulation, &
photometric & other sensors
Contaminated solutions
Inadequate calibration
technique
Nonlinear or unstable calibration
function
Inadequate sample blank
Unstable reagent blanks
EQA
Procedures used to compare the
performance of different laboratories
(EQA)
IQC & EQA are complementary
IQC
• For daily
monitoring of
accuracy
• For daily
monitoring of
precision
EQA
• Maintenance of
long term
accuracy of the
analytical
methods
Features of External Quality Assessment
Programs
 EQA program available to the clinical
laboratories by professional societies &
manufactures of control materials
 All the participating laboratories analyzing the
same lot of control material
 Results are tabulated periodically & sent to
the sponsering group for data analysis
 The reports often includes extensive data
analysis, statesical sumaries & plots
 The mean of values of all laboratories is taken
as the true or correct value & is used for
comparision with the indivisual laboratory’s
mean
 Different approaches for data anaalysis e.g. ttest, SDI,Youden plots & Levey-Jennings plots
New quality initiatives
The six sigma process
Lean production
ISO 9000
The six sigma process
 The six sigma control is an evolution in quality
management
 6 sigma or 6 SD of process variation should fit
within the tolerance limits for the process
- Tolerance
specification
Target
+ Tolerance
specification
-6s -5s -4s -3s -2s -1s 0s 1s 2s 3s 4s 5s 6s
Figure : Six sigma goal for process performance “ tolerance specification”
represents the quality requirements
Lean production
 It is a quality process that is focused on
creating more value by eliminating activities
that are considered waste
 e.g. Lean team at Saint Mary’s Hospital used
lean production to improve the efficiency of
its paper ordering system for lab work in their
ICU.
Six sigma process
Lean Production
Improve quality
Increase efficiency
Management of health care
facilities & clinical laboratories
ISO 9000
 The International Standard Organization (ISO)
has developed the ISO 9000 standards
 It is a set of 4 standards (ISO 9001-9004)
enacted to ensure quality management & QA.
 ISO 9000 represents an international
consequence on the essential features of a QS
to ensure the effective operation of an
organization
Joint Committee for traceability in
Laboratory Medicine
The traceability of values assigned to calibrators
&/or control materials must be assured
through available reference measurement
procedures &/or available reference materials
of a higher order
True value
Definitive method
Primary reference
material
Reference method
traceability
Method validation,
external quality
control
Secondary
reference
method
Observed value
Figure: Structure of an accuracy
based measurement system showing
relationships among reference
methods & materials
Method
validation
Field method
Control material
IQC
References
Burtis,C.A., Ashwood,E.R. & Br uns,D.E. Fundamentals of
Clinical Chemistry. 2008. .6th.ed. SAUNDERS ELSEVIER.
P:249-262.
Arneson,W. & Brichell,J. Clinical Chemistry ‘A Laboratory
Perspective’. 2007. F. A. Davis Company. P:53-72.
Westgard,J.O. Internal Quality Control: Planning &
Implementation Strategies. 2003. Ann Clin Biochem.
40; 593-611.
Thank you