Transcript Document
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 (standard process for the way things are DONE) QC & QA (CHECK) QP (PLANING steps) PDCA Plan, do, check & act QI (mechanism through which to ACT on those measures) The “five-Q” framework defines how quality is managed objectively with method” or the PDCA cycle the “scientific
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.
B.
C.
D.
E.
F.
G.
H.
Title: clear & concise Purpose or principle: e.g. this process describes how …., Procedure instructions: how to do Related documents: listing of other procedure References: source of information
Appendixes or attachments
Auther(s): author(s) of document
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) 2) In a stable form 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 -0,5 5 10 15 20 25 30 -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
D-
Control 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. 1 3s.
2 2s, R 4s & 10 x If any is out The analytical run is rejected & the patients results are not reported
E-
The 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
170 165 160 155 150 145 140 135 130 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Run Number
18 19 20 21 22 23 24 25 26 27 28 29 30 Figure : Westgard multirule chart with control limit drawn at the mean ± 1s, 2s & 3s. Chart for high concentration
Low Concentration Control Material
116 112 108 104 100 96 92 88 84 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 28 29 30 Figure : Westgard multirule chart with control limit drawn at the mean ± 1s, 2s & 3s. Chart for low concentration
Identifying sources of analytical errors
Alerted to a control problem Analytical methods Inspection OR Checklist Equipments Reagents ERRORS Systemic Random Specimens
Systemic Errors Random Errors Impure calibration materials Improper preparation of calibrating solution Erroneous set points & assigned values Unstable calibrating solutions Contaminated solutions Inadequate calibration technique Nonlinear or unstable calibration function Inadequate sample blank 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 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 manufactures of control materials the clinical laboratories by professional societies & 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. t test, 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 traceability Observed value Figure: Structure of an accuracy based measurement system showing relationships among reference methods & materials Definitive method Primary reference material Method validation Reference method Secondary reference method Method validation, external quality control Field method Control material IQC
References
Burtis,C.A., Ashwood,E.R. & Br uns,D.E. Fundamentals of Clinical Chemistry. 2008. .6
th.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.