Presentation on theme: "Control of Analytical Variables Dr. Roula Hamid MSc Clin Biochem Central Puplic Health Laboratory QC Chemistry."— Presentation transcript:
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 Laboratory Process (QLP( Quality Control (QC( Quality Assessment (QA( Quality Improvement (QI( Goals, Objectives, Quality Riquirements Total Quality Management framework for management of quality in healthcare laboratories
The “five-Q” framework defines how quality is managed objectively with the “scientific method” or the PDCA cycle PDCA Plan, do, check & act QP (PLANING steps) QLP (standard process for the way things are DONE) QC & QA (CHECK) QI (mechanism through which to ACT on those measures)
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
Control limit a) Stable performance b) Accuracy problem; shift in mean c) Precision problem; increase in standard deviation Figure: Conceptual basis of control charts. Frequency distributions of control observations for different error conditions
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 : C- 2 control samples are introduced into each analytical run B- computer software; control values on y-axis ±4s, horizontal lines for 1s,2s & 3s, & x-axis for days A- 20 days, 2 different materials, mean & SD are calculated for both.
D- Analytical run is accept & patient results reported Control observations fall within 2s limits
The analytical run is rejected & the patients results are not reported If any is out Additional rules are applied e.g. 13s.2 2s,R 4s & 10x Patient results are held One of control observations exceed 2s limits
E- The analysis of the entire run repeated including both control & patient samples The problem is corrected Looking for the source of that error The type of error is determined based on the control rule that have been violated
Figure : Westgard multirule chart with control limit drawn at the mean ± 1s, 2s & 3s. Chart for high concentration
Figure : Westgard multirule chart with control limit drawn at the mean ± 1s, 2s & 3s. Chart for low concentration
Identifying sources of analytical errors ERRORS SystemicRandom Inspection OR Checklist Analytical methods EquipmentsReagentsSpecimens Alerted to a control problem
Systemic 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 blankUnstable reagent blanksRandom Errors 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
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. 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
-6s -5s -4s -3s -2s -1s 0s 1s 2s 3s 4s 5s 6s + Tolerance specification - Tolerance specification Target 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 Improve quality Management of health care facilities & clinical laboratories Lean Production Increase efficiency
ISO 9000 The International Standard Organization (ISO) has developed the ISO 9000 standards It is a set of 4 standards (ISO ) 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
Definitive method Method validation Primary reference material Reference method Method validation, external quality control Field method Control material IQC True value Observed value traceability Figure: Structure of an accuracy based measurement system showing relationships among reference methods & materials Secondary reference method
References Burtis,C.A., Ashwood,E.R. & Br uns,D.E. Fundamentals of Clinical Chemistry th.ed. SAUNDERS ELSEVIER. P: Arneson,W. & Brichell,J. Clinical Chemistry ‘A Laboratory Perspective’ F. A. Davis Company. P: Westgard,J.O. Internal Quality Control: Planning & Implementation Strategies Ann Clin Biochem. 40;