Automatic Rule Selection with EZ Rules 3

Automatic Rule Selection with EZ Rules 3

Effective Control of the Examination Process Reunin de Expertos 5 Ciclo Internacional De Conferencias De La Calidad 1 Coordinator James Westgard, United States PARTICIPANTS Laura Mercapide, Argentina Amadeo Saez, Brasil Ada Porras, Colombia Oscar Martnez, Colombia Enrique Amaya, Per Margarita Iturriza,

Venezuela Erik Mendoza, Mxico Our Focus is on the Examination Process Pre-analytic and post-analytic errors are also of concern, but our charge is to focus on the analytical part of the examination process Evidence in scientific literature indicates analytical errors are still major source of problems leading to mistreatment and harm to patients What errors have been observed in the Total Testing Process? Plebani & Carraro. Clin Chem 2007:53:133842 60% 15% 25% Pre-analytic

Patient preparation Specimen acquisition Specimen processing Sample transport Physician test order Analytic Sample aliquot Analyzer setup Test calibration Quality control Reportable test Post-analytic

Test report Transmittal of report Receipt of report Review of test results Action on test results 5 Lab Errors and Patient Care Plebani-Carraro study Clin Chem 2007:53:1338-42 51,746 tests 393 questionable results 160 confirmed as laboratory errors 46 caused inappropriate patient care 24 of those were analytical errors

Analytical errors are still major cause (over 50% cases) of inappropriate patient care 6 Need for Guidance on Effective Control of Examination Process for Latin American Countries Important to be relevant and practical for local laboratories Must consider national and governmental interests and requirements Must consider professional assessment of needs and practice guidelines General guidance on effective control of examination process?

ISO 15189: Medical Laboratories Particular requirements for quality and competence ISO 15198: Validation of user quality control procedures by the manufacturer CLSI C24-A3: Statistical Quality Control for Quantitative Measuring Processes Principles and Definitions CLSI EP23-P: Laboratory Quality Control based on Risk Management 8 ISO 15189 Guidance for Assuring Quality of Examination Process 5.6.1. The laboratory shall design internal quality control systems that verify the attainment of the intended quality of results

Medical relevance of laboratory tests is an important consideration! Comparability of test results is important for medical relevance! How define medical relevance? 9 ISO 15189 Assuring quality 5.6.2 ..determine uncertainty of results, where relevant and possible 5.6.3 ensure that results are

traceable 5.6.4 participate in interlaboratory comparisons 5.6.5 if EQA not available, develop a mechanism for determining acceptability 5.6.6 For examinations performed using different procedures or at Particular issues assigned to this work group What frequency of QC is sufficient? How important are recommendations from manufacturers for QC? Should laboratory modify recommendations? How often for EQC or PT? What frequency of QC? CLSI guidance on defining run length An analytical run is an interval (i.e., a

period of time or series of measurements) within which the accuracy and precision of the measuring system is expected to be stable; between which events may occur causing the measurement process to be more susceptible (i.e., greater risk) to errors that are important to detect. 12 Factors Affecting Run Length (1) Events and non-events Event driven QC Non-event driven QC Known, scheduled and expected changes Other things that happen

Parvins concepts Ref: Parvin, Gronowski. Effect of analytical run length on QC performance and the QC planning process. Clin Chem 1997;11:2149-2154. 13 Factors Affecting Run Length (2) Mode of Operation Batch mode All patient specimens and control samples are analyzed together Patient results not reported until control results are validated Continuous mode

Patient results are being reported as they are determined Control samples are analyzed periodically 14 Factors affecting Run Length Cost-Effectiveness Number of controls relative to number of patient specimens Levels of controls Factors Affecting Run Length (3) Strategies

Manufacturers instructions may provide minimum strategy e.g., 2 levels per day + Event QC to assess significance of changes in the testing process + non-event QC to monitor process during routine operation Multi-stage QC for startup, monitoring, and patient data QC 16 Factors Affecting Run Length (4) Strategies Sigma QC relative amount of QC can be related to methods sigma-performance Risk analysis and residual risks guidance for susceptibility testing Consensus of experts professional practice standards Experienced judgment knowledgeable

analyst has expertise about stability and susceptibility of testing process 17 Events Operations Calibration, reagents Stats Maintenance Operators Components Lab conditions Parts Analyte stability Residual risks Continuous mode System stability Susceptibility LIS

Batch mode Unexpected Batch size events Reporting interval Cost of repeat analysis Models Strategies Empirical Manuf. recommendations Statistical Measures Assumptions Batch processing Economic Error frequency, f Q/P QC performance goals

Residual risks Event+Monitor Event only Sigma Risk analysis Residual risks Consensus of experts Experienced judgment Run length and frequency of controls 18 What frequency of QC is sufficient? Recommendations (1) Strategy Define length of run In terms of time, numbers of patient samples, mode of operation Importance of events or changes that

occur with the process that require verification by controls Medically important concentrations for controls General practice to use two levels of controls Sometimes advisable to have three levels Many factors to consider to optimize run length or frequency of QC What frequency of QC? Recommendations (2) For small runs, utilize batch strategy QC at beginning QC at end

Release results after inspect all controls and reviewing patient results when necessary What frequency of QC? Recommendations (3) For large runs, highly automated systems with continuous reporting of results Controls at beginning of run + Controls for events e.g., Change of reagent lots + Controls to monitor performance during run

Right QC design to detect medically important errors Or, possibly use mean or median of patient data to monitor stability during run + Controls at end of run How important are the manufacturers QC directions? Recommendations (4) Provide minimum requirements that the laboratory must satisfy E.g., calibration, preventive maintenance, etc. Laboratory is still responsible for design of IQC system Intended clinical use Observed method performance Necessary QC rules and Numbers of

measurements How important manufacturers QC directions? Recommendations (5) Need for independent control Traceability is an important responsibility of manufacturer Third party control Calibration materials and process Verification/validation of method performance is an important responsibility of the laboratory EQA/PT important responsibility in monitoring/measuring accuracy over time

Commutability important characteristic of materials How often EQA/PT? Recommendations (6) At least monthly Approved EQA program preferred With fast turnaround of results to be useful for identifying bias and making improvements in the laboratory ILAC G13:08/2007 ISO 17043 Most essential information bias observed vs assigned value

Other issues of interest Medical relevance Traceability Intended use, intended quality of test results Comparability of test results Validation of method performance Design of IQC Available planning approach, tools How assure quality? (1a) Regulatory & Accreditation

Requirements (2a) Traceability & Calibration (1) Define Goals for Intended Use (TEa, Dint) (2) Select Analytic Measurement Procedure (1b) Clinical and Medical Applications (2b) Manufacturers Reference Methods & Materials (3) Validate Method Performance (CV,bias) (3a) Manufacturers Claims (4) Design SQC (rules, N, F)

(5a) Manufacturers Risk Analysis (11) Improve AQC Effectiveness (5) Formulate AQC Strategy (5b) Lab Evaluation of Residual Risk (10) Monitor AQC Effectiveness (f), EQA (6) Develop AQC Plan (6a) QC Toolbox (9) Measure Quality & Uncertainty (7) Implement AQC System (8) Verify Attainment of Intended Quality of Test Results

26 ISO 15198 Validation of QC Procedures QC procedures shall be validated to assure that failures are not a hazard to patients Recommends use of risk analysis Conventional statistical quality control procedures (e.g., as described in CLSI C24) are considered adequate Validation may be based on simulated 27 CLSI C24 QC Planning Process Define quality specifications for test

Select appropriate control materials Determine method performance Identify quality control strategies Predict QC performance Calculate Sigma %TEa-%Bias %CV Utilize Sigma-metric QC Selection Tool Specify goals for QC performance Select QC to satisfy goals 28 Sigma-metrics QC Selection Tool 2 Levels Control Sigma

Probability for Rejection (P) 1.65 1.0 2.65 3sScale 3.65 4s 4.65 5s 5.65 Pfr 0.8 Desirable Error Detection 0.6 Desirable False Rejection

0.5 0.4 0.3 0.2 0.1 0.0 0.0 1.0 N R 13s /2of32s /R4s /31s /6x 0.07 ----6 1 1 /2 /R /4 /8 3s 2s 4s 1s x 0.03 ----4 2 1 /2 /R /4 3s 2s 4s 1s 0.03 ----4 1 12.5s

0.9 0.7 Ped 2.0 3.0 0.04 ----1 2.5s 0.03 ----13s /22s /R4s 4 1 2 1 0.01 1 3s 0.00

1 3.5s 0.00 ----- 2 1 ----- 2 1 ----- 2 1 4.0 Systematic Error (SE, multiples of s) 29

EP22, EP23 on Risk Analysis Original purpose of CLSI project was to develop scientific approach for defining frequency of QC Adopted risk analysis approach Failure-modes and risk should provide guidance on need for control mechanisms and frequency of QC Analytical QC Plan should be the outcome of the risk analysis process Important Considerations in Future QC Systems Design Validation

Is there a scientific basis and approach for selecting parameters and setting limits on basis of intended quality of results? Is there an objective approach for assessing the reliability of technical and medical decisions on control status? Control Is there a quantitative process for monitoring and verifying the attainment of intended quality of test results? 31

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