To assist the clinical interpretation of a test result, there is a necessity to have an additional non-analytical component in the overall estimation of UM, namely the biological variation.
A routine session on quality assurance practice in a medical laboratory to sensitize and provide basics to those interested in working in a medical testing laboratory.
This document discusses the requirements and process for laboratory accreditation according to ISO 15189 standards. It explains that accreditation is a formal recognition by an authoritative body that a laboratory is competent to perform specific tasks. The key requirements outlined include developing a quality management system, appointing a quality manager and technical manager, establishing quality indicators for monitoring and continual improvement, comprehensive documentation, and regular audits.
This is a series of notes on clinical pathology, useful for postgraduate students and practising pathologists. It covers all internal and external quality control techniques. The topics are presented point wise for easy reproduction.
The document discusses internal quality control procedures in a medical laboratory. It defines internal quality control and explains the three main stages - pre-analytical, analytical, and post-analytical - that need to be controlled. It describes the process for internal quality control, including using control materials, establishing statistical limits, and interpreting quality control data using rules like Westgard's multi-rules. The document emphasizes the importance of root cause analysis when quality control is out of control and comparing internal quality control with external quality assessment.
Quality control, quality assurance, and quality assessment are important concepts for ensuring accuracy and reliability in medical laboratory testing. Quality control refers to internal processes like running controls to verify test accuracy during each run. Quality assurance encompasses the overall program to deliver correct results. Quality assessment challenges these programs through external proficiency testing. Proper documentation, trained personnel, validated methods and equipment, and monitoring control rules are key to achieving the goals of quality control, quality assurance and providing quality medical laboratory testing.
Chemiluminescence Immunoassay (CLIA) using Microplate luminometers provides a sensitive, high throughput, and economical way to quantitatively measure antigen in cell lysates, plasma, urine, saliva, tissue and culture media samples.
Chemiluminescence Immunoassay does not require long incubations and the addition of stopping reagents, as is the case in conventional colorimetric assays such as Enzyme-linked ImmunoSorbent Assays (ELISA).
Among various enzyme assays that employ light-emitting reactions, one of the most successful assays is the enhanced chemiluminescent immunoassay involving a horseradish peroxidase (HRP) labeled antibody or antigen and a mixture of chemiluminescent substrate, hydrogen peroxide, and enhancers.
In recent years, CLIA has become very popular in clinical chemistry and environmental analysis, due to its high sensitivity, wide dynamic range and complete automation. With the development and application of recombinant Ab (rAb) technology, markers and related techniques, solid-phase materials and improvements in automation, integration and miniaturization, CLIA has acquired an entirely new appearance.
A routine session on quality assurance practice in a medical laboratory to sensitize and provide basics to those interested in working in a medical testing laboratory.
This document discusses the requirements and process for laboratory accreditation according to ISO 15189 standards. It explains that accreditation is a formal recognition by an authoritative body that a laboratory is competent to perform specific tasks. The key requirements outlined include developing a quality management system, appointing a quality manager and technical manager, establishing quality indicators for monitoring and continual improvement, comprehensive documentation, and regular audits.
This is a series of notes on clinical pathology, useful for postgraduate students and practising pathologists. It covers all internal and external quality control techniques. The topics are presented point wise for easy reproduction.
The document discusses internal quality control procedures in a medical laboratory. It defines internal quality control and explains the three main stages - pre-analytical, analytical, and post-analytical - that need to be controlled. It describes the process for internal quality control, including using control materials, establishing statistical limits, and interpreting quality control data using rules like Westgard's multi-rules. The document emphasizes the importance of root cause analysis when quality control is out of control and comparing internal quality control with external quality assessment.
Quality control, quality assurance, and quality assessment are important concepts for ensuring accuracy and reliability in medical laboratory testing. Quality control refers to internal processes like running controls to verify test accuracy during each run. Quality assurance encompasses the overall program to deliver correct results. Quality assessment challenges these programs through external proficiency testing. Proper documentation, trained personnel, validated methods and equipment, and monitoring control rules are key to achieving the goals of quality control, quality assurance and providing quality medical laboratory testing.
Chemiluminescence Immunoassay (CLIA) using Microplate luminometers provides a sensitive, high throughput, and economical way to quantitatively measure antigen in cell lysates, plasma, urine, saliva, tissue and culture media samples.
Chemiluminescence Immunoassay does not require long incubations and the addition of stopping reagents, as is the case in conventional colorimetric assays such as Enzyme-linked ImmunoSorbent Assays (ELISA).
Among various enzyme assays that employ light-emitting reactions, one of the most successful assays is the enhanced chemiluminescent immunoassay involving a horseradish peroxidase (HRP) labeled antibody or antigen and a mixture of chemiluminescent substrate, hydrogen peroxide, and enhancers.
In recent years, CLIA has become very popular in clinical chemistry and environmental analysis, due to its high sensitivity, wide dynamic range and complete automation. With the development and application of recombinant Ab (rAb) technology, markers and related techniques, solid-phase materials and improvements in automation, integration and miniaturization, CLIA has acquired an entirely new appearance.
Total Quality Management (TQM) by Dr Anurag YadavDr Anurag Yadav
Total quality management principles aim to improve patient care through monitoring laboratory work to detect deficiencies and correct them. Errors can occur in preanalytical, analytical, and postanalytical phases, and quality control procedures help control variables and ensure accuracy. Calibration, precision, accuracy, linearity, and detection limits are important analytical concepts, and factors like equipment, reagents, personnel, and documentation must be controlled and monitored to minimize errors and ensure quality.
Quality control and quality assurance programs are necessary to ensure reliable and accurate laboratory test results. Quality control involves daily monitoring processes like equipment calibration and testing control samples to verify test accuracy and precision. Quality assessment through external proficiency testing further challenges the quality programs. Proper quality control is implemented through all stages of testing - pre-analytical, analytical and post-analytical. Statistical tools like Levey-Jennings charts and Westgard rules are used to monitor quality control data and identify out of control results. The goal is to minimize errors and validate test results for optimal patient care.
QUALITY
Conformance to the requirements of users or customers satisfaction of their needs and expectations.
Total Quality Management
A management approach that focuses on processes and their improvement.
ISO 15189 is an international standard that specifies the general requirements for the competence of medical laboratories. It is based on ISO 17025 for testing and calibration laboratories and ISO 9001 for quality management systems. ISO 15189 has both management and technical requirements that medical laboratories must meet in order to be accredited. The standard is designed to ensure that laboratories consistently deliver accurate, reliable and timely medical testing services.
Harmonization of Laboratory Indicators, 09 03-2017Ola Elgaddar
The document discusses harmonization of quality indicators (QIs) in medical laboratories. It notes that while QIs are important for quality improvement, there is currently no consensus on common QIs or how they should be defined and measured. The Working Group on Laboratory Errors and Patient Safety has developed a Model Quality Indicator (MQI) to address this issue, which defines 53 measurements across 27 QIs. Laboratories can now report results to the MQI website voluntarily. Statistical analysis of early participation in the MQI shows the most widely used QIs relate to pre-analytical errors, analytical test performance, and post-analytical issues. However, broader adoption remains a challenge.
This document discusses Total Quality Management in medical laboratories. It covers key aspects of quality management including defining quality, the core elements of a quality management system, and the seven common tools used in quality control. It also discusses requirements for laboratory accreditation such as establishing quality indicators, documentation, and quality assurance activities like proficiency testing and calibration. The overall goal of quality management in medical laboratories is to provide accurate and reliable test results to customers through an effective quality management system.
Internal quality control in clinical laboratories hematology(2)NAZAR ABU-DULLA
This document discusses internal quality control in hematology laboratories. It begins with an introduction of the author, Nazar Ahmed Mohamed Abd-Alla, which notes his qualifications and experience in hematology and laboratory administration.
The document then outlines topics that will be covered, including quality definitions, types of errors, specimen handling, method selection, calibration, documentation, and quality programs. It discusses sources of errors like pre-analytical, analytical, and post-analytical errors. It also covers quality assurance, method validation, and specifications for proper specimen collection, transport, and acceptance or rejection. The goal is to provide reliable and accurate test results through effective quality control.
Global Manager Group has prepared presentation to provide information about Medical Laboratory Accreditation Standard - ISO 15189 and about Documentation kit. All the documents like quality manual, procedures, SOPs, audit checklist, etc that required for the ISO 15189 Certification process. are described in details in this presentation.
Quality in clinical laboratory is a continuous journey of improving processes through team work, innovative solutions, regulatory compliance with final objective to meet the evolving needs of clinicians & patients.
This document discusses laboratory errors, their causes, and ways to prevent them. It notes that errors can occur at any stage of the testing process, from specimen collection to reporting of results. The majority of errors are pre-analytical or post-analytical. Common causes of errors include inadequate staffing, poor quality control, time pressures, and lack of validation of tests. Errors are classified as latent due to organizational failures or infrastructure issues, or active including pre-analytical, analytical, and post-analytical errors. Preventing errors requires measures such as staff education, adherence to standards and procedures, quality monitoring, and effective communication across departments. Reducing errors is important for accurate diagnosis and treatment of patients
Use of laboratory instruments and specimen processing equipment to perform clinical laboratory assays with only minimal involvement of technologist .
Automation in clinical laboratory is a process by which analytical instruments perform many tests with the least involvement of an analyst.
The International Union of Pure and Applied Chemistry (IUPAC) define automation as "The replacement of human manipulative effort and facilities in the performance of a given process by mechanical and instrumental devices that are regulated by feedback of information so that an apparatus is self-monitoring or self adjusting”.
Quality Assurance of Laboratory Test Results based on ISO/IEC 17025PECB
The document discusses quality assurance of laboratory test results based on ISO/IEC 17025. It discusses the importance of analytical measurement data and ensuring results are accurate and precise. It describes internal quality control procedures like spikes, blanks, and replicates that analysts use to ensure results are correct. It also discusses external quality assurance like participating in inter-laboratory comparisons and proficiency testing schemes. The document emphasizes that laboratories must have quality control procedures to monitor validity of tests and ensure trends in results are detectable.
Quality control in the medical laboratoryAdnan Jaran
This document discusses quality control in medical laboratories. It emphasizes that quality is achieved through determining customer requirements, ensuring necessary resources are available, planning management procedures, training staff, undertaking tasks correctly, taking corrective action when errors occur, conducting regular reviews and audits, and total management commitment. The quality assurance cycle involves various steps from patient preparation to reporting. Achieving high quality requires addressing all aspects of the laboratory, including organization, personnel, equipment, purchasing, process control, information management, documents, occurrence management, assessment, process improvement, customer service, and facilities/safety. The goal is to detect and prevent errors through a quality management system.
The document discusses three key principles of clinical laboratory quality: quality, quality control, and total quality management. It describes the importance of quality control procedures to monitor performance across the total testing process, including pre-analytical and post-analytical phases. The document also outlines the different steps in laboratory testing and potential sources of errors at each step, from test ordering to specimen acquisition to analytical measurement to reporting and interpretation. It emphasizes the role of quality assurance programs in ensuring technical competence and controlling variables.
This document discusses quality control in laboratories. It defines key terms like quality assurance, quality assessment, total quality management, and continuous quality improvement. It describes factors that can affect quality like pre-analytical, analytical, and post-analytical variables. The importance of standard operating procedures, proficiency testing, and documenting quality control procedures is emphasized. Maintaining accurate and precise results through internal quality control using control charts and Westgard rules is also outlined.
This document discusses external quality assurance (EQA) of serological testing. It outlines key elements of a quality system including documentation, training, assessment, and standards. EQA involves laboratories testing unknown samples provided by an EQA scheme and comparing results to improve accuracy. Participating in EQA allows laboratories to identify any issues, enhance performance, and ensure quality standards are met through objective review of results across laboratories. EQA schemes provide benefits for laboratories and regulatory authorities by establishing networks to improve testing practices and public confidence in testing standards.
Validation of lab instruments and quantitative test methods Mostafa Mahmoud
This lecture shows the procedures applied when going to validate your laboratory instruments and quantitative test methods also either FDA approved or laboratory developed tests.
Laboratory Internal Quality Control presentation master revision, 2014Adel Elazab Elged
Short presentation about using internal quality control material in clinical laboratory to ensure analytical quality laboratory results for the sake of better patient care and minimizing errors in diagnosis, management, and follow up.
Haemoglobin quality control by maintaining levey jennings chartDr Rashmi Sood
Haemoglobin quality control is important for blood donor selection and ensuring donor safety. The document discusses maintaining Levey Jennings charts to monitor internal quality control of haemoglobin measurements using a Hemocue analyzer. Westgard rules are applied to the charts to detect random errors like those caused by new staff and systematic errors indicating issues like reagent problems. Maintaining quality control through daily, weekly and monthly checks helps assure accurate haemoglobin results.
This document discusses quality control in clinical laboratory testing. It emphasizes that quality control is essential to provide reliable diagnostic reports and cost-effective patient care. Quality control involves monitoring precision, accuracy, and sources of variation through internal quality control, external quality assessment, and statistical analysis of control values using tools like control charts and Westgard rules. The goal is to minimize laboratory errors and reliably distinguish pathological variations in patient samples.
This content is suitable for medical technologists/technicians/lab assistants/scientists writing the SMLTSA board exam. The content is also suitable for biomedical technology students and people also interested in learning about test methodologies used in medical technology. This chapter describes test quality assurance (QA) and quality control (QC). Please note that these notes are a collection I used to study for my board exam and train others who got distinctions using these.
Disclaimer: Credit goes to those who wrote the notes and the examiners of each exam question. Please use only as a reference guide and use your prescribed textbook for the latest and most accurate notes and ranges. The material here is not referenced as it is a collection of pieces of study notes from multiple people, and thus will not be held viable for any misinterpretations. Please use at your own discretion.
Total Quality Management (TQM) by Dr Anurag YadavDr Anurag Yadav
Total quality management principles aim to improve patient care through monitoring laboratory work to detect deficiencies and correct them. Errors can occur in preanalytical, analytical, and postanalytical phases, and quality control procedures help control variables and ensure accuracy. Calibration, precision, accuracy, linearity, and detection limits are important analytical concepts, and factors like equipment, reagents, personnel, and documentation must be controlled and monitored to minimize errors and ensure quality.
Quality control and quality assurance programs are necessary to ensure reliable and accurate laboratory test results. Quality control involves daily monitoring processes like equipment calibration and testing control samples to verify test accuracy and precision. Quality assessment through external proficiency testing further challenges the quality programs. Proper quality control is implemented through all stages of testing - pre-analytical, analytical and post-analytical. Statistical tools like Levey-Jennings charts and Westgard rules are used to monitor quality control data and identify out of control results. The goal is to minimize errors and validate test results for optimal patient care.
QUALITY
Conformance to the requirements of users or customers satisfaction of their needs and expectations.
Total Quality Management
A management approach that focuses on processes and their improvement.
ISO 15189 is an international standard that specifies the general requirements for the competence of medical laboratories. It is based on ISO 17025 for testing and calibration laboratories and ISO 9001 for quality management systems. ISO 15189 has both management and technical requirements that medical laboratories must meet in order to be accredited. The standard is designed to ensure that laboratories consistently deliver accurate, reliable and timely medical testing services.
Harmonization of Laboratory Indicators, 09 03-2017Ola Elgaddar
The document discusses harmonization of quality indicators (QIs) in medical laboratories. It notes that while QIs are important for quality improvement, there is currently no consensus on common QIs or how they should be defined and measured. The Working Group on Laboratory Errors and Patient Safety has developed a Model Quality Indicator (MQI) to address this issue, which defines 53 measurements across 27 QIs. Laboratories can now report results to the MQI website voluntarily. Statistical analysis of early participation in the MQI shows the most widely used QIs relate to pre-analytical errors, analytical test performance, and post-analytical issues. However, broader adoption remains a challenge.
This document discusses Total Quality Management in medical laboratories. It covers key aspects of quality management including defining quality, the core elements of a quality management system, and the seven common tools used in quality control. It also discusses requirements for laboratory accreditation such as establishing quality indicators, documentation, and quality assurance activities like proficiency testing and calibration. The overall goal of quality management in medical laboratories is to provide accurate and reliable test results to customers through an effective quality management system.
Internal quality control in clinical laboratories hematology(2)NAZAR ABU-DULLA
This document discusses internal quality control in hematology laboratories. It begins with an introduction of the author, Nazar Ahmed Mohamed Abd-Alla, which notes his qualifications and experience in hematology and laboratory administration.
The document then outlines topics that will be covered, including quality definitions, types of errors, specimen handling, method selection, calibration, documentation, and quality programs. It discusses sources of errors like pre-analytical, analytical, and post-analytical errors. It also covers quality assurance, method validation, and specifications for proper specimen collection, transport, and acceptance or rejection. The goal is to provide reliable and accurate test results through effective quality control.
Global Manager Group has prepared presentation to provide information about Medical Laboratory Accreditation Standard - ISO 15189 and about Documentation kit. All the documents like quality manual, procedures, SOPs, audit checklist, etc that required for the ISO 15189 Certification process. are described in details in this presentation.
Quality in clinical laboratory is a continuous journey of improving processes through team work, innovative solutions, regulatory compliance with final objective to meet the evolving needs of clinicians & patients.
This document discusses laboratory errors, their causes, and ways to prevent them. It notes that errors can occur at any stage of the testing process, from specimen collection to reporting of results. The majority of errors are pre-analytical or post-analytical. Common causes of errors include inadequate staffing, poor quality control, time pressures, and lack of validation of tests. Errors are classified as latent due to organizational failures or infrastructure issues, or active including pre-analytical, analytical, and post-analytical errors. Preventing errors requires measures such as staff education, adherence to standards and procedures, quality monitoring, and effective communication across departments. Reducing errors is important for accurate diagnosis and treatment of patients
Use of laboratory instruments and specimen processing equipment to perform clinical laboratory assays with only minimal involvement of technologist .
Automation in clinical laboratory is a process by which analytical instruments perform many tests with the least involvement of an analyst.
The International Union of Pure and Applied Chemistry (IUPAC) define automation as "The replacement of human manipulative effort and facilities in the performance of a given process by mechanical and instrumental devices that are regulated by feedback of information so that an apparatus is self-monitoring or self adjusting”.
Quality Assurance of Laboratory Test Results based on ISO/IEC 17025PECB
The document discusses quality assurance of laboratory test results based on ISO/IEC 17025. It discusses the importance of analytical measurement data and ensuring results are accurate and precise. It describes internal quality control procedures like spikes, blanks, and replicates that analysts use to ensure results are correct. It also discusses external quality assurance like participating in inter-laboratory comparisons and proficiency testing schemes. The document emphasizes that laboratories must have quality control procedures to monitor validity of tests and ensure trends in results are detectable.
Quality control in the medical laboratoryAdnan Jaran
This document discusses quality control in medical laboratories. It emphasizes that quality is achieved through determining customer requirements, ensuring necessary resources are available, planning management procedures, training staff, undertaking tasks correctly, taking corrective action when errors occur, conducting regular reviews and audits, and total management commitment. The quality assurance cycle involves various steps from patient preparation to reporting. Achieving high quality requires addressing all aspects of the laboratory, including organization, personnel, equipment, purchasing, process control, information management, documents, occurrence management, assessment, process improvement, customer service, and facilities/safety. The goal is to detect and prevent errors through a quality management system.
The document discusses three key principles of clinical laboratory quality: quality, quality control, and total quality management. It describes the importance of quality control procedures to monitor performance across the total testing process, including pre-analytical and post-analytical phases. The document also outlines the different steps in laboratory testing and potential sources of errors at each step, from test ordering to specimen acquisition to analytical measurement to reporting and interpretation. It emphasizes the role of quality assurance programs in ensuring technical competence and controlling variables.
This document discusses quality control in laboratories. It defines key terms like quality assurance, quality assessment, total quality management, and continuous quality improvement. It describes factors that can affect quality like pre-analytical, analytical, and post-analytical variables. The importance of standard operating procedures, proficiency testing, and documenting quality control procedures is emphasized. Maintaining accurate and precise results through internal quality control using control charts and Westgard rules is also outlined.
This document discusses external quality assurance (EQA) of serological testing. It outlines key elements of a quality system including documentation, training, assessment, and standards. EQA involves laboratories testing unknown samples provided by an EQA scheme and comparing results to improve accuracy. Participating in EQA allows laboratories to identify any issues, enhance performance, and ensure quality standards are met through objective review of results across laboratories. EQA schemes provide benefits for laboratories and regulatory authorities by establishing networks to improve testing practices and public confidence in testing standards.
Validation of lab instruments and quantitative test methods Mostafa Mahmoud
This lecture shows the procedures applied when going to validate your laboratory instruments and quantitative test methods also either FDA approved or laboratory developed tests.
Laboratory Internal Quality Control presentation master revision, 2014Adel Elazab Elged
Short presentation about using internal quality control material in clinical laboratory to ensure analytical quality laboratory results for the sake of better patient care and minimizing errors in diagnosis, management, and follow up.
Haemoglobin quality control by maintaining levey jennings chartDr Rashmi Sood
Haemoglobin quality control is important for blood donor selection and ensuring donor safety. The document discusses maintaining Levey Jennings charts to monitor internal quality control of haemoglobin measurements using a Hemocue analyzer. Westgard rules are applied to the charts to detect random errors like those caused by new staff and systematic errors indicating issues like reagent problems. Maintaining quality control through daily, weekly and monthly checks helps assure accurate haemoglobin results.
This document discusses quality control in clinical laboratory testing. It emphasizes that quality control is essential to provide reliable diagnostic reports and cost-effective patient care. Quality control involves monitoring precision, accuracy, and sources of variation through internal quality control, external quality assessment, and statistical analysis of control values using tools like control charts and Westgard rules. The goal is to minimize laboratory errors and reliably distinguish pathological variations in patient samples.
This content is suitable for medical technologists/technicians/lab assistants/scientists writing the SMLTSA board exam. The content is also suitable for biomedical technology students and people also interested in learning about test methodologies used in medical technology. This chapter describes test quality assurance (QA) and quality control (QC). Please note that these notes are a collection I used to study for my board exam and train others who got distinctions using these.
Disclaimer: Credit goes to those who wrote the notes and the examiners of each exam question. Please use only as a reference guide and use your prescribed textbook for the latest and most accurate notes and ranges. The material here is not referenced as it is a collection of pieces of study notes from multiple people, and thus will not be held viable for any misinterpretations. Please use at your own discretion.
Quality control (QC) is a procedure or set of procedures intended to ensure that a manufactured product or performed service adheres to a defined set of quality criteria or meets the requirements of the client or customer. QC is similar to, but not identical with, quality assurance (QA).
QC IN clinical biochemistry labs and hospitals
Quality Control for Quantitative Tests by Prof Aamir Ijaz (Pakistan)Aamir Ijaz Brig
This document provides an overview of quality control and quality assurance processes in a chemical pathology laboratory. It discusses key terms like quality control, quality assurance, internal quality control, external quality assurance. It also describes different types of errors like random error and systematic error. The document explains statistical concepts like measures of central tendency, standard deviation, coefficient of variation. It discusses the Westgard rules for evaluating quality control results and triggering investigations into potential errors. The goal of the lecture is to describe the processes involved in quality management for chemical pathology laboratories.
The document discusses key concepts for interpreting clinical biochemical data, including:
1. Calculating measures of central tendency like the mean and measures of variability like the standard deviation and range to establish normal reference values.
2. Factors that determine the validity and reliability of analytical tests, and the importance of quality assurance and quality control.
3. Methods for describing data distribution including the coefficient of variation and using critical differences to determine the clinical significance of changes in serial measurements.
4. Concepts related to screening tests like sensitivity, specificity, and the ability to correctly identify individuals with and without the disease.
Quality control and quality assurance are important for ensuring accurate lab results. Quality control involves regularly testing controls of known values to monitor a test's performance. Key quality control statistics include the mean and standard deviation, which are used to calculate control limits on a Levey-Jennings chart. Westgard rules provide standards for determining when a test is out of control based on control values. Quality assurance encompasses the overall programs and procedures that a lab follows to ensure accurate and reliable results. It has strategic, tactical, and operational levels.
Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...Randox
Randox Quality Control's latest educational guide examines Internal Quality Control, External Quality Assessment, Why laboratories should run QC, How often laboratories should run QC, Basic QC statistics and the quality control process.
Quality Control in Pathological Laboratorysanarehman8159
This document discusses quality control in pathological laboratories. It defines quality as being free from defects and errors. Quality control monitors and evaluates analytical testing processes to ensure reliable patient results. There are two types of quality control: internal quality control performed within the lab, and external quality control performed by an outside agency. Quality assurance involves assessing all aspects of the testing process. Regular quality control using control samples is important to validate test systems and equipment are working properly so patient results can be accurately diagnosed and treated.
Control of analytical quality using stable control materials postgradMedhatEldeeb2
This document discusses quality control basics for analytical testing methods. It defines internal and external quality control and describes how control materials of known concentration are used to monitor test performance. The key aspects covered include:
- Types of errors such as random errors and systematic errors
- Criteria for selecting stable control materials with acceptable concentration ranges
- Calculations for mean, standard deviation, and quality control limits
- The Levey-Jennings chart for graphing quality control results over time
- Westgard rules for identifying errors based on the number of controls tested
- Identifying trends or shifts that indicate problems requiring troubleshooting
SHE, Quality, and Ethics in Medical Laboratories - PCLPAlAcademia Tsr
The document discusses various topics related to medical laboratories including quality control, safety, and ethics. It begins by covering types of quality control including internal quality control methods to check precision and external quality assessment schemes to check accuracy. It then discusses types of hazards in medical laboratories including chemical, physical, biological, and safety hazards. Recommendations are provided for safely handling chemical hazards. Finally, the document discusses the importance of ethics in relation to one's profession, laboratory premises, patients, and community.
High variability in PK can be a characteristic of certain drug products which require different from ordinary strategies and study designs for establishing bioequivalence.
Basics of laboratory internal quality control, Ola Elgaddar, 2012Ola Elgaddar
Total Quality Management (TQM) is a continuous approach to improve quality and performance. It requires integrating quality functions throughout an organization with involvement from management, employees, suppliers, and customers. For medical laboratories, quality control has three main stages - pre-analytical, analytical, and post-analytical. Analytical quality control involves internal quality control (IQC) using control materials and external quality assessment (EQA) to monitor quality and compare results between laboratories. IQC follows procedures like plotting daily control results on Levey-Jennings charts and evaluating them using Westgard rules to detect errors.
This document discusses bioequivalence standards for highly variable drug products. It begins by defining highly variable drug substances and products as those with intra-subject variabilities greater than 30%. It notes the sources of high variability can include administration conditions, physiological factors, and technical aspects. The usual standards for passing bioequivalence require average AUC and Cmax values to fall within 80-125% intervals. However, these criteria may be impossible to meet for highly variable drugs even with large sample sizes. The document therefore proposes alternative approaches for demonstrating bioequivalence of highly variable drugs, including replicate study designs and reference scaled average bioequivalence criteria. It provides examples and discusses some issues that can arise with these alternative approaches.
This document discusses quality control in hematology. It defines quality as meeting specifications and quality control as measures taken during testing to ensure tests are working properly. Quality assurance ensures the correct test is performed and the right result is delivered. Quality control involves analytical measurements to assess data quality while quality assurance is an overall management plan. The key activities of quality assurance are preventive, assessment and corrective measures. The document outlines the importance of accuracy, precision, internal quality control, external quality control and corrective actions when errors are found.
Variability of clinical chemistry laboratory resultsAdetokunboAjala
Understanding the concepts associated with variability of laboratory results would help laboratorians improve the quality of laboratory service as well as aid the drive towards harmonization of laboratory quality practices.
quality control in clinical laboratory DrmanarEmam
The document discusses quality control, quality assurance, and quality assessment in medical laboratories. It defines each term and describes their related but distinct roles. Quality control refers to statistical processes used during each test run to verify test accuracy and precision. Quality assurance describes the overall program that ensures correct final test results. Quality assessment challenges the quality programs through proficiency testing to evaluate the quality of reported results. The document provides details on quality control measurements and rules to monitor test performance over time and determine if tests are in or out of control.
This document discusses quality management systems in medical laboratories. It defines key terms like quality control, accuracy, precision, and quality assurance. It describes the goals of internal and external quality control. The document also explains sources of error like pre-analytical, analytical, and post-analytical phases. It provides details on Levey-Jennings charts, including how they are used to evaluate quality control results over time using statistical process control rules like Westgard rules.
- Reliability is a measure of reproducibility of a test when repeated, quantifying random error. Validity is how well a test measures what it intends to, requiring comparison to a criterion.
- Reliability is typically quantified by the typical error or intraclass correlation. Validity uses correlation and error of estimate from regression of the test on a criterion.
- Both reliability and validity should be high for a test to accurately track small individual changes over time and distinguish individuals. Ideal values are >0.96 for reliability and validity correlations and typical/estimate errors <20% of between-subject standard deviation.
This document provides guidance on bioanalytical method validation. It discusses validation parameters such as selectivity, accuracy, precision, recovery, calibration curves, and stability. Full validation is recommended when developing a new bioanalytical method or validating a revised method. Partial validation may be done for modifications like changes in matrix, reagents, or instrumentation. Cross-validation between methods and labs is also addressed. Recommendations are provided for chemical and microbiological/ligand-binding assay validation.
Similar to Biological variation as an uncertainty component (20)
In your routine laboratory works, do you have to issue a statement of conformity after testing a product sample, such as stating a Pass or fail, a compliance or non-compliance? What is your decision rule as required by this ISO standard? What is the risk to make a wrong decision in rejecting the test result based on the product specification limit when it is actually in conformance? Are you able to control such a risk in order to make an informed decision?
If you have all these questions in mind, I have got the answers for you.
Common mistakes in measurement uncertainty calculationsGH Yeoh
The basic calculation for measurement uncertainty (MU) is through the law of propagation of uncertainty. Some find it difficult to apply and make some mistakes in the MU evaluation.
Worked examples of sampling uncertainty evaluationGH Yeoh
ISO/IEC 17025:2017 laboratory accreditation standard has expanded its requirement for measurement uncertainty to include both sampling and analytical uncertainties.
A note and graphical illustration of type II errorGH Yeoh
This document discusses Type I and Type II errors in hypothesis testing. It begins by introducing the concepts of Type I errors, which occur when a null hypothesis is incorrectly rejected, and Type II errors, which occur when a null hypothesis is incorrectly accepted. The document then provides examples to illustrate Type I and Type II errors using hypothesis testing on a population mean. It shows how the probabilities of Type I and Type II errors are related and how varying the test value and critical value impacts the likelihood of each type of error. The document concludes by emphasizing that reducing Type I errors increases the risk of Type II errors.
The document discusses concepts related to the limit of detection (LOD) in chemical analysis. It defines LOD as the lowest concentration of an analyte that can be reliably detected by an analytical method. The document outlines different definitions of LOD and distinguishes it from method sensitivity. It discusses statistical approaches to estimating LOD using parameters like standard deviation of blank measurements. Factors that can affect LOD determination like number of replicates, matrix effects, and instrument performance are also covered. The relationship between LOD and limit of quantification is explained.
This document discusses quality assurance and quality control procedures for chemical test laboratories to meet ISO/IEC 17025:2017 requirements. It covers establishing quality assurance plans, differentiating quality assurance and quality control, applying quality control practices like blanks, replicates, and laboratory controls. Quality control charts are presented as a tool to monitor analytical accuracy and precision over time.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
8.Isolation of pure cultures and preservation of cultures.pdf
Biological variation as an uncertainty component
1. Use of Biological
Variation as an
Uncertainty
Component
Evaluation Methods of Uncertainty of
Measurement for Medical Testing
https://consultglp.com
by
Yeoh Guan Huah
GLP Consulting, Singapore
2. Outline
• A review of the uncertainty of measurement
(UM) calculation methods by component-by-
component approach
• Necessity to consider non-analytical
uncertainty components in addition to the
analytical uncertainty in the laboratory
• Biological variation – what and why
• How to incorporate biological variation in the
overall evaluation of UM.
3. Introduction
• The ultimate use of analysis data with its
associated uncertainty of measurement (UM)
must be fit for intended purpose
• Data generated by medical laboratory are used
for any signaling which may differentiate health
from disease, based on some pre-set reference
ranges for diagnostic decision
• Amongst other applications, these data are also
adopted for clinical monitoring of a patient upon
treatment.
4. Introduction
• Both ISO 15189 and ISO/IEC 17025 accreditation standards
have given uncertainty of measurement a new
perspective.
• They have recognized that apart from the analytical
uncertainty during the testing process, there are other
non-analytical uncertainty components to be considered
as well.
• For industrial, environmental and commodity testing,
ISO/IEC 17025 stresses the importance of taking
representative samples from the lot (population) for
analysis. Hence, sampling uncertainty is incorporated as
another uncertainty contributor, if applicable.
• For clinical application of a test result, the in vivo biological
variability of the measurand as an uncertainty component
cannot be ignored for clinical interpretation.
5. Reported result with uncertainty : X + U (units)
• Measurement uncertainty U is expressed
as:
• Test result : X + U
• and, U = k x u
• where k is a coverage factor (usually k = 2 or
1.96 for 95% confidence), and
• u, the combined standard uncertainty
expressed as standard deviation
• U is also called expanded uncertainty,
assumed rightly a normal probability
distribution of data
5
Example:
Consider an expression for a
measurand (urea in serum):
“5.0 + 0.2 mmol/L with 95%
confidence”
The + 0.2 mmol/L is the
uncertainty ( U ) of the supposed
5.0 mmol/L serum urea
measurement.
Its combined standard
uncertainty (u ) therefore, is
0.2/2 or 0.1 mmol/L
6. Basic methods for combining uncertainty
components
• Calculated from a sum and/or a difference of independent
measurements with associated standard uncertainties
• Let: R = X + Y or R = X – Y
• Then, (uR)2 = (uX)2 + (uY)2
• where uR , uX and uY are the respective analytical
standard uncertainty (as standard deviation)
• Therefore, combined standard uncertainty uR = (uR)2
• Note: X and Y are of same units and involved in addition
& subtraction conditions
7. Worked example No. 1
• Anion gap (AG) is calculated based on the results of serum (plasma)
ions: sodium, potassium, chloride and bicarbonate, in the form:
• AG = (Na+ + K+) – (Cl- + HCO3-)
Ion,
mmol/L Test result
Standard
deviation, u
Na+ 140 1.2
K+ 3.9 0.1
Cl- 102 1.3
HCO3- 24 1.2
Therefore,
AG = (140+3.9) – (102 + 24) = 17.9 mmol/L
𝐶𝑜𝑚𝑏𝑖𝑛𝑒𝑑 𝑆𝐷 𝑅= (1.2)2+(0.1)2+(1.3)2+(1.2)2
= 2.1 mmol/L
Report:
Anion Gap (AG) = 18 + 4 mmol/L
with a coverage factor of 2 (95% confidence)
9. Worked example No. 2
• In a creatinine clearance test, the following calculation applies:
• C = (U x V ) / (P x T )
Parameter Unit Value u
U mmol/L 10.0 0.25
V ml 1500 15
P mmol/L 0.1 0.01
T secs 86400 0
By calculation,
C = (10.0 mmol/L x 1500 ml)/(0.1 mmol/L x 24 hrs x 3600 sec)
= 1.74 ml/sec
and,
(
𝑢 𝑅
1.74
)2
=(
0.25
𝑈10.0
)2
+(
15
1500
)2
+(
0.01
0.1
)2
+(
0
86400
)2
= 0.0107
Therefore,
uR = 1.74 x 0.0107 = 0.18 ml/sec
Report:
Creatinine clearance C = 1.74 + 0.36 ml/sec with the
coverage Factor of 2 for 95% confidence
10. Non-analytical uncertainty contributors
• In medical testing, there are many
potential uncertainty contributors
which can significantly affect test
results, apart from the analytical
process itself, such as:
• Improper practice during specimen
collection
• Poor specimen handling during transit
to the laboratory
• Patient related factors like biological
variability and the presence of any drug
residues
11. Analytical uncertainty component
• The laboratory’s analytical quality comes from two factors for total
analytical error as its analytical goal:
• Intermediate precision (random error; imprecision)
• Analytical bias (systematic error)
• Intermediate precision or imprecision (ua) is estimated from its
long-term combined (pooled) standard deviation after a series of
analysis in replicates by different analysts within the laboratory on
different occasions on a stable internal quality control serum
sample.
• Analytical bias is evaluated by studying any significant difference
between the average test result against the target or certified value
of the internal QC sample through the Student’s t-test. If there is a
significance difference between the mean result and the certified
value, then the standard uncertainty of bias (ub) is estimated.
12. Why do we consider the biological variation?
• There are many clinical uses of the test results reported.
• As a test result must be ‘fit for purpose’, there is a need to assess its clinical
goal in addition to the analytical goal.
• For some measurands, an analytical goal may not be physiologically or clinically
relevant. Then, the method imprecision study would be sufficient.
• However most medical laboratory need to identify if UM information when
reported could significantly affect clinical interpretations and patient
management.
• Hence, there is a necessity to have additional non-analytical uncertainty
component in the overall estimation of UM to set such goal.
• This additional uncertainty component is the intra-individual biological
variation of the measurand.
13. Components of biological variation (BV)
• Actually, there are two types of components of biological
variation, namely:
• Intra-individual (random fluctuation of analytes around the
setting point of each individual), sometimes termed as “within-
subject” BV;
• Inter-individual (overall variation from the different person’s
setting point), sometimes termed as “between-subject” BV.
• The laboratory’s analytical method, instrument and
reagents do not make difference in BV estimates.
14. Some applications of biological variation
Evaluating the clinical significance of changes in consecutive test
results from an individual in monitoring treatment progress
Setting quality specifications for analytical performance (usually
targeting the analytical goal as a percentage of BV) to satisfy the
general needs of diagnosis and monitoring
Validating new procedures in a laboratory
Assessing the usefulness of population-based reference values
Determining which type of specimen (e.g. plasma, serum, 24-hr
urine, first-morning urine) is optimal for analyzing a specific
constituent
15. Relationship of analytical imprecision and biological
variation
• There are 3 levels of analytical goal for long-term imprecision
(CVa) based on intra-individual biological variation (CVi):
• Optimum: CVa < 0.25 x CVi
• Desirable: CVa < 0.50 x CVi
• Minimum: CVa < 0.75 x CVi
• For analytes with unavailable CVi data, other criteria may be
used, such as relative performance in inter-laboratory
comparison studies or proficiency testing programs, certified
reference interval, professional clinical opinion, etc.
16. Relationship of analytical imprecision and biological
variation
• If test results are interpreted using reference or clinical decision
values determined by a different method, bias should be considered
as a component of uncertainty to be estimated.
• The inter-individual biological variation CVg is to be incorporated.
• Like imprecision, we have to ensure the three levels of analytical
goal for bias (CVb) based on biological variation are met:
• Optimum:
• Desirable:
• Minimum:
𝐶𝑉𝑏 ≤ 0.125 × 𝐶𝑉𝑖
2
+ 𝐶𝑉𝑔
2
𝐶𝑉𝑏 ≤ 0.250 × 𝐶𝑉𝑖
2
+ 𝐶𝑉𝑔
2
𝐶𝑉𝑏 ≤ 0.375 × 𝐶𝑉𝑖
2
+ 𝐶𝑉𝑔
2
17. Biological variation database
• There are study findings which indicate that the factors
inherent to the subject, such as sex, age, race,
geographical location, do not produce changes in BV
results
• Practically all the currently available estimates of
biological variation (more than 300 analytes) have been
compiled in a database:
https://www.westgard.com/biodatabase1.htm
• The intra-individual (or within-subject) coefficients of
variation (in %) from the database can be used to
evaluate the clinical significance of changes in two
consecutive results from a patient, in addition to the
analytical variation.
18.
19.
20. Summation of analytical uncertainty &
biological variation
• If the combined estimate of coefficient of variation of
reported test result (CVT) covers both the CV of analytical
imprecision (CVa) and that of within-subject biological
variation (CVi) for a comparison of two consecutive test
results from a patient, we then have:
• CVT
2 = CVa
2 + CVi
2
• Note: If test results are clinically interpreted by comparison
with reference or previous values produced by the same
analytical method, analytical bias component is not required.
21. Worked example No. 3
• Plasma alkaline phosphatase (ALP) activity for a patient:
• 1st day’s result : 95 U/L; 3rd day’s result : 108 U/L
• UM for ALP : CVa = 1.45% at QC sample mean : 87 U/L
• ALP intro-individual BV (given by Westgard website): CVi = 6.45%
• Were these two sets of daily test results of any significant
different?
• Sum of analytical & BV as CV’s:
• CVT = (CVa
2 + CVi
2) = (1.452 + 6.452) = 6.61%
22. Worked example No. 3 (contd.)
• If the two test results are analytically and biologically different, the “critical difference”
or Reference Change Value (RCV) is to be:
• 1.96 x 2 x (CVa
2 + CVi
2) or 2.77 x (CVa
2 + CVi
2) for 95% confidence
• That means the two results need to differ by 2.77 x 6.61% = 18.3%
• Now, the first day’s result was 95 U/L, thus
• 95 U/L + (95 U/L x 18.3%) = 95 + 17.4 = 112 U/L
• and, 95 U/L - (95 U/L x 18.3%) = 95 - 17.4 = 77.6 U/L
• So, the critical range of expanded uncertainty is [77.6 U/L, 112 U/L]
• The second result would have to be at least 112 U/L for there to be 95% confidence that
it was both analytically and biologically different from the first result. We can therefore
conclude that the test results of 95 U/L and 108 U/L are analytically different, but
probably not biologically different.
23. A note on
Reference
Change Value
RCV
• Some references recommend to use a one-tail test with a
probability risk (α) of 5% error, instead of a two-tail test.
• In that case, the Reference Change Value has a formula:
• 1.645 x 2 x (CVa
2 + CVi
2) or 2.33 x (CVa
2 + CVi
2)
• By using this new formula, the two results need to differ by
2.33 x 6.61% = 15.4%
• Now, the first day’s result was 95 U/L, thus the upper critical
limit is:
• 95 U/L + (95 U/L x 15.4%) = 95 + 14.7 = 109.7 U/L
• Same conclusion could be made based on the new RCV.
ααα/2α/2
The choice of 1-tail or 2-
tail probability depends
on the alternative
hypothesis H1
24. How do we get the factor 1.96 x 2 or 1.645 x 2?
• A measurement result, X, is associated with a standard deviation +sX
• The duplicate result, Y, on the same sample is also associated with the
same standard deviation +sX
• When an average value is calculated for X and Y, we have a total
variance of
• sX
2 + sX
2 = 2sX
2
• Therefore, the pooled standard deviation is 2 x sX
• The choice of coverage factor k = 1.96 or 1.645 depend on either 2-tail
or 1-tail hypothesis testing, assuming a normal distribution with an
error probability α = 0.05 or 95% confidence.
25. Conclusion
• It is a good practice to maintain a list of measurands with their
respective RCV (reference change value) to define healthy and
non-healthy situations.
• Remember that the RCV is applied for the target analyte in each
pair of consecutive results from the same patient
• The laboratory information system (LIMS) can be set to flag off
the second report with a predefined signal after the numeric
result of the target analyte is keyed in.
• Most important challenge for the medical personnel is how to
explain the UM concept to requesting doctors in a clinically
meaningful way.
26. References
• “Uncertainty of Measurement in Quantitative
Medical Testing – A Laboratory Implementation
Guide” by Australasian Association of Clinical
Biochemists, November 2004
• “Application of Biological Variation – A Review” by
Ricos C. et al, Biochemia Medica 2009, 19(3):250-9
• “Desirable Biological Variation Database
specifications” by James Westgard,
https://www.westgard.com/biodatabase1.htm