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Selection, Verification and Validation of Qualitative Tests for Medical Laboratories

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CoverFINAL(1)Quality Control for Qualitative Tests in Medical Laboratories
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Description

Selection, Verification, and Validation of Qualitative Tests for Medical Laboratory

- Your Instructor: Paulo Pereira, Ph.D.https://www.westgard.com/paulo-pereira-phd.htm

- In 10 lessons, you will learn the theory, tools, and applications of selection, verification, and validation of qualitative tests.

Course Lesson

1. Introduction to qualitative tests quality control

  • In this lesson, we introduce evidence-based medicine into laboratory logic.
  • Briefly discusses the importance of regulating in vitro diagnostic (IVD) medical devices for confidence in laboratory results.

Objectives:

  • Review the evidence-based laboratory medicine
  • Recognize standardization and good practices importance
  • Understand "Emergency Use Only" validation principles
  • Recognize in vitro diagnostic (IVD) medical devices regulation

2. Principles and guidelines

  • In this lesson, you will learn the principles behind QC.
  • We will review some of the major milestones in laboratory quality control over time.
  • Briefly discusses the importance of the dynamics of quality cycles and associated practices.
  • Introduces harmonized vocabularies to the medical laboratory.
  • Finally, discuss some of the most important QC guides.

Objectives:

  • Interpretation difference between GMP and GLP
  • Learn the PDCA and TQM principles
  • Demystifying the differences between selection, validation, and verification
  • Introduction to the guidelines

3. ISO compliance

  • In this lesson, you will learn which ISO 15189 and ISO 9001 requirements are applicable in the QC of qualitative tests.
  • Primary introduction to ISO 15189 global guideline focused on selection, validation, and verification of qualitative med lab tests.
  • Interpretation of ISO 9001 in fulfilling technical requirements in qualitative tests.
  • Some myths associated with ISO standards and their application in the medical laboratory will be discussed.

Objectives:

  • Identify ISO requirements for laboratory tests
  • Demystifying some ISO pseudo-assumptions
  • Learn which harmonized QC practices are consistent with meeting these requirements
  • Know how to relate fit for purpose of results and technical requirements

4. Causes of uncertainty in qualitative tests

  • In this lesson, you will learn the principal sources of error that can cause untrue binary results
  • The impact of the analytical error on the cutoff trueness is discussed, as well as the effect of the analytical error on the accuracy of the classification of binary results
  • The importance of the “gray zone” and the associated trinary classification to minimize the impact of analytical error in the results is debated

Objectives:

  • Learn what are the most significant causes of uncertainty
  • Interpretation of analytical uncertainty components
  • Know how the analytical sensitivity and analytical specificity in NAAT
  • Identify biological uncertainty components

5. Sampling principles

  • In this lesson, you will learn the pro and cons of sampling
  • Introduction to the epidemiological prevalence
  • Techniques for collecting statistically and clinically representative samples

Objectives:

  • Recognize the importance of the representativeness of the samples (fit for purpose)
  • Know which are the best samples of individuals with and without a particular condition
  • Know the importance of the sampling dimensions for the confidence interval estimates
  • Recognize the critical role of samples for the robustness and reliability of estimates

6. Performance of binary classification tests

  • In this lesson, we move to the discussion and application of models for calculating the accuracy of the condition, such as diagnostic accuracy
  • We will focus on clinical sensitivity and clinical specificity
  • However, we will also discuss the physician's perspective through predictive values
  • The importance of the confidence interval will also be discussed. We explore misevaluations implications

Objectives:

  • Learn how to calculate and interpret clinical sensitivity and clinical specificity
  • Understand the limitations of estimates with poorly representative samples of the population
  • Identify the importance of computing the confidence interval
  • Understand how to assess the uncertainty of binary results

7. Agreement of binary classification tests

  • In this lesson, we concentrate on the computation of binary results agreement
  • The determination should only occur when it is not possible to calculate the condition's accuracy
  • The misinterpretation can lead to weakly sustained decisions. For example, when referring to "clinical sensitivity" in real cases of agreement of positive results

Objectives:

  • Know how to calculate and interpret concordances
  • Recognize the limitations of this approach
  • Recognize the importance of performance to comparative testing for a lower risk of misestimation
  • Know how to interpret the confidence interval

8. Condition accuracy by analyzing numerical data

  • In this lesson, you learn the importance of delta value assessment to differentiate mainly tests with identical clinical accuracy
  • The delta value is associated with different levels of misclassification risk of binary results

Objectives:

  • Learn when delta evaluation is important for binary results
  • Know how to interpret the positive delta
  • Know how to interpret the negative delta
  • Assess the risk of false results in a series of qualitative tests

9. Seronegative window period

  • In this lesson, we introduce the seroconversion window period using a binary and trinary results logic
  • Recognize the seroconversion period as a primary source of biological bias

Objectives:

  • Identify the pros and limitations of this approach
  • Evaluate the seroconversion period with binary results
  • Assess the seroconversion period with trinary results
  • Characterize laboratory methods generations based on the window period

10. Limit of detection in nucleic acid amplification tests

  • In this lesson, we introduce the evaluation of the limit of detection in nucleic acid amplification tests (NAAT).
  • The statistical models are based on logit regression, probit regression, and hit rate.

Objectives:

  • Learn the methodology for estimating the limit of detection in NAAT
  • Introduction to logit regression, probit regression, and hi-rate
  • Know how to determine the limit of detection through probit regression
  • Know how to evaluate the detection limit

Material:

  • Video presentations (1920×1080)
  • Lesson handouts
  • Self-assessment questions
  • Lesson quizzes
  • Spreadsheets
  • Terminology and vocabulary
  • References
  • List of abbreviations and acronyms, and
  • Final exam

Evaluation:

  • This is a comprehensive exam covering all 10 lessons and comprises 40 multiple-choice questions
  • The student will have three attempts to have a minimum score of 70 values
  • Trial questions are chosen at random
  • The test must be completed within 2 hours

Please allow 1 to 3 business days for us to review and confirm your enrollment in the course.

The course is housed on a Moodle portal.

You will receive additional emails once access has been granted and your information has been transferred over to the portal.