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Quality Control

Quality Assessment 2. Quality Control. What is Quality Control?.

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Quality Control

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  1. Quality Assessment 2 Quality Control

  2. What is Quality Control? • Quality Control in the clinical laboratory is a system designed to increase the probability that each result reported by the laboratory is valid and can be used with confidence by the physician making a diagnostic or therapeutic decision.

  3. Quality Control Programs • The goal of a well-defined QC system is to detect immediate errors in an analytical run while minimizing the number of false rejections. • The simplest type of QC procedure uses one rule to reject the analysis based on QC results falling outside of a range such as the 95% range. • These facts are based on probability that the correct decision was made 95% of the time when results that fall within this range are accepted. • When testing is qualitative—that is, positive or negative—a simple one-rule policy is acceptable.

  4. Control Of The Analytical Quality Using Stable Control Materials • The performance of analytical methods can be monitored by analyzing specimens whose concentrations are known and then by comparing the observed values with known values. • The known values are usually represented by an interval of acceptable values, or upper and lower limits for control (control limits) • When the observed values fall within the control limits – analysis is working properly • When the observed value fall outside the control limits the analyst should be alerted to the possibility of problems in the analysis.

  5. Control Of The Analytical Quality Using Stable Control Materials • QA includes analyzing known samples called quality control (QC) samples along with unknown (patient) samples to test for analytical problems. • When QC samples do not produce accurate and precise results, it can be assumed that any patient results obtained at the same time are also erroneous. • Following a set of guidelines for acceptance or rejection of patient results based on the QC results helps to assure reliability of the analysis.

  6. Standards and Controls • Standard • A substance that has an exact known value and that, when accurately measured, can produce a solution of an exact concentration • Not usually used on a daily basis • Used to calibrate new instruments, recalibrate instruments after repair, at manufacturer’s recommended intervals, or if a method is out of control

  7. Control • A solution that contains the same constituents as those being analyzed in the patient sample • Most are commercially produced from pooled sera • The manufacturer has analyzed each lot of serum for a variety of test components and the expected range of assay values for each component is provided to the laboratory when shipped

  8. Control • Controls are analyzed with each patient test or batch of tests and the results are compared with the manufacturer’s range of values • For most tests, a “normal” control and an “abnormal” control are analyzed with each patient test or batch of tests • Results are plotted on a QC record called a Levey-Jennings Chart

  9. Quality control (QC) procedures • Quality control (QC) procedures function by detecting analytical errors; • ideally any error large enough to invalidate the medical usefulness of laboratory results should be detected. • The measurement of QC samples will detect problems of precision and accuracy over time. • Interpretation of control results is based on using specific rules for acceptance and rejection of QC results, documenting results and decisions, and having a process for resolving problems that result in rejection of results.

  10. The basic quality control statistics

  11. The mean (X) is the sum of the control observations (x1, x2, ... xi) divided by the number (n) of observations: Practice calculating the mean of the following five results: 45, 44, 45, 48, 39 (n 5). (45 44 45 48 39)/5 221/5 44.2 The standard deviation (SD or s) is the measure of the dispersion of a group of values around the mean. It is derived from the curve of normal distribution. It is used to assess precision. Practice calculating the SD of the following results: 45, 44, 45, 48, 39 (n 5). SD (s) = 3.3 Statistical Calculations

  12. Standard Deviation (SD) • It is the commonly used measure of dispersion • Gives a measurement of dispersion around the mean • Calculation: • X ± 1SD includes 68% observations • X ± 2SD includes 95% observations • The higher the SD, the more the observation varies (deviates) from the mean.

  13. Percentage Coefficient of Variation (%CV) • Measures level of imprecision • Assess the reliability of a given method based on preset values % CV =

  14. Gaussian Probability Distribution • It is very important in statistics • When the distribution of values around the mean are plotted graphically and are symmetrical this is referred to as a Gaussian curve. • Statistical procedures are based on Gaussian probability distribution.

  15. 68-95-99.7 Rule • For any normal curve with mean mu and standard deviation sigma: • 68 percent of the observations fall within one standard deviation sigma of the mean. • 95 percent of observation fall within 2 standard deviations. • 99.7 percent of observations fall within 3 standard deviations of the mean.

  16. How Are These Values Used? Mean and SD are calculations that assess the accuracy and precision of the analysis statistically. Errors of accuracy may be assessed by examining changes in the measured concentration of the control over time and comparing these concentration values to mean and SD ranges of the control. By contrast, an imprecision problem will be demonstrated by an increase in the SD and %CV of results of the control concentration over time.

  17. Control charts are simple graphical displays in which the observed values are plotted versus the time when the observations are made. The control limits are calculated from the mean (x) and standard deviations (s) General Principles Of Control Charts

  18. The most commonly used charts indicate day or run number on the X-axis and observed QC concentration, indicating mean, and SD ranges on the Y-axis One example of a QC chart is the Levey-Jennings control chart. By plotting the daily QC results, one can visualize the deviation of the results from the mean, typically noting when the results are greater than 2 SD from the mean on a daily basis. General Principles Of Control Charts

  19. Control Rules The criteria used to determine acceptability of each control measurement are termed control rules, or QC rules. Westgard defined QC rules based on the earlier work of Shewhart, Levey, and Jennings. Use of multiple control rules (commonly referred to as Westgard rules) can improve the performance of the control system.

  20. Control Rules • Individual rules have different capabilities for detecting different types of analytical error. • A control rule or control decision is used to judge whether analysis is performing well. • Ranges can be tighter if clinical requirements are more demanding, but the SD limit should not be set so narrow that excessive time and resources are wasted checking false rejections.

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  22. Errors in Laboratory Testing Random or Systematic Random Errors – cannot be absolutely identified (Ex. Differences in techniques between workers, specimen characteristics, etc.) Systematic Errors – variation that may make results consistently higher or lower than the mean value for a control (Ex. Trouble with the instrument, deteriorated reagents, etc.)

  23. QC can be achieved through: Internal Quality Control (IQC) External Quality Assessment (EQA) Types of Quality Control

  24. Internal Quality Control Program • An internal quality control program depend on the use of: • internal quality control (IQC) specimens, • LJ Control Charts, • and the use of statistical methods for interpretation.

  25. Impact of Internal Quality Control Continuous detection and rectification of the Analytical Process. Reagent-Equipment-Personnel-Specimen Ensure the degree of both precision & accuracy of your results Assure the quality and clinical applicability of your laboratory reports Generate objective evidence of your analytical performance.

  26. External Quality Assessment (EQA) Inter laboratory Complimentary to IQC Maintain the long-term accuracy of the analytical process.

  27. Impact of External Quality Assessment Continuous quality improvement Independent laboratory audit Objective evidence of a laboratory analytical performance Assess the results which the laboratory delivers Encourages the search for the root cause of unacceptable performance.

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