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Quality Control Introduction

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Quality Control Introduction

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    1. Quality Control – Introduction

    2. The Quality System

    4. Quality Control Definitions Qualitative Quality Control Quantitative QC – How to implement Selection and managing control materials Analysis of QC data Monitoring quality control data

    5. What is Quality Control? Process or system for monitoring the quality of laboratory testing, and the accuracy and precision of results Routinely collect and analyze data from every test run or procedure Allows for immediate corrective action

    6. Designing a QC Program – Establish written policies and procedures Corrective action procedures Train all staff Design forms Assure complete documentation and review

    7. Qualitative vs.Quantitative Quantitative test measures the amount of a substance present Qualitative test determines whether the substance being tested for is present or absent Qualitative tests Tests that do not have measurable endpoints, for example: Growth-no growth Positive-negative Color change Qualitative tests Tests that do not have measurable endpoints, for example: Growth-no growth Positive-negative Color change

    8. Qualitative QC Quality control is performed for both, system is somewhat different Controls available Blood Bank/Serology/Micro RPR/TPHA Dipstick technology Pregnancy

    9. Stains, Reagents, Antisera Label containers contents concentration date prepared placed in service expiration date/shelf life preparer

    10. Media Preparation Record amount prepared Source Lot number Sterilization method Preparation date Preparer pH Expiration date

    11. Microbiology QC Check: Sterility Ability to support growth Selective or inhibitory characteristics of the medium Biochemical response Frequency Test QC organisms with each new batch or lot number Check for growth of fastidious organisms on media of choice – incubate at time and temp recommended RECORD Results on Media QC form Check for sterility – incubate uninoculated plate – continue only if no growthCheck for sterility – incubate uninoculated plate – continue only if no growth

    12. Quality Control: Stains and Reagents Gram stain QC Use gram positive and gram negative organisms to check stain daily Other : Check as used – positive and negative reactions

    13. Stock QC organisms Organisms to be maintained must be adequate to check all media and test systems. E. coli – MacConkey, EMB, susceptibility tests Staphylococcus aureus – Blood agar, Mannitol Salt, susceptibility tests Neisseria gonorrhoeae – chocolate, Martin-Lewis Discussion of where to get control organisms Discuss media used in their labs and what organisms should be used to test it. ATCC – best, but other sources may be available. May use known organisms from their lab and make stock cultures??Discussion of where to get control organisms Discuss media used in their labs and what organisms should be used to test it. ATCC – best, but other sources may be available. May use known organisms from their lab and make stock cultures??

    14. Detecting Errors Many organisms have predictable antimicrobial test results Staphylococcus spp. are usually susceptible to vancomycin Streptococcus pyogenes are always susceptible to penicillin Klebsiella pneumoniae are resistant to ampicillin I think you could include a pretty long list here, that are worldwide in application.I think you could include a pretty long list here, that are worldwide in application.

    15. Sources of Error If you encounter an unusual pattern rule out error by checking identification of organisms repeat antimicrobial susceptibility test Report if repeat testing yields same result, or refer the isolate to a reference laboratory for confirmation Establish patterns in your patient population and advise health care providers. So providers can initiate treatment if necessary before susceptibility testing is completeEstablish patterns in your patient population and advise health care providers. So providers can initiate treatment if necessary before susceptibility testing is complete

    16. Quality Control – Quantitative Tests How to implement a laboratory quality control program

    17. Implementing a QC Program –Quantitative Tests Select high quality controls Collect at least 20 control values over a period of 20-30 days for each level of control Perform statistical analysis Develop Levey-Jennings chart Monitor control values using the Levey-Jennings chart and/or Westgard rules Take immediate corrective action, if needed Record actions taken Monitor control values on a run-by-run basis using the Levey-Jennings chart and/or Westgard rules Develop Levey-Jennings chart using the calculated values for mean, SD, and rangeMonitor control values on a run-by-run basis using the Levey-Jennings chart and/or Westgard rules Develop Levey-Jennings chart using the calculated values for mean, SD, and range

    18. Selecting Control Materials Calibrators Has a known concentration of the substance (analyte) being measured Used to adjust instrument, kit, test system in order to standardize the assay Sometimes called a standard, although usually not a true standard This is not a control

    19. Selecting Control Materials Controls Known concentration of the analyte Use 2 or three levels of controls Include with patient samples when performing a test Used to validate reliability of the test system

    20. Control Materials Important Characteristics Values cover medical decision points Similar to the test specimen (matrix) Available in large quantity Stored in small aliquots Ideally, should last for at least 1 year Often use biological material, consider bio-hazardous Values cover medical decision points – often use high and low, or normal and abnormal Values cover medical decision points – often use high and low, or normal and abnormal

    21. Managing Control Materials Sufficient material from same lot number or serum pool for one year’s testing May be frozen, freeze-dried, or chemically preserved Requires very accurate reconstitution if this step is necessary Always store as recommended by manufacturer

    22. Sources of QC Samples Appropriate diagnostic sample Obtained from: Another laboratory EQA provider Commercial product 1. QC Sample The Sources of QC Samples QC samples can be obtained from a number of different sources: Producing your own external control - requires resources because there are many validation and testing steps. Can produce very large volumes with exact specifications. Selecting a diagnostic sample - must meet QC sample requirements. Cheaper option but difficult to get very large volumes so may have to change QC samples often. This will inhibit the collection of longitudinal and batch to batch data. Obtaining a QC sample form another laboratory - saves time, but does not guarantee a well standardised and validated product. Collaboration between laboratories assists in providing the large volume required and may decrease costs. Samples provided as participation in an External QC Programme -this is the preferable option as it provides follow up and feedback on your performance in comparison to other laboratories. Commercially available controls - e.g. BBI Accurun (Boston Biomedica Inc) are purchased by the millilitre and are expensive. Samples are ‘stand alone’ because no results are collected and no feedback is provided. BBI Accurun is a very stable sample due to its production process which involves heat treatment. Because of this the sample is not as sensitive to small fluctuations and may only identify large shifts in variation shifts, similar to the positive control. 1. QC Sample The Sources of QC Samples QC samples can be obtained from a number of different sources: Producing your own external control - requires resources because there are many validation and testing steps. Can produce very large volumes with exact specifications. Selecting a diagnostic sample - must meet QC sample requirements. Cheaper option but difficult to get very large volumes so may have to change QC samples often. This will inhibit the collection of longitudinal and batch to batch data. Obtaining a QC sample form another laboratory - saves time, but does not guarantee a well standardised and validated product. Collaboration between laboratories assists in providing the large volume required and may decrease costs. Samples provided as participation in an External QC Programme -this is the preferable option as it provides follow up and feedback on your performance in comparison to other laboratories. Commercially available controls - e.g. BBI Accurun (Boston Biomedica Inc) are purchased by the millilitre and are expensive. Samples are ‘stand alone’ because no results are collected and no feedback is provided. BBI Accurun is a very stable sample due to its production process which involves heat treatment. Because of this the sample is not as sensitive to small fluctuations and may only identify large shifts in variation shifts, similar to the positive control.

    23. Types of Control Materials Assayed mean calculated by the manufacturer must verify in the laboratory Unassayed less expensive must perform data analysis “Homemade” or “In-house” pooled sera collected in the laboratory characterized preserved in small quantities for daily use

    24. Preparing In-House Controls

    25. Criteria for Developing Quality Controls for HIV Low positive Between the cut off and positive control At a level where variability can be followed Generally ~2 times the cut off QC Sample Criteria The selection of the QC sample is critical to the QC programme. A QC should be selected at a level where the sample can identify variability. When plotting serial dilutions of a high positive sample this range is the linear portion of the sigmoidal curve, between the upper and lower plateau of the graph (Refer section 1.3.2 Selecting a suitable sample dilution). Generally, an appropriate level for a QC sample is a low positive which reacts between the cut off and the low positive control. For many assays a low positive which reacts at between 2 to 3 times the cut off is appropriate. A sample at this level will identify any minor changes in assay performance which may not be easily recognised by reviewing the negative and positive kit controls. For example, if a borderline QC sample proceeds out of range in the negative direction ie. below the lower control limit, this indicates an opportunity for the specimens with OD’s below the cut off to be falsely interpreted as negative. A decrease such as this may not be evident if monitoring the negative or high positive kit controls. The sample must be of good quality, of large volume and stable for long storage.QC Sample Criteria The selection of the QC sample is critical to the QC programme. A QC should be selected at a level where the sample can identify variability. When plotting serial dilutions of a high positive sample this range is the linear portion of the sigmoidal curve, between the upper and lower plateau of the graph (Refer section 1.3.2 Selecting a suitable sample dilution). Generally, an appropriate level for a QC sample is a low positive which reacts between the cut off and the low positive control. For many assays a low positive which reacts at between 2 to 3 times the cut off is appropriate. A sample at this level will identify any minor changes in assay performance which may not be easily recognised by reviewing the negative and positive kit controls. For example, if a borderline QC sample proceeds out of range in the negative direction ie. below the lower control limit, this indicates an opportunity for the specimens with OD’s below the cut off to be falsely interpreted as negative. A decrease such as this may not be evident if monitoring the negative or high positive kit controls. The sample must be of good quality, of large volume and stable for long storage.

    26. Production of a QC Sample - Production Protocol Materials Calculation of Volume stock sample diluent QC batch Method Validation Acceptance Criteria batch stability Protocol for the Production of QCs A production protocol should be developed to ensure that a QC sample of high precision and consistent performance is the result. This is particularly important when more than one batch of the same sample will be produced over time. 1.1 Materials All materials to be used should be listed in the protocol, in particular the stock sample which will be diluted to make up the sample, must be identified. This sample should be uniquely identified and its source, location and remaining volume recorded. 1.2 Calculating Volumes The volumes of each reagent and the total volume of QC sample required must be calculated before beginning production to ensure all reagents are available. The volume of sample produced will depend on: How long the batch needs to last (eg 12 months) The sample volume required by the assay (eg 20ul for Sanofi Genelavia MIXT HIV-1/2 and 400 ul for Abbott PRISM HIV-1/2 per run) How often the assay is performed Number of laboratories using the QC sample 1.3 Method The laboratory method to define what steps are required to produce the sample should be defined and documented. 1.4 Validation The validation acceptance criteria for batch to batch variation and stability testing should be defined in the protocol and based on previous experience. Protocol for the Production of QCs A production protocol should be developed to ensure that a QC sample of high precision and consistent performance is the result. This is particularly important when more than one batch of the same sample will be produced over time. 1.1 Materials All materials to be used should be listed in the protocol, in particular the stock sample which will be diluted to make up the sample, must be identified. This sample should be uniquely identified and its source, location and remaining volume recorded. 1.2 Calculating Volumes The volumes of each reagent and the total volume of QC sample required must be calculated before beginning production to ensure all reagents are available. The volume of sample produced will depend on: How long the batch needs to last (eg 12 months) The sample volume required by the assay (eg 20ul for Sanofi Genelavia MIXT HIV-1/2 and 400 ul for Abbott PRISM HIV-1/2 per run) How often the assay is performed Number of laboratories using the QC sample 1.3 Method The laboratory method to define what steps are required to produce the sample should be defined and documented. 1.4 Validation The validation acceptance criteria for batch to batch variation and stability testing should be defined in the protocol and based on previous experience.

    27. Process for Preparing In-house Controls Serial dilution of high positive stock sample Select suitable dilution Produce large batch Test stability Test batch variation Dispense, label, store Method of QC Production A protocol should be developed which details how to carry out each step. An original stock sample of high antibody titre is tested at different dilutions to identify a suitable antibody concentration to use as a QC sample Large volumes of the selected dilution are prepared and tested on different batch numbers to validate the samples stability and reproducibility. After validation the sample is aliquoted into volumes suitable for a weeks use and stored according to protocol which will be written to assure sample stability during storage. It may be appropriate to make a multi-marker QC sample which includes more than one analyte in the sample. For example, the NRL has produced a multi-marker QC sample which includes anti-HIV1/2, anti HTLV, anti-HCV and HBsAg markers. After production of a multimarker all analytes must be validated individually. Method of QC Production A protocol should be developed which details how to carry out each step. An original stock sample of high antibody titre is tested at different dilutions to identify a suitable antibody concentration to use as a QC sample Large volumes of the selected dilution are prepared and tested on different batch numbers to validate the samples stability and reproducibility. After validation the sample is aliquoted into volumes suitable for a weeks use and stored according to protocol which will be written to assure sample stability during storage. It may be appropriate to make a multi-marker QC sample which includes more than one analyte in the sample. For example, the NRL has produced a multi-marker QC sample which includes anti-HIV1/2, anti HTLV, anti-HCV and HBsAg markers. After production of a multimarker all analytes must be validated individually.

    28. Making Suitable Dilutions 1.3 Method of QC Production 1.3.1 Making Suitable Dilutions A high antibody titre positive sample is diluted in normal human serum (NHS) or Basematrix (BioMedica BBI). The positive sample should be heat treated at 62°C for 20 minutes and filtered though a 0.2um Biological filter. A plasma diluent may need to be defibrinated, delipified and filtered (0.2um filter). An anti-bacterial agent such as Bronidox can be added to the dilutions. Check the package insert of the assay which the QC sample is to be produced for, to ensure that the preservative is appropriate and will not interfere with assay performance. A titration is conducted in tenfold or in doubling dilutions as follows. 100ul of NHS is added to each tube. 100ul of positive sample is added to the first tube. mix by moving the pipette plunger up and down at least 3 times. transfer 100ul from tube 1 to tube 2 and mix well. repeat this step for the remaining tubes. dilution in tube 10 is 1/1024 or in the case of tenfold dilution, 1010. Test each titration in the particular assay(s) for which the QC is (are) being synthesised. 1.3 Method of QC Production 1.3.1 Making Suitable Dilutions A high antibody titre positive sample is diluted in normal human serum (NHS) or Basematrix (BioMedica BBI). The positive sample should be heat treated at 62°C for 20 minutes and filtered though a 0.2um Biological filter. A plasma diluent may need to be defibrinated, delipified and filtered (0.2um filter). An anti-bacterial agent such as Bronidox can be added to the dilutions. Check the package insert of the assay which the QC sample is to be produced for, to ensure that the preservative is appropriate and will not interfere with assay performance. A titration is conducted in tenfold or in doubling dilutions as follows. 100ul of NHS is added to each tube. 100ul of positive sample is added to the first tube. mix by moving the pipette plunger up and down at least 3 times. transfer 100ul from tube 1 to tube 2 and mix well. repeat this step for the remaining tubes. dilution in tube 10 is 1/1024 or in the case of tenfold dilution, 1010. Test each titration in the particular assay(s) for which the QC is (are) being synthesised.

    29. Selecting a Suitable Sample Dilution 1.3 Method of QC Production 1.3.2 Selecting a Suitable Sample Dilution Plot the assay results against the sample dilutions to review the sigmoidal response curve. An appropriate QC dilution should be selected from the section of the graph were variability can be followed ie. the linear portion between 1/512 and 1/1638. Sample dilutions at the upper and lower plateau of the graph will not identify variation. The dilution should be between the positive kit control value and the calculated cut off value. If a QC sample is chosen with an OD which is too low, then the OD may fluctuate above and below the cut off due to normal variations. Although variation can still be monitored with this sample it is preferable to choose a QC which is consistently reactive. A sample chosen with an OD which is too high will be of limited use for border line monitoring and may not identify subtle changes. In the above example the 1/8192 dilution which produced a S/Co of 2.3 was chosen as the appropriate dilution. If the results achieved in the serial dilution are not suitable further dilutions between these titrations may be required. 1.3 Method of QC Production 1.3.2 Selecting a Suitable Sample Dilution Plot the assay results against the sample dilutions to review the sigmoidal response curve. An appropriate QC dilution should be selected from the section of the graph were variability can be followed ie. the linear portion between 1/512 and 1/1638. Sample dilutions at the upper and lower plateau of the graph will not identify variation. The dilution should be between the positive kit control value and the calculated cut off value. If a QC sample is chosen with an OD which is too low, then the OD may fluctuate above and below the cut off due to normal variations. Although variation can still be monitored with this sample it is preferable to choose a QC which is consistently reactive. A sample chosen with an OD which is too high will be of limited use for border line monitoring and may not identify subtle changes. In the above example the 1/8192 dilution which produced a S/Co of 2.3 was chosen as the appropriate dilution. If the results achieved in the serial dilution are not suitable further dilutions between these titrations may be required.

    30. Batch Production Prepare positive sample centrifuge heat inactivate Mix positive sample in diluent magnetic stirrer Bottle batch in numbered lots of suitable volume 1.3 Method of QC Preparation 1.3.3 Batch Production Centrifuge and filter (0.2um) positive sample. Positive sample may be heat inactivated at 62oC for 20 minutes. (Note: this may disturb the reactivity in some assays). Mix sample and the calculated volume of diluent for at least 1 hour on a magnetic stirrer in a biohazard cabinet, 3-4 hours may be required for volumes > 2 litres. Aliquot the batch into sterile large polypropylene storage containers such as Nalgene 250ml square bottles. Do not use polystyrene containers as these adsorb antibodies and will alter the antibody level of the QC sample. Number each bottle for future identification. Store at 4°C until ready to dispense. 1.3 Method of QC Preparation 1.3.3 Batch Production Centrifuge and filter (0.2um) positive sample. Positive sample may be heat inactivated at 62oC for 20 minutes. (Note: this may disturb the reactivity in some assays). Mix sample and the calculated volume of diluent for at least 1 hour on a magnetic stirrer in a biohazard cabinet, 3-4 hours may be required for volumes > 2 litres. Aliquot the batch into sterile large polypropylene storage containers such as Nalgene 250ml square bottles. Do not use polystyrene containers as these adsorb antibodies and will alter the antibody level of the QC sample. Number each bottle for future identification. Store at 4°C until ready to dispense.

    31. Stability Testing Assess the rate of deterioration 1.3 Method of QC Production 1.3.4 Testing the Stability of the QC If the dilution selected is greater than 1/3000, it will be necessary to check the stability of the prepared sample to assess the rate of possible deterioration. Place aliquots of the diluted QC sample at -20?C, 4?C and room temperature (in areas where room temperature is 15-25?C) for one month. The samples in multiple aliquots are then tested throughout the month at 7, 14, 21 and 28 days. The results obtained at each temperature are reviewed and pass stability testing if the value is not statistically different from the original calculated results. The stability requirements are dependent on the future use of the sample. For example, if a sample is to be shipped to another region and may be at ambient temperature for a few weeks in transit, it is critical that the QC sample pass stability testing at this level. 1.3 Method of QC Production 1.3.4 Testing the Stability of the QC If the dilution selected is greater than 1/3000, it will be necessary to check the stability of the prepared sample to assess the rate of possible deterioration. Place aliquots of the diluted QC sample at -20?C, 4?C and room temperature (in areas where room temperature is 15-25?C) for one month. The samples in multiple aliquots are then tested throughout the month at 7, 14, 21 and 28 days. The results obtained at each temperature are reviewed and pass stability testing if the value is not statistically different from the original calculated results. The stability requirements are dependent on the future use of the sample. For example, if a sample is to be shipped to another region and may be at ambient temperature for a few weeks in transit, it is critical that the QC sample pass stability testing at this level.

    32. Batch Validation Dispense aliquots Test aliquots Confirm desired titre level compare against target value Confirm minimal batch variation acceptable if CV <20% aim for <10% 1.3 Method of QC Production 1.3.5 Batch Validation The batch must be validated to ensure it has been sufficiently mixed and that the product is homogenous. Validation of the QC sample is extensive and should include the testing of an appropriate number of aliquots to represent the total volume. For example, if 1 litre of sample is produced and stored in 5 200 ml bottles, 20 aliquots from each of the bottles should be tested. The aliquots should be numbered so the results can be traced back to the original storage bottles. Test these aliquots in at least one assay system which the QC sample was designed to quality control. Compare the results with the target value to ensure correct result. Investigate the variability of the of the QC sample batch by calculating the coefficient of variation (CV) for selected samples. The batch is acceptable if the CV of the results is less than 20% (a discussion on statistical analyses and how to calculate a CV is described further in this chapter) . This validation process is a most important step in the methodology especially for large volume batches of QC with extensive dilution (e.g. 5 litres of 1/3000 dilution).1.3 Method of QC Production 1.3.5 Batch Validation The batch must be validated to ensure it has been sufficiently mixed and that the product is homogenous. Validation of the QC sample is extensive and should include the testing of an appropriate number of aliquots to represent the total volume. For example, if 1 litre of sample is produced and stored in 5 200 ml bottles, 20 aliquots from each of the bottles should be tested. The aliquots should be numbered so the results can be traced back to the original storage bottles. Test these aliquots in at least one assay system which the QC sample was designed to quality control. Compare the results with the target value to ensure correct result. Investigate the variability of the of the QC sample batch by calculating the coefficient of variation (CV) for selected samples. The batch is acceptable if the CV of the results is less than 20% (a discussion on statistical analyses and how to calculate a CV is described further in this chapter) . This validation process is a most important step in the methodology especially for large volume batches of QC with extensive dilution (e.g. 5 litres of 1/3000 dilution).

    33. Storage of QC Samples Validated batch aliquoted into smaller ‘user friendly’ volumes for storage Establish a storage protocol: store at -20oC in use vials stored at 4oC use 0.5 ml vial maximum of one week freeze-dried (requires accurate reconstitution) chemically preserved 1.3 Method of QC Production 1.3.6 Storing QC Samples The QC sample is dispensed into labelled aliquot's of a smaller ‘user friendly’ volume. Aliquot's in use can be stored at 4?C. These should be discarded after 7 days and a new 1ml vial introduced. By aliqoting the sample into 1ml volumes all but the QC sample in use can be stored at -20?C. This prevents freeze thawing which can affect the QC sample result. 1ml aliquot's can be sub-aliquoted if the 1ml volume will not be exhausted in a week. The label on the QC Sample should include the name of the QC Sample, and may include the expiry date (usually 12 months from the date of production) and the production laboratory’s initials. 1.3 Method of QC Production 1.3.6 Storing QC Samples The QC sample is dispensed into labelled aliquot's of a smaller ‘user friendly’ volume. Aliquot's in use can be stored at 4?C. These should be discarded after 7 days and a new 1ml vial introduced. By aliqoting the sample into 1ml volumes all but the QC sample in use can be stored at -20?C. This prevents freeze thawing which can affect the QC sample result. 1ml aliquot's can be sub-aliquoted if the 1ml volume will not be exhausted in a week. The label on the QC Sample should include the name of the QC Sample, and may include the expiry date (usually 12 months from the date of production) and the production laboratory’s initials.

    35. Quality Control -Quantitative Analysis of QC Data

    36. How to carry out this analysis? Need tools for data management and analysis Basic statistics skills Manual methods Graph paper Calculator Computer helpful Spreadsheet Important skills for laboratory personnel

    37. Analysis of Control Materials Need data set of at least 20 points, obtained over a 30 day period Calculate mean, standard deviation, coefficient of variation; determine target ranges Develop Levey-Jennings charts, plot results

    38. Establishing Control Ranges Select appropriate controls Assay them repeatedly over time at least 20 data points Make sure any procedural variation is represented: different operators different times of day Determine the degree of variability in the data to establish acceptable range

    39. Measurement of Variability A certain amount of variability will naturally occur when a control is tested repeatedly. Variability is affected by operator technique, environmental conditions, and the performance characteristics of the assay method. The goal is to differentiate between variability due to chance from that due to error.

    40. Measures of Central Tendency Data are frequently distributed about a central value or a central location There are several terms to describe that central location, or the ‘central tendency’ of a set of data

    41. Measures of Central Tendency Median = the value at the center (midpoint) of the observations Mode = the value which occurs with the greatest frequency Mean = the calculated average of the values

    42. Calculation of Mean

    43. Calculation of Mean: Outliers 192 mg/dL 194 mg/dL 196 mg/dL 196 mg/dL 160 mg/dL 196 mg/dL 200 mg/dL 200 mg/dL 202 mg/dL 255 mg/dL 204 mg/dL 208 mg/dL 212 mg/dL

    44. Calculation of Mean 192 mg/dL 194 mg/dL 196 mg/dL 196 mg/dL 196 mg/dL 200 mg/dL 200 mg/dL 202 mg/dL 204 mg/dL 208 mg/dL 212 mg/dL Sum = 2,200 mg/dL Mean = the calculated average of the values The sum of the values (X1 + X2 + X3 … X11) divided by the number (n) of observations The mean of these 11 observations is (2200 ? 11) = 200 mg/dL

    45. Calculation of Mean: ELISA Tests Collect optical density (OD) values for controls for each assay run Collect cutoff (CO) value for each run Calculate ratio of OD to CO (OD/CO) for each data point or observation This ratio standardizes data Use these ratio values to calculate the mean

    46. Normal Distribution All values are symmetrically distributed around the mean Characteristic “bell-shaped” curve Assumed for all quality control statistics

    47. Normal Distribution

    48. Normal Distribution

    49. Accuracy and Precision The degree of fluctuation in the measurements is indicative of the “precision” of the assay. The closeness of measurements to the true value is indicative of the “accuracy” of the assay. Quality Control is used to monitor both the precision and the accuracy of the assay in order to provide reliable results.

    50. Precise and inaccurate Precise and accurate Precision and Accuracy

    51. Imprecise and inaccurate

    52. Measures of Dispersion or Variability There are several terms that describe the dispersion or variability of the data around the mean: Range Variance Standard Deviation Coefficient of Variation

    53. Range Range refers to the difference or spread between the highest and lowest observations. It is the simplest measure of dispersion. It makes no assumption about the shape of the distribution or the central tendency of the data.

    54. Calculation of Variance (S2)

    55. Calculation of Variance Variance is a measure of variability about the mean. It is calculated as the average squared deviation from the mean. the sum of the deviations from the mean, squared, divided by the number of observations (corrected for degrees of freedom)

    56. Degrees of Freedom Represents the number of independent data points that are contained in a data set. The mean is calculated first, so the variance calculation has lost one degree of freedom (n-1)

    57. Calculation of Standard Deviation

    58. Calculation of Standard Deviation The standard deviation (SD) is the square root of the variance it is the square root of the average squared deviation from the mean SD is commonly used (rather than the variance) since it has the same units as the mean and the original observations SD is the principle calculation used in the laboratory to measure dispersion of a group of values around a mean

    59. Standard Deviation and Probability For a set of data with a normal distribution, a value will fall within a range of: +/- 1 SD 68.2% of the time +/- 2 SD 95.5% of the time +/- 3 SD 99.7% of the time

    60. Standard Deviation and Probability In general, laboratories use the +/- 2 SD criteria for the limits of the acceptable range for a test When the QC measurement falls within that range, there is 95.5% confidence that the measurement is correct Only 4.5% of the time will a value fall outside of that range due to chance; more likely it will be due to error

    61. Calculation of Coefficient of Variation The coefficient of variation (CV) is the standard deviation (SD) expressed as a percentage of the mean Ideally should be less than 5%

    62. Monitoring QC Data

    63. Monitoring QC Results Use Levey-Jennings chart Plot control values each run, make decision regarding acceptability of run Monitor over time to evaluate the precision and accuracy of repeated measurements Review charts at defined intervals, take necessary action, and document

    64. Levey-Jennings Chart A graphical method for displaying control results and evaluating whether a procedure is in-control or out-of-control Control values are plotted versus time Lines are drawn from point to point to accent any trends, shifts, or random excursions

    65. Levey-Jennings Chart

    66. Levey-Jennings Chart - Record Time on X-Axis and the Control Values on Y-Axis

    67. Levey-Jennings Chart - Plot Control Values for Each Run

    68. Levey-Jennings Chart Calculate the Mean and Standard Deviation; Record the Mean and +/- 1,2 and 3 SD Control Limits

    69. Levey-Jennings Chart - Record and Evaluate the Control Values

    70. Findings Over Time Ideally should have control values clustered about the mean (+/-2 SD) with little variation in the upward or downward direction Imprecision = large amount of scatter about the mean. Usually caused by errors in technique Inaccuracy = may see as a trend or a shift, usually caused by change in the testing process Random error = no pattern. Usually poor technique, malfunctioning equipment

    71. Statistical Quality Control Exercise Hypothetical control values (2 levels of control) Calculation of mean Calculation of standard deviation Creation of a Levey-Jennings chart

    72. When does the Control Value Indicate a Problem? Consider using Westgard Control Rules Uses premise that 95.5% of control values should fall within ±2SD Commonly applied when two levels of control are used Use in a sequential fashion

    73. Westgard Rules “Multirule Quality Control” Uses a combination of decision criteria or control rules Allows determination of whether an analytical run is “in-control” or “out-of-control”

    74. Westgard Rules (Generally used where 2 levels of control material are analyzed per run) 12S rule 13S rule 22S rule R4S rule 41S rule 10X rule

    75. Westgard – 12S Rule “warning rule” One of two control results falls outside ±2SD Alerts tech to possible problems Not cause for rejecting a run Must then evaluate the 13S rule

    76. 12S Rule = A warning to trigger careful inspection of the control data

    77. Westgard – 13S Rule If either of the two control results falls outside of ±3SD, rule is violated Run must be rejected If 13S not violated, check 22S

    78. 13S Rule = Reject the run when a single control measurement exceeds the +3SD or -3SD control limit

    79. Westgard – 22S Rule 2 consecutive control values for the same level fall outside of ±2SD in the same direction, or Both controls in the same run exceed ±2SD Patient results cannot be reported Requires corrective action

    80. 22S Rule = Reject the run when 2 consecutive control measurements exceed the same +2SD or -2SD control limit

    81. Westgard – R4S Rule One control exceeds the mean by –2SD, and the other control exceeds the mean by +2SD The range between the two results will therefore exceed 4 SD Random error has occurred, test run must be rejected

    82. R4S Rule = Reject the run when 1 control measurement exceed the +2SD and the other exceeds the -2SD control limit

    83. Westgard – 41S Rule Requires control data from previous runs Four consecutive QC results for one level of control are outside ±1SD, or Both levels of control have consecutive results that are outside ±1SD

    84. Westgard – 10X Rule Requires control data from previous runs Ten consecutive QC results for one level of control are on one side of the mean, or Both levels of control have five consecutive results that are on the same side of the mean

    85. 10x Rule = Reject the run when 10 consecutive control measurements fall on one side of the mean

    86. Westgard Multirule QC

    87. When a rule is violated Warning rule = use other rules to inspect the control points Rejection rule = “out of control” Stop testing Identify and correct problem Repeat testing on patient samples and controls Do not report patient results until problem is solved and controls indicate proper performance

    88. Solving “out-of-control” problems Policies and procedures for remedial action Troubleshooting Alternatives to run rejection

    89. Summary Why QC program? Validates test accuracy and reliability

    90. Summary: How to implement a QC program? Establish written policies and procedures Assign responsibility for monitoring and reviewing Train staff Obtain control materials Collect data Set target values (mean, SD) Establish Levey-Jennings charts Routinely plot control data Establish and implement troubleshooting and corrective action protocols Establish and maintain system for documentation

    91. The Quality System

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