MLAB 2401: Clinical Chemistry - PowerPoint

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					MLAB 2401: Clinical
Chemistry


Quality Control, Quality
Assessment and Statistics


                            1
Introduction

   When evaluating laboratory results, how do we determine that is
    normal or acceptable? In other words: What is “normal” or
    “OK”?

   When does a laboratory test result become “weird” or “abnormal” ?
    When do be become uncomfortable with a result?

   At some point we have to draw a “line in the sand” … on this side
    of the line you’re normal … on the other side of the line you’re
    abnormal. Where and how do we “draw the line” ?

   Answer: Statistics are used to determine the lines of ‘normal’
    and ‘acceptable’.


                                                                        2
Introduction

   Statistics is used to draw “lines in the sand” for patient
    specimens, control specimens and calibrators

   If the results are “normal” we ‘re comfortable about them
    and don’t worry

   But if they’re abnormal, we’re uncomfortable and we
    fear that there is something wrong with the patient or that
    something is wrong with the test procedure




                                                                 3
   Statistical Concepts
       Statistics is a (science of )branch of mathematics that collects, analyzes,
        summarizes and presents information about “observations.”

       In the clinical lab, these “observations” are usually numerical test results

       A statistical analysis of lab test data can help us to define normal ranges
        for patient’s ( normal and abnormal ) and acceptable ranges for control
        specimens ( “in” and “out” of control )




                                                                                   4
Statistical Formulas
  Standard     Deviation (SD)
      Is a mathematical expression of the dispersion of a
       group of data.




                        x  x
                                       2

        SD 
                           n  1


                                                         5
                                                   x  x
                                                               2

    Standard Deviation :               SD 
                                                     n  1


n   =       the number of observations (how many numerical values )

Σ   =       the sum of … in this case, the sum of all the
                                                            x    x
                                                                        2




x       =   the mean value


X   = the value of each individual observation
                     x    x
                                 2




The Standard Deviation is an expression of dispersion … the greater the
SD, the more spread out the observations are



                                                                            6
Statistical Formulas
   Coefficient of Variation (CV)
       a way of expressing standard deviation in terms of average
        value of the observations used in the calculation


                CV% =

                           Sandard Deviation
                                                  100
                                 Mean




                                                                     7
Coefficient of Variation (CV) %

•The CV allows us to compare different sets of
observations relative to their means.
  •You can’t use the SD to compare different groups of data because they
  are measuring different observations - you can’t compare apples to
  oranges. The CV can turn all groups of observations into a percentage of
  their relative means - everything gets turned into “oranges.”

  The smaller the CV, the more reproducible the
  results: more values are closer to the mean.




                                                                             8
Bias
    Bias – the amount by which an analysis
    varies from the correct result.
     Example,  If the Expected Value is 50 units,
      and the result of an analysis is 47, the bias is
      3 units.




                                                         9
Quality Assurance (Assessment) & Quality Control

   Quality Assurance(Assessment) (QA)
     Includes pre-analytic, analytic and post analytic
      factors
     “All systematic actions necessary to provide adequate
      confidence that the laboratory services will satisfy
      given medical needs for patient care.” – Bishop

     In other words: Quality assurance is an all inclusive
      / comprehensive system monitoring the accuracy of
      test results where all steps before, during and after
      the testing process are considered.


                                                         10
Quality Assurance & Quality Control
   Quality Control (QC)
      Crudely, it is the system we use in the clinical
      laboratory to recognize and minimize the analytic
      errors.
     QC system is to monitor the analytical process; detect
      errors during the analysis and prevent reporting of
      erroneous test results.
     It uses statistical analysis of test system data
     Requires following published rules (ie Westgard
      Rules)



                                                          11
Other times we think of QC
   Daily
     Ensures  instrument are reagents are
      functioning properly
 Establishment of a Reference Range
 Validation of a new reagent lot and/or
  shipment
 Following instrument repair


                                             12
Establishment of a QC system
   Collecting data
     Run   assay on control sample & manually
        enter control results on chart
            One chart for each analyte and for each level of
             control



    




                                                                13
Establishment of a QC system
   Collecting data
     Many  modern chemistry analyzers have
        computer program that maintains the QC log.
            i.e Dade Dimension

    




                                                  14
Collecting Data for QC
   Charting techniques
     Levey    Jennings chart is a graph that plots QC
        values in terms of how many standard
        deviations each value is from the mean




    




                                                     15
Collecting Data for QC
Youden Plot
   Allows you to compare the relationship of each level’s mean to the group
    performance
   Shaded areas represent 1SD, 2SD, 3SD values
   Group mean of Level I and Level II represented by intersection in center
   Comparison to group mean represented at intersection of dark lines




                                                                               16
Collecting Data for QC
   Minimum number of determinations
     Statistically should have at least 20
      determinations to establish acceptable mean
      and practical standard deviation.




                                                17
Use of Standard Deviation
   Once you have determined the standard
    deviation, must use the information to
    evaluate current/ future analysis.

   Most labs make use of ± 2 SD or 95%
    confidence limit. To put this into a
    workable form, you must establish the
    range of the ± 2 SDs
                                             18
Use of Standard Deviation to obtain the
‘range of acceptable results”
   mean of group of control values = 104 mg/dL
   Standard Deviation = ± 5 mg/dL
   Determine the Range of ± 2SD; (which will allow
    you to evaluate acceptability of performance of
    the control on subsequent days.)
   Is a control value of 100 mg/dL acceptable?




                                                  19
But what if your control specimen is “out of control?”

   “Out of control” means that there is too much dispersion in
    your result compared with the rest of the results

   This suggests that something is wrong with the process
    that generated that observation

   Patient test results cannot be reported to physicians when
    there is something wrong with the testing process that is
    generating inaccurate reports

   Remember … No information is better than wrong
    information



                                                             20
But what if your control specimen is “out of control?”


   Corrective methods
        Things that can go Wrong     Corrective Action

        Instrument malfunction       Identify malfunction and fix

        Reagents: preparation,       New reagents
        contamination, volume

        Tech error                   Identify error and repeat
                                     test

        Control specimen is old or   Use new control
        prepared improperly


                                                                    21
Establishment of Reference Ranges

   Reference ranges – the ‘normals’
      The normal or expected value for patients.

      Are defined as being within +2 Standard Deviations from the
       mean
      A large sampling of clinical normal representatives.


   Each lab must establish its own reference ranges based on local
    population




                                                                      22
Establishment of Reference Ranges

Factors affecting reference ranges:
      Age
      Sex
      Diet
      Medications
      Physical activity
      Pregnancy
      Personal habits ( smoking, alcohol )
      Geographic location ( altitude )
      Body weight

      Laboratory instrumentation ( methodologies )
      Laboratory reagents




                                                      23
Terms
   Delta check
      Comparison of individual patient results throughout the day or
       week with computer detection of changes from earlier individual
       patient results
   AMR= Analytical Measurement Range
      Range of analyte values that a method can directly measure on
       the specimen without any dilution, concentration or other
       pretreatment
   CRR= Clinical Reportable Range
      Range of analyte values that a method can report as a
       quantitative result, allowing for specimen dilution, concentration,
       or other pretreatment used to expand the direct AMR.

                                                                         24
“External” QC
   Proficiency Testing
       Determination of laboratory testing performance by means of
        intralaboratory comparisons
       CAP requirement
       Must be integrated within routine workload and analyzed by
        personnel who are running the tests.
       Ongoing evaluation of results to correct for unacceptable results




                                                                        25

				
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