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MLAB 2401: Clinical Chemistry

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Clinical Chemistry: Techniques, principles, Correlations. Baltimore: Wolters Kluwer Lippincott Williams & Wilkins. ... QC systems monitor the analytical process; ... – PowerPoint PPT presentation

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Title: MLAB 2401: Clinical Chemistry


1
MLAB 2401 Clinical Chemistry
  • Quality Control, Quality Assessment and Statistics

2
Quality Assurance/Assessment (QA)
  • An all inclusive / comprehensive system
    monitoring the accuracy of test results where all
    steps before, during and after the testing
    process are considered. Includes pre-analytic,
    analytic and post analytic factors
  • Essentials include commitment to quality,
    facilities, resources, competent staff, and
    reliable procedures, methods and instrumentation
  • Provides a structure for achieving lab and
    hospital quality goals

3
Quality Control (QC)
  • QC systems monitor the analytical process detect
    and minimize errors during the analysis and
    prevent reporting of erroneous test results.
  • It uses statistical analysis of test system data
  • Requires following published rules
  • Westgard Rules

4
Types of QC
  • Internal
  • External
  • Daily
  • Establishment of reference ranges
  • Validation of a new reagent lot and/or shipment
  • Following instrument repair
  • Proficiency testing
  • Determination of laboratory testing performance
    by means of intralaboratory comparisons
  • CAP, CLIA, The Joint Commission 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
  • Used to access employee competency

5
Pre-Analytical Analytical Causes of Error
6
Post- Analytical Causes of Error
  • Incorrect reference values
  • Physician not notified of a panic or critical
    value
  • Incorrect interpretation of lab results by
    physician
  • Incorrect data entry of lab result

7
Introduction to Statistical Analysis
  • 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 we become uncomfortable
    with a result?
  • At some point we have to draw a line in the
    sand on this side of the line youre normal
    on the other side of the line youre
    abnormal. Where and how do we draw the line ?
  • Answer Statistics are used to determine the
    lines of normal and acceptable.

8
Introduction to Statistical Analysis
  • Statistics is used to draw lines in the sand
    for patient specimens, control specimens and
    calibrators
  • If the results are normal were comfortable
    about them and dont worry
  • But if theyre abnormal, were uncomfortable and
    we fear that there is something wrong with the
    patient or that
  • something is wrong with the test procedure

9
  • 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
  • Reference ranges for patients ( normal and
    abnormal )
  • Acceptable ranges for control specimens ( in
    and out of control )

10
Measures of Central Tendency
  • Mean (x) - the mathematical average of a group
    of numbers, determined by adding a group of
    numbers (events) and dividing the result by the
    number of events
  • Median - determined as the middle of a group of
    numbers that have been arranged in sequential
    order. That is to say, there are an equal number
    of numbers on either side of the middle number.
    In an odd of observations, it is the middle
    observation. In an even of observations,
    average the two middle values.
  • Mode - the number that appears most frequently
    in a group of numbers. There can be more than
    mode, or none at all.
  • For a visual illustration of these terms visit
    http//video.google.com/videoplay?docid5206457578
    534296681

11
Gaussian/Normal Distribution
  • All values are symmetrically distributed around
    the mean
  • Characteristic bell-shaped curve
  • Assumed for all quality control statistics

12
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13
Accuracy and Precision
  • The degree of fluctuation in the measurements is
    indicative of the precision of the assay.
  • Precision-refers to the ability to get the same
    (but not necessarily true) result time after
    time.
  • The closeness of measurements to the true value
    is indicative of the accuracy of the assay.
  • Accuracy - An accurate result is one that is the
    true result.

14
Precise and Accurate
15
Precision and Accuracy
  • Precise and inaccurate
  • Imprecise and inaccurate

Random Error
Systematic Error
16
Systematic error
  • Systematic change in the test system resulting in
    a displacement of the mean from the original
    value
  • Systematic error of an analytic system is
    predictable and causes shifts or trends on
    control charts that are consistently low or high

17
Causes of Systematic Error
  • Change in reagent or calibrator lot numbers
  • Wrong calibrator values
  • Improperly prepared reagents
  • Deterioration of reagents or calibrators
  • Inappropriate storage of reagents or calibrators
  • Variation in sample or reagent volumes due to
    pipettor misalignments
  • Variation in temperature or reaction chambers
  • Deterioration of photometric light source
  • Variation in procedure between technologists

18
Random Error
  • Imprecision of the test system causing a scatter
    or spread of control values around the mean

19
Causes of Random Error
  • Air bubbles in reagent
  • Improperly mixed reagents
  • Reagent lines, sampling, or reagent syringes
  • Improperly fitting pipette tips
  • Clogged or imprecise pipetter
  • Fluctuations in power supply

20
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.

21
Statistical Formulas
  • Standard Deviation (SD)
  • Is a mathematical expression of the dispersion of
    a group of data around a mean.

22
Standard Deviation
n the number of observations (how many
numerical values ) S the sum of in
this case, the sum of
the mean value
X the value of each individual
observation The Standard Deviation is an
expression of dispersion the greater the SD,
the more spread out the observations are
23
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

24
Statistical Formulas
  • Coefficient of Variation (CV)
  • Indicates what percentage of the mean is
    represented by the standard deviation
  • Reliable means for comparing the precision or SD
    at different units or concentration levels
  • Expressed as a percentage
  • CV
  • Standard deviation X 100
  • mean

25
Coefficient of Variation (CV)
Analyte FSH Concentration SD CV
1 0.09 9.0
5 0.25 5.0
10 0.40 4.0
25 1.20 4.8
100 3.80 3.8
  • The smaller the CV, the more reproducible the
    results more values are closer to the mean.
  • Useful in comparing 2 or more analytical methods
  • Ideally should be less than 5

26
Establishment of a QC System
  • Two or three levels of control material used
  • A control is a material or preparation used to
    monitor the stability of the test system within
    predetermined limits
  • Measure of precision and reproducibility
  • Purpose verify the analytic measurement range of
    instrument for a specific analyte

27
Establishment of a QC System
  • Control material matrix should resemble actual
    specimens tested
  • Lyophilized/liquid
  • Assayed
  • Mean calculated by the manufacturer
  • Must verify in the laboratory
  • Unassayed
  • Less expensive
  • Must perform data analysis in house

28
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

29
Establishment of a QC system
  • Collecting data
  • Many modern chemistry analyzers have computer
    program that maintains the QC log.
  • i.e Dade Dimension

30
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

31
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

32
So, how do we determine the range of acceptable
results ?
  • Scenario
  • 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?

33
Shifts and Trends
  • Shift
  • QC data results are distributed on one side of
    the mean for 6-7 consecutive days
  • Trend
  • Consistent increase of decrease of QC data points
    over a period of 6-7 days

34
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

35
Westgard System
36
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, contamination, volume New reagents
Tech error Identify error and repeat test
Control specimen is old or prepared improperly Use new control
37
QC terms
  • 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.

38
System Flags
  • Delta check
  • Comparison of individual patient results
    throughout the day or week with computer
    detection of changes from earlier individual
    patient results
  • Helpful to identify pre-analytical errors

Test Change Time Frame, hours Other
Sodium, adult 7 24
Creatinine 50 72
Hemoglobin 3.0 g/dl 48 Transfusion/ Bleeding?
39
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

40
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

41
Test results
  • Critical values and read back of results
  • Values that indicate a life-threatening situation
    for the patient
  • Require notification of the value to nurse or
    physician
  • Nurse or physician must read back the results
    to the technician

Test Results Decreased Significance- low results Results Increased Significance- high results
Glucose, adult lt 50 mg/dL Brain damage gt500 mg/dL Diabetic coma
Sodium lt120 mEQ/L Paralysis, arrhythmias gt160 mEQ/L Dehydration, heart failure
42
References
  • Bishop, M., Fody, E., Schoeff, l. (2010).
    Clinical Chemistry Techniques, principles,
    Correlations. Baltimore Wolters Kluwer
    Lippincott Williams Wilkins.
  • Sunheimer, R., Graves, L. (2010). Clinical
    Laboratory Chemistry. Upper Saddle River Pearson
    .
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