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

Types of QC

Internal Daily Establishment of

reference ranges Validation of a new

reagent lot and/or shipment

Following instrument repair

External 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

4

Pre-Analytical & Analytical Causes of Error

5

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

6

7

Introduction to Statistical Analysis

When evaluating laboratory results, how do we determine what 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 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’.

8

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 patient’s (normal and abnormal) Acceptable ranges for control specimens ( “in” and “out” of

control)

Measures of Central Tendency

Mean (x̄? ) - 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.

9

Gaussian/Normal Distribution

• All values are symmetrically distributed around the mean

• Characteristic “bell-shaped” curve

• Assumed for all quality control statistics

10

11

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.

12

Precise and AccuratePrecise and Accurate

Systematic Error

Random Error

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

15

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

16

Random Error

Imprecision of the test system causing a scatter or spread of control values around the mean

17

Causes of Random Error

Air bubbles in reagent Improperly mix̄ed reagents Reagent lines, sampling, or reagent syringes Improperly fitting pipette tips Clogged or imprecise pipetter Fluctuations in power supply

18

Bias

Bias – the amount by which an analysis varies from the correct result.Ex̄ample, If the Ex̄pected Value is 50 units,

and the result of an analysis is 47, the bias is 3 units.

19

20

Statistical Formulas

Standard Deviation (SD) Is a mathematical ex̄pression of the dispersion of a

group of data around a mean.

SDx x

n

2

1

21

SDx x

n

2

1

x

Standard Deviation :

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

Σ = 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

x x2

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

22

23

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

Ex̄pressed as a percentage

CV% =

Standard deviation X 100

mean

24

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 %

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

25

Establishment of a QC System

Control material matrix̄ should resemble actual specimens tested Lyophilized/liquid Assayed

Mean calculated by the manufacturer Must verify in the laboratory

Unassayed Less ex̄pensive Must perform data analysis in house

26

27

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

28

Establishment of a QC system Collecting data

Many modern chemistry analyzers have computer program that maintains the QC log.

i.e Dade Dimension

29

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

30

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

31

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?

Shifts and Trends

ShiftQC data results are distributed on one side of

the mean for 6-7 consecutive days

TrendConsistent increase or decrease of QC data

points over a period of 6-7 days

32

33

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

Remember … No information is better than wrong Remember … No information is better than wrong informationinformation

Westgard System

34

Is 1control> 2 SD?

12S

No

Yes

No NoNo NoYes

No

Violation

randomindicates

error

Violation

randomindicates

error

indicates

errorsystematic

Violationindicates

errorsystematic

Violation

Rejectrun

ReportResults

Is 1control> 3 SD?

13S

Are 2controls> 2 SDon sameside ofmean?

22S

Is SD

controls

betweendifference

any 2

> 4?

R4S

Are 4

controls> +/- 1 SD?

consecutive

42S

Acceptrun

Acceptrun

Are 10

controlsconsecutive

on sameside ofmean?

10X

ReportResults

Rejectrun Reject

run

Rejectrun

Rejectrun

indicates

errorsystematic

Violation

Yes Yes

Yes

Yes

Testremaining

rules

Testremaining

rulesTest

remainingrules

Testremaining

rules

35

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

Corrective methods

Things that can go Wrong Corrective Action

Instrument malfunction Identify malfunction and fix̄

Reagents: preparation, contamination, volume

New reagents

Tech error Identify error and repeat test

Control specimen is old or prepared improperly

Use new control

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 ex̄pand the direct AMR.

36

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

37

Test Change Time Frame, hours Other

Sodium, adult 7% 24

Creatinine 50% 72

Hemoglobin 3.0 g/dl 48 Transfusion/ Bleeding?

38

Establishment of Reference Ranges

Reference ranges – the ‘normals’ The normal or ex̄pected 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

39

Establishment of Reference Ranges

Factors affecting reference ranges:

Age Sex̄ Diet Medications Physical activity Pregnancy Personal habits (smoking, alcohol) Geographic location (altitude) Body weight Laboratory instrumentation (methodologies) Laboratory reagents

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

40

Test Results Decreased

Significance- low results

Results Increased

Significance- high results

Glucose, adult < 50 mg/dL Brain damage >500 mg/dL Diabetic coma

Sodium <120 mEQ/L Paralysis, arrhythmias

>160 mEQ/L Dehydration, heart failure

References

Astles, J. R., Stang, H., & Alspach, T. (2013, September). CLIA requirements for proficiency testing: the basics for laboratory professionals. MLO, 45(9), 8-15.

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 .

41