One of the best means of improving the reliability of results is the establishment of the system of quality control. Quality control can tell the degree of uncertainty which always goes with a result. The objective of a good quality control program is to keep this degree of uncertainty within narrow and usable limits.
Quality control is a system for insuring precision and hence accuracy of quantitation in clinical chemistry. It is a tool that the medical technologist uses to reveal variation in the results of his determinations.
Characteristics of a good quality control:
1. Specificity – ability of a method to detect a particular substance without the interference of some other substances present in the sample.
2. Sensitivity – ability of a method to detect and measure even the smallest amount of the particular substance tested for. It is also the degree by which significant deviation can be detected.
Sensitivity = (+) result x 100
(–) result
3. Practicability – ability of a method to be easily repeated.
4. Reliability – ability of a method to maintain its accuracy and precision for an extended period of time under different variables.
5. Accuracy – the extent to which the measurement agrees with or approximates the true value of the quantity being measured or the closeness of test results to the true value. Determination of accuracy is not always simple since the true value of concentration of a substance in a biologic system may not be known. Some laboratories resort to commercial control sera, others to pure standards and reference laboratories for accurate assayed value.
6. Precision (reproducibility) – degree to which repeated results agree or the closeness of test results to each other. Usually expressed in terms of standard deviation. For occasional checks of reproducibility, the use of randomized duplicate specimens is useful
· Accurate values are always precise values but precise values are not always accurate values.
Importance of quality control:
1. To monitor laboratory test results.
2. To give the physician an exact knowledge on the precision of the laboratory work done.
Purpose of quality control:
1. To check the stability of the equipment.
2. To check the quality of the reagents.
3. To check the technical error committed by laboratory personnel.
4. To make results obtained in various laboratories comparable to each other.
5. To enhance the role of the laboratories in the prevention, diagnosis and treatment of disease.
6. To improve the performance of clinical laboratory.
Types of quality control system:
1. Intralaboratory – quality control performed in a certain laboratory to see that day to day performance is not deviating from an established standard. We are interested in precision and accuracy.
2. Interlaboratory – quality control in several laboratories which infers that a determination performed in several laboratories will yield the same values. This has been subject to many studies and results indicate that the odds are against this happening.
Procedures necessary for introducing quality control to the clinical laboratory:
1. Preparation of serum pools and other control sera.
2. Determination of mean values for each component of the control specimen.
3. Determination of standard deviation for each component of the control specimen.
4. Setting up quality control charts.
5. Choosing control chart limits.
6. Determination of out of control results.
7. Trouble shooting out of control situations.
8. Checking all results by someone other than the person carrying out determinations.
9. Checking the coefficient of variation of all common laboratory procedures periodically.
10. Post–graduate education of technology staff in regard to quality control and advances in clinical chemistry.
Important points to be considered in setting up a quality control system:
1. Qualified staff
2. Equipment
3. Selection of procedures
4. Sample collection
5. Reagents
6. Calculations
7. Correlation of results
8. Distribution of values obtained
9. Self–prepared standard
10. Participation in surveys
11. Use of pooled serum pools
12. Use of commercial control sera
Benefits obtained from a quality control program:
1. Provision of a continuous record of reliability of laboratory results.
2. Permits valid judgment on the accuracy of results by monitoring precision and permitting comparisons of assay values on known control sera with stated values.
3. Gives early warning of trends and shifts in control results so that remedial actions may be taken before serious loss of precision.
4. Monitors the performance and stability of equipment used in the assay.
5. Allows a comparison between different techniques for the assay of a substance, and thus, derive a choice between methods.
6. Establishes confidence on the part of the technologist when making a report of the results.
Quality control reagents:
1. Standard – a solution of known characteristics and of known value. It is used as a basis or reference for the calculation of the value of which is established by an analytical procedure different from that used in the clinical laboratory. If the clinical laboratory procedure is able to duplicate the standard value, therefore it is accepted as accurate.
2. Control – a solution (either commercially or non–commercially prepared) which contains various substances of known concentration that are assayed by the usual clinical laboratory methods. They are assayed daily, together with the unknown and are used to measure precision.
a. Sources of control pools:
(1) Commercially prepared – manufactured by different companies which may be in lyophilized form (dried) or non–lyophilized form (liquid).
e.g. Labtrol Enzatrol
Manitrol I & II Versatrol
(2) Non–commercially prepared:
a. Left over sera
b. Animal blood
c. Blood bank plasma (expired blood)
d. Fasting human donor
Characteristics of serum to be pooled:
a. Clear
b. Non–hemolyzed
c. Non–lipemic
d. Non–icteric
e. Undyed
Statistical concepts in quality control:
1. Arithmetic value or mean or average (x) – the mathematical result when the summation of data is divided by the total number of data.
2. Coefficient of variables (CV) – is the percentile of expression of the mean which is a measure of the relative magnitude of variability. It is the ratio of standard deviation over the mean expressed in percent.
CV = SD x 100
X
3. Variance (V) – is a statement of variability and measures the significant differences between groups of data. It is the square of the standard deviation.
V = (SD)2
4. Standard deviation (SD) – is the statement of the extent of random variation in any series of measurements. A measure of the distribution of range values around the mean.
S.D. = Ö S (x – x)2
n – 1
where:
S – the sum of
n – total number of data or frequency
x – mean or average
x – individual data
1 – degree of freedom
Calculation of standard deviation:
a. Get the mean.
b. Subtract individual values from the mean (x – x)
c. Square the differences (x – x)2
d. Find the sum of the squared differences
e. Divide the sum of the squared differences by one less than the number of original items of data (n–1)
f. Extract the square root.
Notes to remember:
1. The smaller the S.D., the more precise the values are.
2. The results of the test should be rounded off to the nearest significant digit.
3. 1 S.D. from the mean = 68.7%
2 S.D. from the mean = 95.5%
3 S.D. from the mean = 99.9%
The Westgard Rules:
a. 1 (2S) – one control value exceeds +/– 2SD from the mean
b. 1 (3S) – one control value exceeds +/– 3SD from the mean
c. 2 (2S) – two consecutive control values exceed the same limit, either +2SD or –2SD.
The rule can apply to 2 different control materials in the same run or to 2 successive analyses of same control materials.
d. R (4S) – numerical difference between 2 control values within the same run exceeds 4SD. One material could show a value greater than 2SD while the other may provide a value in excess of +2SD.
e. 4 (1S) – 4 consecutive control values exceeds either +1SD or –1SD. The values must all be in the same direction.
f. 10 (x) – ten consecutive control results all lie on the same side of the mean.
Tonks’s formula – a statistical tool that will premise the allowable error.
Allowable error: 10%
¼ of normal range x 100
mean
normal range = mean +/– 2SD
HISTOGRAMS: QUALITY CONTROL CHARTS
Histograms or quality control chart is a sheet of rectangular coordinate graphing paper where data for sequential analysis are plotted to locate source of error.
Types of histograms:
1. Shewhart–Levey Jennings
Synonyms:
b. S–L/J chart
c. L/J chart
d. Dot chart
+2SD |
|
+1SD |
|
X |---------------------------------------
|
–1SD |
|
–2SD |
|__________________________
Number of days
2. Gaussian curve – will group any series of measurement in the same sample in a cluster around the mean in a bell shape curve.
Synonyms:
a. Gaussian distribution curve
b. Normal distribution curve
c. Normal frequency curve
d. Bell–shaped curve
3. Cumulative sum (CUSUM) – this is done by subtracting the mean from individual values and the cumulative differences are plotted.
4. Youden plot – a two mean chart drawn at right angles to one another with the set of values on one axis and another set of values on the other axis. It compares two different methods, reagents, etc.
Synonyms:
Turn plot
Two–mean chart
Two–way average chart
+2SD | |
| |
+1SD | |
| |
X |----------------------------------------------------------
| |
– 1SD | |
| |
– 2SD | |
|_________________ |_________________
–2SD –1SD x +1SD +2SD
Interpretation of results:
1. In control – when values obtained are within the +/– 2SD.
2. Out of control – when values obtained go out of the +/–2SD.
Types of out of control:
a. Trend – when six or more consecutive value go up or down the mean.
Causes of trend:
(1) Deterioration of one or more reagents
(2) Changes in the concentration of the standard
(3) Incomplete protein precipitation
b. Shift – when six or more consecutive value stay on either side of the mean.
Causes of shift:
(1) Deteriorating standard but maintaining a constant level
(2) A new standard prepared at a lower concentration
(3) Reagent has shifted to a new level of sensitivity
3. Outliers – are values which are far from the main set of value due to wild errors.
Conditions when dealing with outliers:
1 outlier in 20 days – in control
2 outlier or more outlier in 20 days – out of control
Variations – errors or mistakes in quality control. It is the fundamental basis of any statistical analysis.
Types of error encountered during analytical process:
a. Random error – due to an unpredictable cause.
Common situations:
(1) Mislabeling a sample
(2) Pipetting errors
(3) Improper mixing of sample and reagent
(4) Voltage fluctuation not compensated for by instrument circuitry
(5) Temperature fluctuation
Violated Westgard rules:
(1) 1 (2S)
(2) 1 (3S)
(3) R (4S)
Correction:
(1) Reassay using same reagent
b. Systematic error – due to definite cause. Control values usually rise or fall from previously established limits.
Aging phenomenon – variation due to reagent
Personal bias – variation due to operator
Common situation:
(1) Improper calibration
(2) Deterioration of reagents
(3) Sample instability
(4) Instrument drift
(5) Changes in standard material
Violated Westgard rules:
(1) 2 (2S)
(2) 4 (1S)
Correction:
(1) Preparing new control materials
(2) Restandardizing the assay
(3) Checking the wavelength or other instrument setting
(4) Making new reagents
c. Clerical errors
(1) Mistake in punching
(2) Calculation
(1) Mistake in punching
(2) Calculation
Proficiency surveys – provide a means of evaluating the reliability of laboratory data.
95% limits – reference range usually set at the mean +/– 2SD.
Precautions in establishing reference values:
1. Reference population studied was small (100 people or fewer)
2. Blood sampling may not have been done under controlled conditions.
3. Considerations may not have been given to all medications used by the subject.
4. Physical exercise and smoking.
Factors affecting reference values:
1. Age
2. Sex
3. Diet
4. Medication
5. Physical activity
6. Pregnancy
7. Personal Habits
8. Body weight
9. Biologic Rhythm
10. Assay Methodology
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