1. Tabuk University Hematology – 2, MLT 307
Faculty of Applied Medical Sciences
Department Of Medical Lab. Technology
3rd Year – Level 5 – AY
Hematology – 2, MLT 307

2. Quality Assurance and Automation in Hematology
By/
Dr WalidZAMMITI; Phd; M.Sc; MLT

3. Objectives
Describe the electrical impedance and light scatter principles for performing cell counts.
Utilize quality control procedures to determine if patient results are acceptable.
Explain histograms and their indications.
Concentrate on some parameters and indices.
Identify the major components of a quality assurance program.
Be able to distinguish between quality assurance & quality control.
Define and give examples of each of the following terms: Accuracy-Calibration-Control-Standard-Precision.
Understand the concepts of internal & external control.

4. Quality system begins and ends with the patient

5. Quality Assurance vs. Quality Control
An overall
management plan to
guarantee the
integrity of data
(The “system”)
A series of analytical
measurements used
to assess the quality of the
analytical data (The “tools”)

6. Quality Assurance in Hematology
QA includes all aspects of laboratory activities that affects the results produced, from the choice of methods, to the education of personnel, to the handling of specimens and reporting results.
The real purpose of QA activities is to determine how correct or incorrect the results emanating from the lab are, and to allow those managing the lab to determine whether or not the lab is fulfilling its functions satisfactorily.

7. QA in Haematology Laboratory
QA in haematology lab is intended to ensure the reliability of the lab tests.
The objective is to achieve precision and accuracy
4 components of QA programme :
1 ) Internal Quality Control ( IQC )
2 ) External Quality Control ( EQC )
3 ) Standardization
4 ) Proficiency surveillance

8. Accuracy vs. Precision Accuracy Precision
How well a series of measurements agree with each other: Is the closeness of agreement between independent test results obtained under stipulated conditions.
Accuracy
How well a easurement agrees with an accepted value: is the closeness of the agreement between the result of a measurement and a true value of the measurand.

9. Accuracy vs. Precision

10. Internal Quality Control
Internal Quality Control Internal quality control is set up within a laboratory to monitor and ensure the reliability of test results from that laboratory.
The primary tool for internal quality control is called a control. A control is a specimen with a predetermined range of result values, called control values, that is processed in the same manner as a patient sample.
Control samples are processed with each series or run of patient samples.
If the result of a test on a control sample is different from its known value, this indicates a problem in the equipment or the methods being used.

11. External Quality Control ( EQC )
is the objective evaluation by an outside agency of the performance by a number of laboratories on material which is supplied specially for the purpose
is usually organized on a national or regional basis
analysis of performance is retrospective
the objective is to achieve comparability with results of other labs.

12. Standardization Refers to both materials and methods.
A material standard or reference preparation is used to calibrate analytic instruments and to assign a quantitative value to calibrators.
A reference method is an exactly defined technique which provides sufficiently accurate and precise data for it to be used to assess the validity of other methods

13. Proficiency surveillance
Implies critical supervision of all aspects of laboratory tests: collection, labelling, delivery, storage of specimens before the tests are preformed and of reading and reporting of results.
Also includes maintenance and control of equipment and apparatus.

14. Control
 What is a Control?
QC programs require the same sample to be tested every day testing is done.
This type of sample is called a control.
Controls, which are often purchased from manufacturers, use a human base to ensure the analyses being tested parallel human ranges.
Manufacturers pool together many human blood samples to create the large volume needed for a lot number of control

15. Tools for Validation of QC results
Control Charts: A Control Chart depend on the use of IQC specimens and is developed in the following manner
+3 sd
+2 sd
+1 sd
Target value
-1 sd
-2 sd
-3 sd
Assay Run

16. Control Charts
Samples of the control specimen are included in every batch of patients’ specimens and the results checked on a control chart
Check precision: it is not necessary to know the exact value of the control specimen
Value has been determined reliably by a reference method, the same material can be used to check accuracy or to calibrate an instrument
Controls with high, low and normal values should be used
Advisable to use at least one control sample per batch even if the batch is very small
The results obtained with the control samples can be plotted on a chart

17. How to calculate SD
1. Get the Mean. 2. Get the deviations. (each value minus the mean) 3. Square these. 4. Add the squares. 5. Divide by total numbers less one. 6. Square root of result is Standard Deviation

18. Types Of Errors
An error which varies in an unpredictable manner, in magnitude and sign, when a large number of measurements of the same quantity are made under effectively identical conditions.

19. Systematic vs.Random Errors
Systematic Error
Avoidable error due to controllable variables in a measurement.
Random Errors
Unavoidable errors that are always present in any measurement. Impossible to eliminate

20. Random Error
Random errors create a characteristic spread of results for any test method and cannot be accounted for by applying corrections. Random errors are difficult to eliminate but repetition reduces the influences of random errors.
Examples of random errors include errors in pipetting and changes in incubation period. Random errors can be minimized by training, supervision and adherence to standard operating procedures.

21. Random Errors

22. Systematic Error
An error which, in the course of a number of measurements of the same value of a given quantity, remains constant when measurements are made under the same conditions, or varies according to a definite law when conditions change.
Systematic errors create a characteristic bias in the test results and can be accounted for by applying a correction.
Systematic errors may be induced by factors such as variations in incubation temperature, blockage of plate washer, change in the reagent batch or modifications in testing method.

23. Systematic Errors

24. Automation in Haematology

25. Automated techniques of blood counting
Semi-automated instruments
Require some steps, as dilution of blood samples
Often measure only a small number of variables
Fully automated instruments
Require only that an appropriate blood sample is presented to the instrument.
They can measure 8-20 variables including some new parameters which do not have any equivalent in manual methods.

26. The accuracy of automated counters is less impressive than their precision.
In general automated differential counters are favourable to the manual in 2 conditions
Exam of normal blood samples
Flagging of abnormal samples

27. CBC : Complete Blood Count
The complete blood count is performed as an automated procedure. A sample of blood is placed in an analyzer and the cells are sorted by a laser according to size, granularity, and shape.

29. Parameters :
WBC= Total white blood cells
RBC= Red blood cell count
HGB= Hemoglobin concentration
HCT= Hematocrit (PCV)
MCV= Mean Cell Volume
MCH= Mean Cell Hemoglobin
MCHC= Mean Cell Hemoglobin Concentration
PLT= Platelets count
NEUT%= Percentage Neutrophil count
LYMPH%= Percentage Lymphocyte count
MONO%= Percentage Monocyte count
EO%= Percentage Eosinophil count
BASO%= Percentage Basophil count
NEUT#= Absolute Neutrophil Count
LYMPH#= Absolute Lymphocyte Count
MONO#= Absolute Monocyte Count
EO#= Absolute Eosinophil Count
BASO#= Absolute Basophil Count
RDW-SD= Red cell Distribution Width – Standard Deviation
RDW-CV= Red cell Distribution Width – Coefficient Variation
MPV= Mean Platelet Volume
PDW = Platelet Distribution Width
Some times other parameters are included; e.g.: Reticulocytes.

30. Examples of Haematology analysers
AcT 5diff (Beckman Coulter )
SE 9000, KX21, XE 2100 (Sysmex)
Advia 60 (Bayer)
Cell-Dyn 3500 ( Abott)

31. When to Calibrate You should calibrate your instrument:
At installation.
After the replacement of any component that involves dilution characteristics or the primary measurements (such as the apertures).
When advised to do so by your service representative.

32. Flagging Condition flags Describes cell population normal abnormal
WBC Suspect flags
Blasts
Immature Grans/Bands 1
Immature Grans/Bands 2
Variant lymphocytes
Review Slide

33. More Flagging RBC Suspect flags NRBCs Macrocytic RBCs
Dimorphic RBC population
Micro RBCs/RBC fragments
RBC agglutination
Definitive Flagging
Based on predetermined lab limits
Provide information for review

34. Histograms RBC, PLT, and WBC plotted on histogram X-Axis
Cell size in femtoliters (fL)
Y-Axis
# of cells

35. RBC Histogram As A Quality Control Tool
INDICATOR
PROBABLE CAUSE
COMMENT
Left of curve does not touch baseline
Schistocytes and extremely small red cells
Review smear CBC and Platelet histogram
Bimodal peak
Transfused cells, therapeutic response
Review Smear
Right portion of curve extended
Red cell autoagglutination
Review CBC & Smear
Left shift of curve
Microcytes
Review smear & CBC
Right shift of curve
Macrocytes

36. Platelet Histogram As A Quality Control Tool
INDICATOR
PROBABLE CAUSE
COMMENT
Peak or spike at left end of histogram (2-8 fl)
Cytoplasmic fragments
Review smear
Spike towards right end of histogram
Schistocytes, microcytes, giant platelets
Review smear + CBC
( MCV &  RDW)
( MPV &  PDW)
Bimodal peak

37. Histograms - WBCs
WBC: Distribution with three individual peaks and valleys at specific regions representing the lymphocytes, monocytes, and granulocytes.

38. WBC Histogram As A Quality Control Tool
INDICATOR
PROBABLE CAUSE
COMMENT
Trail extending downward at extreme left, or lymph peak not starting at baseline
NRBC, Plt clumping, unlysed RBC, cryoproteins, parasites
Review smear and correct WBC for NRBC
Peak to the left of lymph peak or widening of lymph peak towards left
NRBC
Review smear & correct WBC for NRBC
Widening of lymph peak to right
Atypical lymphs, blasts, plasma cells, hairy cells, eosinophilia, basophilia
Review smear
Wider mono peak
Monocytosis, plasma cells, eosinophilia, basophilia, blasts

39. WBC Histogram As A Quality Control Tool
INDICATOR
PROBABLE CAUSE
COMMENT
Elevation of left portion of granulocyte
Left Shift
Review smear
Elevation of right portion of granulocyte peak
Neutrophilia

40. RDW-SD
RDW is an actual measurement of the width of the erythrocyte distribution curve.
It is a measurement of Anisocytosis.
May increase before MCV becomes abnormal
Reference values:
female: – 46.3 fL
male: – 43.9 fL
It is increased in many types of anemias to indicate the variation in red cell sizes.

41. RDW-CV
The coefficient of variation (CV) is defined as the % ratio of the standard deviation (x), to the mean (µ)
Cv = x/µ
Sometimes known as relative standard deviation.
Reference values:
female: – 14.4%
male: – 14.4 %

42. MCV = MEAN Cell VOLUME M.C.V. = Hematocrit% X 10 RBC in millions/µl
Normal values: Men & women
82 – 97 fl (femtoliters) = cubic microns
Increased : Macrocytes
Decreased : Microcytes

43. MCH = Mean Cell Hemoglobin
M.C.V. = Hemoglobin g/dl X 10
RBC in millions/µl
Normal values: Men & women
27 – 32 pg (pico grams)
Increased : Hyperchromic
Decreased : Hypochromic

44. MCHC = Mean Cell Hb Concentration
M.C.V. = Hemoglobin g/dl X 100
Hematocrit%
Normal values: Men & women
30 – 34 g/dl
Increased : Hyperchromic
Decreased : Hypochromic

45. Other Hematology Machines
Coagulometers :
- Used in Hemostasis studies, and the Endpoint Detection depends on Mechanical, Optical (Photo- optical , Nephelometric , Chromogenic or Immunologic), Electrochemical principles.
ESR machines : in 30 minutes.
Leucocytes automated Differential Counters :
Using cytochemical or image recognition methods.

46. Thank you