1 / 46

Tabuk University

Tabuk University. Faculty of Applied Medical Sciences Department Of Medical Lab. Technology 3 rd Year – Level 5 – AY 1434-1435. Hematology – 2, MLT 307. Quality Assurance and Automation in Hematology. By/ Dr WalidZAMMITI ; Phd ; M.Sc ; MLT. Objectives.

kedem
Télécharger la présentation

Tabuk University

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Tabuk University Faculty of Applied Medical Sciences Department Of Medical Lab. Technology 3rd Year – Level 5 – AY 1434-1435 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 Quality Assurance 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 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. 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 vs. Precision

  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 materialsand 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. 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

  15. 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

  16. 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

  17. 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.

  18. 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 Systematic vs.Random Errors

  19. 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.

  20. Random Errors

  21. 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.

  22. Systematic Errors

  23. Automation in Haematology

  24. 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.

  25. 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

  26. 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.

  27. 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.

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

  29. 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.

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

  31. 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

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

  33. RBC Histogram As A Quality Control Tool

  34. Platelet Histogram As A Quality Control Tool

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

  36. WBC Histogram As A Quality Control Tool

  37. WBC Histogram As A Quality Control Tool

  38. 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: 36.4 – 46.3 fL male: 35.1 – 43.9 fL It is increased in many types of anemias to indicate the variation in red cell sizes.

  39. 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: 11.7 – 14.4% male: 11.6 – 14.4 %

  40. 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

  41. 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

  42. 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

  43. 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.

  44. Thank you

More Related