eflm e-learning platform - eflm presentation · 2019. 2. 11. · outline of this presentation new...

11-2-2019

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HAEM@CMCVellore 26-27 February 2016 Vellore, India

NEW HEMATOLOGY ANALYZER PARAMETERS AND THEIR CLINICAL SIGNIFICANCE

Pre-analytical and analytical aspects, reference intervals and clinical utility

Hans J. Hoffmann PhD, EuSpLM

H3L Consult, the Netherlands

[email protected]


Outline of this presentation

Technological principles of hematology analyzers: how cellular parameters are generated

New RBC, PLT and WBC parameters: pre-analytical and analytical aspects, reference ranges and clinical utility

Limitations and unmet clinical needs

Conclusions

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Technological principles of hematology analyzers: how cellular parameters are

generated


Technological principles applied in hematology analyzers - 1

• All hematology analyzers utilize flow cytometry, for performing measurements on a single cell level

• Flow cytometry requires a small sample volume and enables high throughput

• Some analyzers do a WBC analysis from a single dilution whereas others divide the diluted blood sample over two or more parallel flow channels, each doing their own measurements

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Technological principles applied in hematology analyzers - 2

• Electric impedance (Coulter principle): single-dimensional volume measurement

• Chemical treatment: cytochemistry or differential lysis. Specific reagents emphasize or eliminate cellular characteristics for distinguishing between different cell types

• Radiofrequency (conductivity): measures intracellular properties like N/C ratio, nuclear density and granularity

• (Laser) light scatter: depends on cellular properties like N/C ratio, granularity, nuclear density, surface etc.

• Fluorescence: uses dedicated fluorescent dyes for staining nuclei and cellular contents like RNA


How cellular parameters are generated - 1

• In each channel, one or multiple signals are measured of every cell passing the detector(s)

• In multiple-channel analyzers, signals from all channels can be combined for deriving more information. However, detailed information on the single-cell level cannot be achieved

• With multiple measurements in a single channel, all signals belonging to a single blood cell can be combined. This greatly enhances the degree of information on the single-cell level

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• Like in any flow cytometer, signals are displayed in several two-dimensional or even multi-dimensional plots

• Sophisticated software analyses the dots and creates clusters of cells with similar characteristics

• The form, size and spatial location of the clusters are then used for classifying and enumerating the cells

• Intracellular hemoglobin concentration is derived from multi-dimensional light scatter, based on Mie principle



• The cell types in normal blood are directly associated with a cell cluster in the scatterplots

• For other cell types the association is indirect. For example, immature granulocytes are usually derived by correlating cell cluster characteristics with a reference method

• Cell Population Data, recently offered as research parameters, are actually means and SD of the normal cell clusters

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Cell cluster analysis

Normal

Left shift



New RBC parameters: (pre-) analytical aspects, reference ranges and clinical utility

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New RBC parameters - 1

• RDW (red cell distribution width): calculated from red cell volume histogram, usually expressed in %

• Micro (microcytic red cell %): calculated from red cell volume histogram

• Macro (macrocytic red cell %): calculated from red cell volume histogram

Usual definitions: micro < 60 fL and macro > 120 fL

Note: Methodological differences do exist, but are generally minor


New RBC parameters - 2

• Hypo (hypochromic red cell %): calculated from red cell Hb concentration histogram

• Hyper (hyperchromic red cell %): calculated from red cell Hb concentration histogram

• HDW (hemoglobin concentration distribution width): calculated from red cell Hb concentration histogram

Usual definitions: hypo < 28 g/dL, hyper > 41 g/dL Sysmex definition: hypo < 17 pg, hyper > 49 pg

Note: Methodological differences do exist, although not of major clinical significance

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Reticulocyte parameters

• MCVr (reticulocyte MCV): derived from multi-angle light scatter using Mie scatter theory and fluorescence for identifying reticulocytes

• MCHCr (reticulocyte Hb concentration): derived from multi-angle light scatter using Mie scatter theory and fluorescence for identifying reticulocytes

• MCHr or CHr® (reticulocyte Hb content): calculated from MCHCr

• Ret-He (reticulocyte Hb content): derived from single- or multi-angle light scatter, depending on analyzer type

Note: MCHr/CHr ®/Ret-He methods differ between manufacturers.


MCV and RDW: pre-analytical aspects

MCV increases during sample storage. Stability is up to 6 h

Also RDW increases and is stable up to 12 h

Hoffmann et al. Clin Chem Lab Med 2012; 50: 941-8

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RDW: analytical aspects

RDW is not standardized: manufacturers use different definitions

Lippi et al. Clin Biochem 2014; 47: 1100-3


RDW: reporting units

RDW-CV: relative to MCV in %

• Practically all analyzers report RDW-CV

• Division by MCV makes RDW MCV-dependent

RDW-SD: “absolute” RDW in fL

• Few analyzers report RDW-SD

• Independent of MCV

• Theoretically better reflection of RBC heterogeneity


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RDW: reference intervals

Van den Bossche et al. Clin Chem Lab Med 2002; 40: 69-73Hoffmann. Clin Chim Acta 2012; 413: 824-5


RDW: reference intervals (Sysmex XE-2100)

RDW increases with age and is higher in females

Lippi et al. Clin Chem Lab Med 2014; 52: e197-9

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RDW: reference intervals (Abbott Sapphire)

RDW increases with age; no gender effect detectable

11.0

11.5

12.0

12.5

13.0

13.5

age class (y)

RD

W (

%)

<18 18-40 41-55 56-70 71-85 85+



NHANES III study (Coulter S+ Jr)

MCV increases with age, irrespective of race

Cheng et al. Lab Hematol 2004; 10: 42-53

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RDW: reference intervals (Abbott Sapphire)


9.0

10.0

11.0

12.0

13.0

age class (y)

RD

W-S

D (

fL)

<18 18-40 41-55 56-70 71-85 85+

11.0

11.5

12.0

12.5

13.0

13.5

age class (y)

RD

W (

%)

<18 18-40 41-55 56-70 71-85 85+

80

85

90

95

100

age class (y)

MC

V (

fL)

<18 18-40 41-55 56-70 71-85 85+

“Absolute” RDW also increases with age, independent of MCV


RDW: clinical utility

Traditionally, RDW is used as a diagnostic tool in anemia (as an objective measure of anisocytosis):

• Increased RDW correlates with reticulocytosis

• Patients with pernicious anemia may have increased RDW

• Screening of thalassemia carriers

• RDW differentiates between sickle cell anemia and other types of anemia

• Part of algorithms for differentiating between iron deficiency anemia and thalassemia

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0

50

100

150

200

250

300

350

400

1975 1980 1985 1990 1995 2000 2005 2010 2015

publications on RDW per year



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RDW has been proven to be a prognostic marker of:

• Mortality risk in acute coronary syndromes

• Survival in atherothrombotic diseases

• Long-term risk in heart failure

• Mortality risk in chronic kidney disease

• Life expectancy in cancer patientsAbrahan et al. Cardiol Res 2018; 9: 144-52

Ahmad et al. Int J Rheumatol 2018; 2018: 2476239Lippi et al. World J Cardiol 2018; 10: 6-14Lippi et al. Acta Biomed. 2017; 87: 323-8

Zhang et al. Arch Med Res 2017; 48: 378-85Montagnana & Danese. Ann Transl Med 2016; 4: 399



RDW has been suggested to be a prognostic marker in:

• Pulmonary arterial hypertension

• Peripheral artery disease

• Recurrence of atrial fibrillation

• Progression of atherosclerosis

• Incidence of venous thromboembolic events in middle-aged persons

• Inflammatory bowel disease

• Esophageal carcinoma

• Mortality of community-acquired pneumonia

• Disease activity in Behçet’s disease

• Uncontrolled glycaemia in type 2 diabetes

• Mortality in the general population

• Mortality following hip fracture

• Survival after venous thromboembolic events

• Attacks in migraine patients

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RDW: mechanism of disease association

RDW reflects RBC dynamics, including erythropoietic activity, lifespan and clearance in response to anemia and/or inflammatory processes

Higgins. Clin Lab Med 2015; 35: 43-57Patel et al. Amer J Hematol 2015; 90: 422-8


Micro and hypo RBC: pre-analytical aspects

Due to RBC swelling, microcytic RBC slightly decrease and hypochromic RBC substantially increase during sample storage. Stability < 6 h

Ermens et al. Int J Lab Hematol 2012; 34: 274-82

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Micro and hypo RBC: reference intervals (Sapphire)



Micro and hypo RBC: clinical utility

Differentiating iron deficiency anemia (IDA) from other forms of anemia such as anemia of chronic disease (ACD)

Rehu et al. Clin Chim Acta 2011; 412: 1809-13

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Micro and hypo RBC: clinical utility

• M/H ratio distinguishes thalassemia trait from iron deficiency anemia (IDA) in patients with microcytic anemia

• Population screening for thalassemia trait in endemic areas

Urrechaga et al. Int J Lab Hematol 2015; 37: 334-40Hoffmann et al. Clin Chem Lab Med 2015; 53: 1883-94

Urrechaga & Hoffmann. Clin Chem Lab Med 2017; 55: 1582-91


Hyperchromic RBC: pre-analytical aspects

Due to RBC swelling, hyperchromic RBC decrease during sample storage. Stability up to 6 h maximum

Rooney et al. Int J Lab Hematol 2015; 37: 98-104

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Hyper RBC: reference intervals (Abbott Sapphire)



Hyperchromic RBC: clinical utility

Screening of (hereditary) spherocytosis

Rooney et al. Int J Lab Hematol 2015; 37: 98-104

0.972 (0.957-0.983) 4.9 96.4 99.1 95.5

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Reticulocyte parameters: pre-analytical aspects

MCVr and MCHCr have limited stability due to EDTA-induced RBC swelling

MCHr is essentially stable over time

Ermens et al. Int J Lab Hematol 2012; 34: 274-82


MCHr and CHr®: clinical utility

• Diagnosing iron restriction in anemic and non-anemic subjects

• Diagnosing functional iron deficiency (inability to release iron fromadequately filled stores)

• Monitoring early response to intravenous iron therapy

• Monitoring erythropoietin therapy in patients with anemia due to renalfailure

Thomas & Thomas. Clin Chem 2002; 48: 1066-76Brugnara & Mohandas. Curr Opin Hematol 2013; 20: 222-30

Thomas & Thomas. Eur J Haematol 2017; 99: 262-8

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MCVr: clinical utility

Diagnosing clinical and sub-clinical cobalamin deficiency

Ceylan et al. Int J Lab Hematol 2007; 29: 327-34.Khan & Agha. Int J Lab Hematol 2019; 41: e1-e4



New PLT parameters: (pre-) analytical aspects, reference ranges and clinical utility

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New PLT parameters - 1

• MPV (impedance): platelet volume is directly measured and MPV calculated

• MPV (optical): volume of individual platelets is derived from multi-angle light scatter and MPV calculated

• PCT (plateletcrit): calculated from MPV and PLT count

• PDW (platelet distribution width): calculated from platelet volume

histogram

•Note: MPV and PDW methods differ strongly between manufacturers


New PLT parameters - 2

• IPF (immature platelet fraction): derived from RNA fluorescence signals of platelets in reticulocyte analysis of Sysmex analyzers

• retPLT (reticulated platelets): measured from RNA fluorescence signals of platelets in reticulocyte analysis of Abbott analyzers

Note: IPF and retPLT methods differ between manufacturers and IPF differs between Sysmex XE-and XN-series analyzers

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MPV: pre-analytical aspects

MPV increases rapidly during sample storage, due to EDTA-induced platelet swelling

Beckman Coulter LH750

Lancé et al. Lab Hematol 2010; 16: 28-31

Technicon H*1

Wynn et al. Clin Lab Hematol 1995; 17: 173-6


MPV: analytical aspects

Lippi et al. Blood Coag Fibrinol 2015; 26: 235-7

MPV is not standardized and thus depends on the hematology analyzer

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MPV: reference intervals

Hoffmann. Thromb Res 2012; 129: 534-5

MPV standardization is still an unresolved issue


Does MPV change with age?

Verdoia et al. Exp Gerontol 2015; 62: 32-6

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Does MPV change with age?

Hoffmann et al. Exp Gerontol 2015; 62: 41-2

No indications for age- or gender-dependent MPV reference intervals


MPV: reference intervals

Hoffmann et al. Arch Pathol Lab Med 2013; 137: 1635-40

MPV correlates inversely with PLT count

Should MPV reference intervals be stratified for PLT count?

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MPV: clinical utility

MPV is traditionally used as a diagnostic tool in patients with thrombocytopenia:

• MPV may identify patients with congenital platelet disorders that are associated with platelet volume

Wiskott-Aldrich syndrome (micro platelets)Bernard-Soulier syndrome (giant platelets)

• MPV can discriminate between bone marrow aplasia and peripheral destruction

• MPV helps to predict bone marrow recovery after chemotherapy and transplantation



0

50

100

150

200

250

300

350

400

450

500

1975 1980 1985 1990 1995 2000 2005 2010 2015

publications on MPV per year

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MPV has been suggested to be a biomarker for:

• Outcome following acute myocardial infarction

• Restenosis after coronary angioplasty

• Development and outcome of pre-eclampsia

• Thrombotic risk in peripheral artery disease

• Development of deep vein thrombosis and pulmonary embolism

• Pulmonary arterial hypertension

• Risk of stroke in atrial fibrillation

• Risk of atherosclerotic renal artery stenosis

• Prognosis of sepsis

• Prognosis in advanced pancreatic cancer

• Predicting survival in metastatic colorectal cancer

• Mortality prediction in infants of preeclamptic mothers

• Bleeding severity and prognosis in gastrointestinal bleeding

• Mortality indicator in patients on peritoneal dialysis

• Diagnosis of pemphigus vulgaris



But, there is very serious concern about the validity of most, if not all, MPV risk studies published:

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Flow cytometry (retPLT)

Considered the preferred research method, but all published methods are cumbersome and lack standardization. Key issues are fluorescent staining details and gating strategy (Matic correction)

Immature platelet fraction (IPF)

Automated methods available on Sysmex XE- and XN-series analyzers

Reticulated platelets (retPLT)

Automated method available on Abbott CELL-DYN Sapphire and Abbott Alinity hq analyzers

Note: IPF and rPLT methods differ between manufacturers and IPF differs between Sysmex XE- and XN-series analyzers

Reticulated or immature platelets: analytical aspects



Principles of determinations in Sysmex XE (A), Sysmex XN (B) and Abbott CELL-DYN Sapphire (C)

Hoffmann. Clin Chem Lab Med 2014; 52: 1107-17

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The two hematology analyzer methods show poor analytical correlation

Meintker et al. Clin Chem Lab Med 2013; 51: 2125-32


IPF: pre-analytical aspects (Sysmex)

Sinclair. Aust J Med Sci 2012; 33: 10-7

The stability of IPF is controversial

Osei-Bimpong. Hematology 2009;14:118-21

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retPLT: pre-analytical aspects (Sapphire)

retPLT are stable for at least 6 hours at ambient temperature



IPF: reference intervals (Sysmex)

Authors (year) Mean Range n Analyzer

Briggs et al (2004) 3.4 1.1 – 6.1 50 XE-2100

Abe et al (2006) 3.3 1.0 – 10.3 129 XE-2100

Cho et al (2007) 1.7 0.4 – 5.4 142 XE-2100

Kim et al (2007) 2.2 1.7 – 5.2 30 XE-2100

Cremer et al (2009) 4.3 0.7 – 7.9 456 XE-2100

Di Mario et al (2009) 2.4 0.8 – 5.1 53 XE-2100

Meintker et al (2013) 2.5 0.8 – 7.9 110 XE-5000

Joergensen et al (2016) 3.7 1.3 – 9.0 1674 XE-5000

Ko et al. (2015) 2.6 0.5 – 9.7 2104 XN-1000

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retPLT: reference intervals (Sapphire)

Authors (year) Mean Range n Analyzer

De Wit et al (2009) 2.2 0.4 – 6.4 53 Sapphire

Gordillo et al (2011) 1.7 0.1 – 11.3 196 Sapphire

Costa et al (2012) 1.4 0.4 – 2.8 151 Sapphire

Getaldic et al (2013) - 0.5 – 4.6 174 Sapphire

Hoffmann et al (2013) 1.6 0.4 – 6.0 8089 Sapphire

Lang et al (2013) 2.5 0.6 – 5.9 90 Sapphire

Meintker et al (2013) 2.2 1.0 – 3.8 110 Sapphire


retPLT: reference intervals (Sapphire)

In adults, retPLT increase with age

retPLT are inversely related to MPV


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Reticulated or immature platelets: clinical utility

Diagnostic utility has been confirmed for:

• Distinguishing decreased production from accelerated destruction as the cause of thrombocytopenia

• Predicting thrombopoietic recovery after chemotherapy, bone marrow and peripheral stem cell transplantation

Meintker et al. Clin Chem Lab Med 2013; 51: 2125-32Strauss et al. Pediatr Blood Cancer. 2011;57:641-7

Sakuragi et al. Int J Hematol 2018; 107: 320-6



New WBC parameters: (pre-) analytical aspects, reference ranges and clinical utility

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WBC parameters: analytical aspects

• CPD (Cell Population Data) represent the raw measurement data from a WBC cluster

• CPD cannot be calibrated

• CPD depend on the “optical signature” of an individual hematology analyzer

Seghezzi et al. J Med Biochem 2018; 37: 1-13


WBC parameters: clinical utility

CPD have been suggested to be a biomarker for:

• Early diagnosis of sepsis

• Exclusion of acute promyelocytic leukemia

• Screening for viral infection

• Timing and efficiency of stem cell transplantation

• Screening of myelodysplastic syndrome

• Screening of paroxysmal nocturnal hemoglobinuria

• Distinction of reactive versus clonal lymphocytosis

• Postprandial leukocyte activation

• Screening of malaria infectionPark et al. Int J Lab Hematol 2015; 37: 190-8

Urrechaga et al. J Clin Pathol 2018; 71: 259-66Buoro et al. Ann Transl Med 2016.10.73

Furundarena et al. Int J Lab Hematol 2018; 40: 41-8

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Limitations and unmet clinical needs


Limitations of RBC parameters

• Pre-analytical changes due to EDTA (nearly all parameters)

• Incomparability of RDW between different methods

• Lack of international standardization (RDW and newer RBC parameters)

• Different reference intervals (RDW and newer RBC parameters)

• Gender- and age-dependency not adequately explored in risk assessment studies (RDW)

• Expression of RDW (% or fL)

Goossens et al. Clin Lab Haematol 1991; 13: 291-5Mehmood et al. J Clin Lab Anal 2018; 32: e22188

Hoffmann. Clin Chim Acta 2012; 413: 824-5Hoffmann et al. Clin Chem Lab Med 2015; 53: 2015-9

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Limitations of reticulocyte parameters

• Pre-analytical changes due to EDTA (nearly all parameters)

• Incomparability between different methods

• Lack of harmonization and standardization (MCHr, most importantly)

• Different reference intervals (all parameters)


Limitations of PLT parameters

• Pre-analytical changes due to EDTA, storage time and temperature (MPV, PDW)

• Incomparability due to different methods (MPV, PDW, retPLT, IPF)

• Lack of harmonization and standardization (MPV, PDW, retPLT, IPF)

• MPV is not normally distributed, so what is ‘mean’?

• Gender- and age-dependency (all parameters)

• PLT count dependency not properly investigated in risk assessment studies (MPV)

Hoffmann et al. Clin Chem Lab Med 2015; 53: 2015-9Beyan & Beyan. Blood Coag Fibrinol 2017; 28: 234-6

Dima et al. Diagnosis 2016; 3: 91-3Mehmood et al. J Clin Lab Anal 2018; 32: e22188

Buttarello et al. Clin Chem Lab Med 2018; 56: 830-7

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Limitations of WBC parameters

• Pre-analytical aspects not yet thoroughly investigated

• Incomparability between different analyzer types

• Variation between individual analyzers of the same type and harmonization require further studies

• International standardization impossible

Seghezzi et al. J Med Biochem 2018; 37: 1-13


Blood cell parameters – unmet clinical needs

• Clinically validated parameter for immature granulocytes

• MPV standardization, analytical and pre-analytical

• MCHr/CHr analytical standardization

• retPLT/IPF standardization, analytical and pre-analytical

• RDW expressed in fL

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Conclusions


Conclusions

These new hematology analyzer parameters

• Are all sensitive for pre-analytical influences, requiring strict procedures of sample handling and processing

• Suffer from lack of analytical standardization

• Some have been proven to be diagnostically useful in certain hematological patients

• Their role as prognostic risk indicators looks promising, but requires studies with appropriate pre-analytical and analytical standardization

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Thank you for your attention

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