scholarly journals Performance evaluation of leukocyte differential on the hematology analyzer Celltac G compared with two hematology analyzers, reference flow cytometry method, and two manual methods

2021 ◽  
Author(s):  
Kenji Yamade ◽  
Toshihiro Yamaguchi ◽  
Yutaka Nagai ◽  
Toshinori Kamisako
Keyword(s):  
Flow Cytometry ◽  
Performance Evaluation ◽  
Reference Flow ◽  
Hematology Analyzer ◽  
Manual Methods ◽  
Hematology Analyzers

WBC Differential by Flow Cytometry: The First Routine Application in a Large University Hospital Clinical Laboratory.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 3845-3845
Author(s):  
Mikael Roussel ◽  
Cyrille Benard ◽  
Béatrice Ly Sunnaram ◽  
Danielle Gerard ◽  
Jean Feuillard ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
White Blood Cells ◽  
Flow Cytometer ◽  
University Hospital ◽  
Inflammatory Monocytes ◽  
Cell Counts ◽  
Hematology Analyzer ◽  
Normal Procedure ◽  
Data Manager ◽  
Hematology Analyzers

Abstract Hematology analyzers deliver high precision blood cell counts and a good leukocyte differential (WBCD) on normal samples. But their ability to identify and quantify abnormal cells is less good and generates a significant amount of false positive results. Routinely, about 10% to 30% of results must have manual blood film reviews, which requires considerable time and are prone to a high degree of inaccuracy, especially for the less frequent cell types (Rümke et al. 1975). In contrast, flow cytometry offers superior detection and quantification of these rare events. A Cyto Diff tube combining six antibodies (CD45, 16, 2, 36, 19 & CRTH2) analysed on a modern multicolor flow cytometer make very accurate automated WBCD feasible for abnormal samples (Feuillard J et al. ISLH 2007). The objective of the study was to evaluate the efficiency of the Cyto Diff process compared to the normal laboratory process as: The time for both methods, the labor and time savings, the relative costs of both methods including med tech time, consumables, number of residual manual review. Two Coulter LH750® hematology analysers were used for the analysis of CBC, WBCD and Reticulocyte counting. An immuno-phenotyping system, with an automatic preparator Coulter FP 1000 and an Coulter FC 500® flow cytometer were connected with a Hematology analyzer to the REMISOL data manager that requests a reflex CytoDiff tube on every sample flagged by the hematology analyzer according to the laboratory’s validation rules. The remaining samples are displayed for manual validation by an operator. The complete line is called HematoFlow. Among the 4896 non-selected CBC tests evaluated during the 10 working days of our study, 877 cases were flagged by the analyzers, reviewed manually following the normal procedure as well as analyzed on HematoFlow. Interestingly, this latter allowed: 68.8% of auto-validation by the REMISOL Data Manager, 12.8% validation directly by the operator after checking the auto-gating, 8.4% required a region readjustment before validation and finally, only 10.3% (91 of the 877-flagged samples) required further exploration because the presence of large amount of ImmGrans, Plt clumps, NRBCs, etc. In conclusion, the CytoDiff tube performs well in regular clinical lab workflow saving almost 90% of the samples flagged by the hematology analyzers for WBC abnormalities that need further exploration following current routine procedure. Our study confirmed our previous results and the fact that the standard auto-gating is set correctly needing only 8.4% of region readjustment by an operator who can be trained easily in few days. Basically, we are expecting that one operator well-trained for smear review and working on the HematoFlow line can handle the same workload as at least 3 operators at microscope stations following a current normal procedure. Furthermore, the CytoDiff approach provides additional information concerning the white blood cells in pathological context never obtained previously by cytomorphology including the detection of likely pro-inflammatory monocytes, several blast subsets, and multiple lymphocyte sub populations as well.

scholarly journals Utility of hematology analyzer and flow cytometry in timely and correct detection of circulating plasma cells: Report of three cases

2016 ◽  
Vol 90 (6) ◽  
pp. 531-537 ◽  
Author(s):  
Evdoxia Gounari ◽  
Vasiliki Tsavdaridou ◽  
Triantafyllia Koletsa ◽  
Androula Nikolaidou ◽  
Georgia Kaiafa ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Plasma Cells ◽  
Correct Detection ◽  
Hematology Analyzer

Rapid Prediction of Peripheral Blood CD34 Counts Using a Multiple Regression Model Derived from Automated Hematology Analyzer Cell Population Data

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3099-3099 ◽  
Author(s):  
Thomas Porturas ◽  
Mary Sell ◽  
Leah Irwin ◽  
Una O'Doherty ◽  
Carlos Hipolito Villa
Keyword(s):  
Flow Cytometry ◽  
Stem Cell ◽  
Multiple Regression ◽  
Peripheral Blood ◽  
Population Data ◽  
Blood Samples ◽  
Peripheral Blood Cd34 ◽  
Cell Counts ◽  
Flow Cytometric ◽  
Hematology Analyzer

Abstract Background: Although peripheral blood CD34+ stem cell counts by flow cytometry correlate well with yields, the time, complexity, and cost associated with flow cytometry limits its utility. Rapid, cost-effective, surrogate predictors (with <1hr turnaround) would allow for same-visit analyses and alteration of collection and mobilization strategies, particularly for the optimal use of time-sensitive and costly agents such as plerixafor. We previously demonstrated that morphologic parameters of neutrophil-like cells measured by hematology analyzers correlated with CD34 counts. We aimed to improve these models by using multiple regression analyses on data from a common hematology analyzer. Methods: Patients undergoing stem cell apheresis were evaluated over a 6 month period. The day prior to initiation of apheresis, and on the morning of initial collection, peripheral blood samples were drawn into EDTA collection tubes and flow cytometric CD34 measurement and/or CBCs were performed on the Beckman Coulter DxH 800 hematology analyzer per standard protocol. CD34 cells were counted by flow cytometric ISHAGE protocols. Data from the DxH (48 variables per specimen) were exported into a data matrix with the corresponding flow cytometric data. Multiple regression analysis was performed using a step-wise method with log(peripheral CD34) as the dependent variable (SPSS, IBM). Data were randomly selected into a training-set of 70% of cases and a test-set of 30% of cases for validation. The derived model was further tested against peripheral blood data from the morning of collection to predict harvest yields. Further analyses were performed using Prism (GraphPad). Results: Tandem peripheral blood CD34 counts and CBC cell-population data were obtained from 69 blood samples in 64 patients. The population included patients with multiple myeloma (45), non-Hodgkin lymphoma (12), Hodgkin lymphoma (5), and amyloidosis (2). 41% of patients were female. In the test data set examining collection yields, 37 patients were mobilized with GCSF (+/- chemotherapy) alone, while 17 had plerixafor added to the regimen. 33 of these patients had same-day CBC data available for model prediction. The median processed volume was 15 L (range 5.9 to 19.7). The model to predict peripheral CD34 counts incorporated 3 variables from the hematology analyzer data (SD-V-EGC, SD-C-EGC, and NE#). Interestingly, the model included two variables descriptive of the morphology of early granulocytic cells. The model demonstrated an R value of 0.829 (adjusted R2 = 0.670, figure 1a). In testing the morning-of-collection model-predicted peripheral CD34, we found the model performed similarly to flow cytometry in predicting 1st collection yields. Furthermore, the CD34 prediction using the model (Figure 1 b) resulted in similar correlation with first-collection yields in patients treated with plerixafor versus patients not treated with plerixafor, in contrast to day-prior CD34 counts by flow-cytometry (Figure 1c). Two outliers for CD34 cell yield based on model predicted peripheral CD34 were identified. In one patient, the processed volume was very low (6.8 L, <5% percentile), while the second had a low mononuclear cell collection efficiency (35%) compared to the mean in this population (58.7%±23.3%). Threshold values for the model accurately identified patients appropriate for collection initiation (or plerixafor administration). Conclusion: Using data from a common, automated CBC analyzer, we developed a rapid, less-costly, and simple model to predict CD34 cell counts and 1st harvest yields. Because the measurement results can be obtained within the same clinic visit, and can be repeated with each CBC, the model is particularly useful to guide optimal use of plerixafor. We also envision that the model is useful for quality assurance of collection by identifying patients in whom cell yields were sub-optimal with respect to predicted CD34 cell counts. Additional studies to test the model in a larger population are ongoing. We propose that this model (and similarly derived models) can be implemented in clinical planning algorithms to improve the efficiency and cost of stem cell collection by apheresis. Acknowledgments: We would like to acknowledge and the nurses and staff of the apheresis unit and the stem cell and flow cytometry laboratories at the Hospital of the University of Pennsylvania for their contributions. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

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