Clinical performance evaluation of the new hematology analyzer Mindray BC‐6000

Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters

OBJECTIVE

We investigated the predictive value of a logistic model utilizing hematological parameters in diagnosing occupational lead poisoning.

METHODS

This retrospective study (September 2020-December 2022) included patients with occupational lead poisoning. Differences in hematological parameters were compared between individuals with occupational blood lead poisoning and healthy individuals. We used logistic regression analysis to develop a diagnostic prediction model for occupational blood lead poisoning. Receiver operating characteristic (ROC) curves and corresponding area under the ROC curve values were used to assess the diagnostic value of hematological parameters and logistic models.

RESULTS

Compared with controls, several indicators were significantly higher in the group with blood lead poisoning, but others were significantly lower. Logistic regression analysis showed that the red blood cell distribution width coefficient of variation (RDW-CV), neutrophil/lymphocyte ratio (NLR), and percentage of small red blood cells (Micro%) were independent factors in diagnosing occupational blood lead poisoning. The logistic regression model constructed based on these three parameters had sensitivity 78.7% and specificity 83.8% for diagnosing occupational lead poisoning.

CONCLUSION

We identified RDW-CV, NLR, and Micro% as independent predictors in the diagnosis of occupational lead poisoning. A logistic regression model that includes these may contribute to better detection of occupational lead poisoning.

Evaluation and comparison of the new Mindray BC-6200 hematology analyzer with ADVIA 2120i

BACKGROUND

The Mindray BC-6200 is a new automatic hematology analyzer that quantifies the parameters of blood morphology and leukocyte differential in five populations (5-Diff). The aim of the study was to evaluate the BC-6200 and compare it with the Siemens ADVIA 2120i analyzer.

MATERIALS AND METHODS

The comparison between BC-6200 and ADVIA 2120i analyzers was performed using 390 whole blood samples collected on K₃ EDTA. For the BC-6200, the carryover effect, precision, and linearity were evaluated. 138 samples were used to assess the sensitivity and flag ability, suggesting the presence of abnormal cells such as blasts, immature granulocytes, or atypical lymphocytes. Flagging results were compared with microscopic evaluation of blood smears.

RESULTS

The BC-6200 analyzer showed a high correlation (r ≥ .97) with ADVIA 2120i for most of the compared parameters except RDW (r = .8350), MPV (r = .7634), Mon# (r = .8366), Baso# (r = .9205), and NRBC (r = .3768). The BC-6200 had better correlation with microscopic evaluation for NRBC (r = .8902) compared with ADVIA 2120i (r = .5677). The BC-6200 has shown high efficiency for flagging blasts (80.4%), immature granulocytes (80.5%), and atypical lymphocytes (69.0%).

CONCLUSION

The new Mindray BC-6200 hematology analyzer provides high measurements precision and good correlation with ADVIA 2120i for most of the morphology and 5-diff parameters.

Nanometer precise red blood cell sizing using a cost-effective quantitative dark field imaging system

Because of the bulk, complexity, calibration requirements, and need for operator training, most current flow-based blood counting devices are not appropriate for field use. Standard imaging methods could be much more compact, inexpensive, and with minimal calibration requirements. However, due to the diffraction limit, imaging lacks the nanometer precision required to measure red blood cell volumes. To address this challenge, we utilize Mie scattering, which can measure nanometer-scale morphological information from cells, in a dark-field imaging geometry. The approach consists of a custom-built dark-field scattering microscope with symmetrically oblique illumination at a precisely defined angle to record wide-field images of diluted and sphered blood samples. Scattering intensities of each cell under three wavelengths are obtained by segmenting images via digital image processing. These scattering intensities are then used to determine size and hemoglobin information via Mie theory and machine learning. Validation on 90 clinical blood samples confirmed the ability to obtain mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration (MCHC), and red cell distribution width (RDW) with high accuracy. Simulations based on historical data suggest that an instrument with the accuracy achieved in this study could be used for widespread anemia screening.