Two mid-range haematology analysers (Abbott CELL-DYN Ruby and Sysmex XT-2000i) were evaluated to determine their analytical performance and workflow efficiency in the haematology laboratory. In total 418 samples were processed for determining equivalence of complete blood count (CBC) measurements, and 100 for reticulocyte comparison. Blood smears served for assessing the agreement of the differential counts. Inter-instrument agreement for most parameters was good although small numbers of discrepancies were observed. Systematic biases were found for mean cell volume, reticulocytes, platelets and mean platelet volume. CELL-DYN Ruby WBC differentials were obtained with all samples while the XT-2000i suppressed differentials partially or completely in 13 samples (3.1%). WBC subpopulation counts were otherwise in good agreement with no major outliers. Following first-pass CBC/differential analysis, 88 (21%) of XT-2000i samples required further analyser processing compared to 18 (4.3%) for the CELL-DYN Ruby. Smear referrals for suspected WBC/nucleated red blood cells and platelet abnormalities were indicated for 106 (25.4%) and 95 (22.7%) of the XT-2000i and CELL-DYN Ruby samples respectively. Flagging efficiencies for both analysers were found to be similar. The Sysmex XT-2000i and Abbott CELL-DYN Ruby analysers have broadly comparable analytical performance, but the CELL-DYN Ruby showed superior first-pass efficiency.

Searching for informative indices indicating cancer type and cause of the disease is of great importance. Here we tried to identify those indices from the data of complete blood analysis, only. We studied the inhomogeneity in the mutual distribution of a number of oncology patients with various types of tumors in the space of qualitative data provided by the complete blood count. The patients with oncology in hematology system were excluded. Ultimate goal is to reveal the relation between such inhomogeneity issues and the cause of a disease. We used the database on complete blood count comprising oncology patients with various causes of tumor development. The analysis has been carried out both by linear (K-means) and non-linear (elastic map technique) methods. No linear clustering has been found. On the contrary, elastic map technique yields stable clustering identifying not less than three clusters, in the set of patients. No relation of those clusters to sex or age of patients has been found. Four indices (namely, BAS, EOS, WBC and IG) exhibit no relation to the cluster structure, while all others do it. Thus, the patients are stratified according to their respond on the stress caused by cancer tumor. The data on complete blood count may be used for preliminary diagnostics of a tumor and its cause, for oncology patients. This type of analysis is cheap, standard and available at any medical organization.

Early prediction of tumor process especially related to the detection of oncological diseases at early stage is a hot problem. We compared the complete blood count data for patients with various types of tumors; the patients with tumors of the hematopoietic system were excluded from the study. We used both linear (K-means) and non-linear (elastic maps) methods. No informative patterns have been found through the implementation of classic linear statistics methods. On the contrary, the elastic maps technique revealed the specific groups among the patients. Neither relation to the gender, nor age of patients has been found. However, the abundance of basophils, eosinophils, leukocytes and immunoglobulins exhibit specific pattern of the clusters occupancy. Thus, in our study we found that patients are stratified in accordance with quantitative changes in laboratory blood parameters, which is associated with the body’s response to stress caused by a malignant tumor.