BACKGROUND: The VCS technology of Beckman Coulter differentiates white blood cells based on measures of their volume, conductivity and light scatter. The current study investigated the predictive value of index measures, known as research population data, for the detection of chronic lymphatic leukemia and myelodysplastic syndrome. METHODS: Blood cell counts were performed in samples from 44 patients with chronic lymphatic leukemia, 19 patients with myelodysplastic syndrome and 199 healthy blood donors using the Beckman Coulter LH750. Means and standard deviations of volume, conductivity and scatter of lymphocytes and neutrophils were evaluated as predictors for both diseases. Their specificity and selectivity were evaluated by logistic regression and receiver operating characteristic curve analysis. RESULTS: Research population data were significantly different among groups. For chronic lymphatic leukemia, standard deviations of lymphocytes scatter and volume showed most relevant differences in comparison to healthy blood donors (sensitivity 88.6%, specificity 84.4%). For myelodysplastic syndrome, standard deviations of neutrophils conductivity were most predictive (sensitivity 73.7%, specificity 93.0%). Areas under corresponding receiver operating characteristic curves were 0.941 and 0.951, respectively. CONCLUSIONS: Based on their high predictive value, research population data could be routinely used to screen for chronic lymphatic leukemia and myelodysplastic syndrome.
BACKGROUND: The VCS technology of Beckman Coulter differentiates white blood cells based on measures of their volume, conductivity and light scatter. The current study investigated the predictive value of index measures, known as research population data, for the detection of chronic lymphatic leukemia and myelodysplastic syndrome. METHODS: Blood cell counts were performed in samples from 44 patients with chronic lymphatic leukemia, 19 patients with myelodysplastic syndrome and 199 healthy blood donors using the Beckman Coulter LH750. Means and standard deviations of volume, conductivity and scatter of lymphocytes and neutrophils were evaluated as predictors for both diseases. Their specificity and selectivity were evaluated by logistic regression and receiver operating characteristic curve analysis. RESULTS: Research population data were significantly different among groups. For chronic lymphatic leukemia, standard deviations of lymphocytes scatter and volume showed most relevant differences in comparison to healthy blood donors (sensitivity 88.6%, specificity 84.4%). For myelodysplastic syndrome, standard deviations of neutrophils conductivity were most predictive (sensitivity 73.7%, specificity 93.0%). Areas under corresponding receiver operating characteristic curves were 0.941 and 0.951, respectively. CONCLUSIONS: Based on their high predictive value, research population data could be routinely used to screen for chronic lymphatic leukemia and myelodysplastic syndrome.