Abstract Introduction The availability of a clinical decision algorithm for diagnosis of chronic lymphocytic leukemia (CLL) may greatly contribute to the diagnosis of CLL, particularly in cases with ambiguous immunophenotypes. Herein we propose a novel differential diagnosis algorithm for the CLL diagnosis using immunophenotyping with flow cytometry. Methods The hierarchical logistic regression model (Backward LR) was used to build a predictive algorithm for the diagnosis of CLL, differentiated from other lymphoproliferative disorders (LPDs). Results A total of 302 patients, of whom 220 (72.8%) had CLL and 82 (27.2%), B-cell lymphoproliferative disorders other than CLL, were included in the study. The Backward LR model comprised the variables CD5, CD43, CD81, ROR1, CD23, CD79b, FMC7, sIg and CD200 in the model development process. The weak expression of CD81 and increased intensity of expression in markers CD5, CD23 and CD200 increased the probability of CLL diagnosis, (p < 0.05). The odd ratio for CD5, C23, CD200 and CD81 was 1.088 (1.050 - 1.126), 1.044 (1.012 - 1.077), 1.039 (1.007 - 1.072) and 0.946 (0.921 - 0.970) [95% C.I.], respectively. Our model provided a novel diagnostic algorithm with 95.27% of sensitivity and 91.46% of specificity. The model prediction for 97.3% (214) of 220 patients diagnosed with CLL, was CLL and for 91.5% (75) of 82 patients diagnosed with an LPD other than CLL, was others. The cases were correctly classified as CLL and others with a 95.7% correctness rate. Conclusions Our model highlighting 4 markers (CD81, CD5, CD23 and CD200) provided high sensitivity and specificity in the CLL diagnosis and in distinguishing of CLL among other LPDs.

SUMMARY OBJECTIVE: The aim of this study was to compare the incidence of factors associated with an increased risk of thrombosis in patients with essential thrombocythemia. METHODS: A total of 200 patients followed-up in our unit with a diagnosis of essential thrombocythemia in 13 years were analyzed retrospectively. RESULTS: Of the study participants, 60.5% were females and 39.5% were males, with an overall mean (±SD) age of 54.93 (±14.21) years. In 119 patients, Janus Kinase 2 was positive with 56.3% of cases. When two patient categories were defined as those with or without history of thrombosis, no significant differences were found in terms of Janus Kinase 2 positivity, mean age, as well as white blood cells and platelet counts (p>0.05). Also, no significant differences in thrombotic event incidence were found between patient categories defined on the basis of cut-off values for white blood cells (cut-off values of 15×103/mm3 and 8.7×103/mm3) and platelets (cut-off values of 1500×103/mm3) (p>0.05). CONCLUSION: Although our results are generally in line with the published data, some divergence from previous results has been observed with respect to risk factors for thrombotic events. Absence of a correlation between leukocytosis and thrombosis may be related with the significant decline in white blood cells after treatment. Also, a significant reduction in platelet counts occurring in association with treatment is linked with a lowered incidence of thrombosis. Janus Kinase 2-positive patients had a similar thrombosis frequency with that reported in the literature.