Giovanni Barosi

Bio: Giovanni Barosi is an academic researcher from University of Pavia. The author has contributed to research in topics: Myelofibrosis & Essential thrombocythemia. The author has an hindex of 69, co-authored 334 publications receiving 19225 citations.

JAK inhibition with ruxolitinib versus best available therapy for myelofibrosis.

Abstract: Background Treatment options for myelofibrosis are limited. We evaluated the efficacy and safety of ruxolitinib, a potent and selective Janus kinase (JAK) 1 and 2 inhibitor, as compared with the best available therapy, in patients with myelofibrosis. Methods We assigned 219 patients with intermediate-2 or high-risk primary myelofibrosis, post–polycythemia vera myelofibrosis, or post–essential thrombocythemia myelofi brosis to receive oral ruxolitinib or the best available therapy. The primary end point and key secondary end point of the study were the percentage of patients with at least a 35% reduction in spleen volume at week 48 and at week 24, respectively, as assessed with the use of magnetic resonance imaging or computed tomography. Results A total of 28% of the patients in the ruxolitinib group had at least a 35% reduction in spleen volume at week 48, as compared with 0% in the group receiving the best available therapy (P<0.001); the corresponding percentages at week 24 were 32% and 0% (P<0.001). At 48 weeks, the mean palpable spleen length had decreased by 56% with ruxolitinib but had increased by 4% with the best available therapy. The median duration of response with ruxolitinib was not reached, with 80% of patients still having a response at a median follow-up of 12 months. Patients in the ruxolitinib group had an improvement in overall quality-of-life measures and a reduction in symptoms associated with myelofibrosis. The most common hematologic abnormalities of grade 3 or higher in either group were thrombocytopenia and anemia, which were managed with a dose reduction, interruption of treatment, or transfusion. One patient in each group discontinued treatment owing to thrombocytopenia, and none discontinued owing to anemia. Nonhematologic adverse events were rare and mostly grade 1 or 2. Two cases of acute myeloid leukemia were reported with the best available therapy. Conclusions Continuous ruxolitinib therapy, as compared with the best available therapy, was associated with marked and durable reductions in splenomegaly and disease-related symptoms, improvements in role functioning and quality of life, and modest toxic effects. An influence on overall survival has not yet been shown. (Funded by Novartis Pharmaceuticals; ClinicalTrials.gov number, NCT00934544.)

Philadelphia-Negative Classical Myeloproliferative Neoplasms: Critical Concepts and Management Recommendations From European LeukemiaNet

Abstract: We present a review of critical concepts and produce recommendations on the management of Philadelphia-negative classical myeloproliferative neoplasms, including monitoring, response definition, fi ...

WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues

Abstract: WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

mice: Multivariate Imputation by Chained Equations in R

Abstract: The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice, which extends the functionality of mice 1.0 in several ways. In mice, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.