ABSTRACT
The presence of CD19 in myelomatous plasma cells (MM-PCs) correlates with adverse prognosis in multiple myeloma (MM). Although CD19 expression is upregulated by CD81, this marker has been poorly investigated and its prognostic value in MM remains unknown. We have analyzed CD81 expression by multiparameter flow cytometry in MM-PCs from 230 MM patients at diagnosis included in the Grupo Español de Mieloma (GEM)05>65 years trial as well as 56 high-risk smoldering MM (SMM). CD81 expression was detected in 45% (103/230) MM patients, and the detection of CD81(+) MM-PC was an independent prognostic factor for progression-free (hazard ratio=1.9; P=0.003) and overall survival (hazard ratio=2.0; P=0.02); this adverse impact was validated in an additional series of 325 transplant-candidate MM patients included in the GEM05 <65 years trial. Moreover, CD81(+) SMM (n=34/56, 57%) patients had a shorter time to progression to MM (P=0.02). Overall, our results show that CD81 may have a relevant role in MM pathogenesis and represent a novel adverse prognostic marker in myeloma.
Subject(s)
Multiple Myeloma/genetics , Multiple Myeloma/metabolism , Plasma Cells/metabolism , Tetraspanin 28/genetics , Aged , Aged, 80 and over , Gene Expression Regulation, Neoplastic , Humans , Immunophenotyping , Middle Aged , Multiple Myeloma/mortality , Prognosis , Survival Analysis , Tetraspanin 28/metabolismABSTRACT
OBJECTIVES: To estimate the organisational impact of the volume of appointments processed in the Primary Care (PC) Administration Units (AU) of Area 10, and to evaluate the effectiveness of organisational measures to correct the excess of appointments processed at particular times of day. DESIGN: Before-and-after intervention study. SETTING: AU of 16 PC teams from Madrid's Area 10. PARTICIPANTS: All the appointments made for users by 78 clerks in the 16 AU in the Area during the two weeks of the study. INTERVENTIONS: Strengthening of administrative staff dealing with the appointment system; and an information campaign for users about the system. MEASUREMENTS AND RESULTS: The percentage of appointments processed by AU was broken down for morning and afternoon, by the way the appointment was made (telephone/counter) and by the scheduling of the list. The hourly development was analysed by a graph. A mean number of appointments per clerk per day was found. The above figures were compared for before and after intervention. CONCLUSIONS: The work load caused by the appointment system was redistributed by internal organisational measures, since user habits did not alter.