ABSTRACT
From 2016 EBMT and JACIE developed an international risk-adapted benchmarking program of haematopoietic stem cell transplant (HSCT) outcome to provide individual EBMT Centers with a means of quality-assuring the HSCT process and meeting FACT-JACIE accreditation requirements relating to 1-year survival outcomes. Informed by previous experience from Europe, North America and Australasia, the Clinical Outcomes Group (COG) established criteria for patient and Center selection, and a set of key clinical variables within a dedicated statistical model adapted to the capabilities of the EBMT Registry. The first phase of the project was launched in 2019 to test the acceptability of the benchmarking model through assessment of Centers' performance for 1-year data completeness and survival outcomes of autologous and allogeneic HSCT covering 2013-2016. A second phase was delivered in July 2021 covering 2015-2019 and including survival outcomes. Reports of individual Center performance were shared directly with local principal investigators and their responses were assimilated. The experience thus far has supported the feasibility, acceptability and reliability of the system as well as identifying its limitations. We provide a summary of experience and learning so far in this 'work in progress', as well as highlighting future challenges of delivering a modern, robust, data-complete, risk-adapted benchmarking program across new EBMT Registry systems.
Subject(s)
Benchmarking , Hematopoietic Stem Cell Transplantation , Humans , Bone Marrow , Reproducibility of Results , Europe , AccreditationABSTRACT
Lymphoma with secondary central nervous system (CNS) involvement represents one of the most aggressive malignancies, with poor prognosis and high mortality. New diagnostic tools for its early detection, response evaluation, and CNS relapse prediction are needed. We analyzed circulating microRNAs in the cerebrospinal fluid (CSF) and plasma of 162 patients with aggressive B-cell non-Hodgkin's lymphomas (B-NHL) and compared their levels in CNS-involving lymphomas versus in systemic lymphomas, at diagnosis and during treatment and CNS relapse. We identified a set of five oncogenic microRNAs (miR-19a, miR-20a, miR-21, miR-92a, and miR-155) in CSF that detect, with high sensitivity, secondary CNS lymphoma involvement in aggressive B-NHL, including DLBCL, MCL, and Burkitt lymphoma. Their combination into an oncomiR index enables the separation of CNS lymphomas from systemic lymphomas or nonmalignant controls with high sensitivity and specificity, and high Receiver Operating Characteristics (DLBCL AUC = 0.96, MCL = 0.93, BL = 1.0). Longitudinal analysis showed that oncomiR levels reflect treatment efficacy and clinical outcomes, allowing their monitoring and prediction. In contrast to conventional methods, CSF oncomiRs enable detection of early and residual CNS involvement, as well as parenchymal involvement. These circulating oncomiRs increase 1-4 months before CNS relapse, allowing its early detection and improving the prediction of CNS relapse risk in DLBCL. Similar effects were detectable, to a lesser extent, in plasma.
ABSTRACT
This retrospective study evaluated the secondary clinical risk score at relapse, the prognostic significance of pre-transplant positron emission tomography (PET), and complete remission (CR) assessed by computed tomography (CT) after salvage chemotherapy before autologous stem cell transplant (ASCT) in 76 patients with relapsed/refractory Hodgkin lymphoma (HL). Median follow-up after ASCT was 23 months. Overall 11/20 PET-positive and 14/56 PET-negative patients relapsed after ASCT. In univariate analysis, only PET negativity before ASCT was significantly associated with better 2-year progression-free survival (PFS) (72.7 ± 6.3% vs. 36.1 ± 11.6%, p = 0.01) and 2-year overall survival (OS) (90.3 ± 4.1% vs. 61.4 ± 11.6%, p = 0.009). Other factors were not significant. In multivariate analysis, none of the evaluated factors were significant for PFS and OS. However, positive pre-transplant PET identified a population with worse PFS and OS at least in univariate analysis.