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      NCCN-IPI score-independent prognostic potential of pretreatment uric acid levels for clinical outcome of diffuse large B-cell lymphoma patients

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          Abstract

          Background:

          Blood-based parameters are gaining increasing interest as potential prognostic biomarkers in patients with diffuse large B-cell lymphoma (DLBCL). The aim of this study was to comprehensively evaluate the prognostic significance of pretreatment plasma uric acid levels in patients with newly diagnosed DLBCL.

          Methods:

          The clinical course of 539 DLBCL patients, diagnosed and treated between 2004 and 2013 at two Austrian high-volume centres with rituximab-based immunochemotherapy was evaluated retrospectively. The prognostic influence of uric acid on overall survival (OS) and progression-free survival (PFS) were studied including multi-state modelling, and analysis of conditional survival.

          Results:

          Five-year OS and PFS were 50.4% (95% CI: 39.2–60.6) and 44.0% (33.4–54.0) in patients with uric acid levels above the 75th percentile of the uric acid distribution (Q3, cut-off: 6.8 mg dl −1), and 66.2% (60.4–71.5) and 59.6% (53.7–65.0%) in patients with lower levels (log-rank P=0.002 and P=0.0045, respectively). In univariable time-to-event analysis, elevated uric acid levels were associated with a worse PFS (hazard ratio (HR) per 1 log increase in uric acid 1.47, 95% CI: 1.10–1.97, P=0.009) and a worse OS (HR=1.60, 95% CI: 1.16–2.19, P=0.004). These associations prevailed upon multivariable adjustment for the NCCN-IPI score. Uric acid levels significantly improved the predictive performance of the R-IPI and NCCN-IPI scores, and in multi-state analysis, it emerged as a highly significant predictor of an increased risk of death without developing recurrence (transition-HR=4.47, 95% CI: 2.17–9.23, P<0.0001).

          Conclusions:

          We demonstrate that elevated uric acid levels predict poor long-term outcomes in DLBCL patients beyond the NCCN-IPI risk index.

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          Most cited references16

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          The revised International Prognostic Index (R-IPI) is a better predictor of outcome than the standard IPI for patients with diffuse large B-cell lymphoma treated with R-CHOP.

          Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity, with patients exhibiting a wide range of outcomes. The addition of rituximab to CHOP chemotherapy (R-CHOP)has led to a marked improvement in survival and has called into question the significance of previously recognized prognostic markers. Since randomized controlled trials of R-CHOP in DLBCL have included select subgroups of patients, the utility of the International Prognostic Index (IPI) has not been reassessed. We performed a retrospective analysis of patients with DLBCL treated with R-CHOP in the province of British Columbia to assess the value of the IPI in the era of immunochemotherapy. The IPI remains predictive, but it identifies only 2 risk groups. Redistribution of the IPI factors into a revised IPI (R-IPI) provides a more clinically useful prediction of outcome. The R-IPI identifies 3 distinct prognostic groups with a very good (4-year progression-free survival [PFS] 94%, overall survival [OS] 94%), good (4-year PFS 80%, OS 79%), and poor (4-year PFS 53%, OS 55%) outcome, respectively (P < .001). The IPI (or R-IPI) no longer identifies a risk group with less than a 50% chance of survival. In the era of R-CHOP treatment, the R-IPI is a clinically useful prognostic index that may help guide treatment planning and interpretation of clinical trials.
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            An enhanced International Prognostic Index (NCCN-IPI) for patients with diffuse large B-cell lymphoma treated in the rituximab era.

            The International Prognostic Index (IPI) has been the basis for determining prognosis in patients with aggressive non-Hodgkin lymphoma (NHL) for the past 20 years. Using raw clinical data from the National Comprehensive Cancer Network (NCCN) database collected during the rituximab era, we built an enhanced IPI with the goal of improving risk stratification. Clinical features from 1650 adults with de novo diffuse large B-cell lymphoma (DLBCL) diagnosed from 2000-2010 at 7 NCCN cancer centers were assessed for their prognostic significance, with statistical efforts to further refine the categorization of age and normalized LDH. Five predictors (age, lactate dehydrogenase (LDH), sites of involvement, Ann Arbor stage, ECOG performance status) were identified and a maximum of 8 points assigned. Four risk groups were formed: low (0-1), low-intermediate (2-3), high-intermediate (4-5), and high (6-8). Compared with the IPI, the NCCN-IPI better discriminated low- and high-risk subgroups (5-year overall survival [OS]: 96% vs 33%) than the IPI (5 year OS: 90% vs 54%), respectively. When validated using an independent cohort from the British Columbia Cancer Agency (n = 1138), it also demonstrated enhanced discrimination for both low- and high-risk patients. The NCCN-IPI is easy to apply and more powerful than the IPI for predicting survival in the rituximab era.
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              Net reclassification indices for evaluating risk prediction instruments: a critical review.

              Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For predefined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true- and false-positive rates. We advocate the use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid predefined risk categories. However, it experiences many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap methods rather than published variance formulas. The preferred single-number summary of the prediction increment is the improvement in net benefit.
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                Author and article information

                Journal
                Br J Cancer
                Br. J. Cancer
                British Journal of Cancer
                Nature Publishing Group
                0007-0920
                1532-1827
                08 November 2016
                20 October 2016
                : 115
                : 10
                : 1264-1272
                Affiliations
                [1 ]Division of Hematology, Department of Internal Medicine, Medical University of Graz (MUG) , Graz 8036, Austria
                [2 ]Laboratory for Immunological and Molecular Cancer Research, Oncologic Center, 3rd Medical Department with Hematology and Medical Oncology, Hemostaseology, Rheumatology and Infectious Diseases, Paracelsus Medical University Salzburg , Salzburg 5020, Austria
                [3 ]Division of Oncology, Department of Internal Medicine, Medical University of Graz (MUG) , Graz 8036, Austria
                [4 ]Institute of Pathology, Medical University of Graz (MUG) , Graz 8036, Austria
                [5 ]Division of Angiology, Department of Internal Medicine, Medical University of Graz (MUG) , Graz 8036, Austria
                [6 ]Institute of Pathology, Paracelsus Medical University Salzburg , Salzburg 5020, Austria
                [7 ]Research Unit for non-coding RNAs and genome editing in cancer , Graz 8036, Austria
                [8 ]Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center , Houston, TX 77054, USA
                Author notes
                [9]

                These authors contributed equally to this work.

                Article
                bjc2016325
                10.1038/bjc.2016.325
                5104895
                27764838
                23ee2a07-438f-467d-ad2b-17e008a1741b
                Copyright © 2016 Cancer Research UK

                From twelve months after its original publication, this work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

                Categories
                Molecular Diagnostics

                Oncology & Radiotherapy
                prognosis,dlbcl,uric acid
                Oncology & Radiotherapy
                prognosis, dlbcl, uric acid

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