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      A predictive model for HIV-related lymphoma

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          Abstract

          Objectives:

          To address the paucity of HIV-related lymphoma (HRL)–specific prognostic scores for the Japanese population by analyzing domestic cases of HRL and constructing a predictive model.

          Design:

          A single-center retrospective study coupled with a review of case reports of HRL.

          Methods:

          We reviewed all patients with HRL treated at our hospital between 2007 and 2023 and conducted a comprehensive search for case reports of HRL from Japan using public databases. A multivariate analysis for overall survival (OS) was performed using clinical parameters, leading to the formulation of the HIV-Japanese Prognostic Index (HIV-JPI).

          Results:

          A total of 19 patients with HRL were identified in our institution, whereas the literature review yielded 44 cases. In the HIV-JPI, a weighted score of 1 was assigned to the following factors: age at least 45 years, HIV-RNA at least 8.0×10 4 copies/ml, Epstein–Barr virus-encoded small RNA positivity, and Ann Arbor classification stage IV. The overall score ranged from 0 to 4. We defined the low-risk group as scores ranging from 0 to 2 and the high-risk group as scores ranging from 3 to 4. The 3-year OS probability of the high-risk group [30.8%; 95% confidence interval (CI): 9.5–55.4%) was significantly poorer than that of the low-risk group (76.8%; 95% CI: 52.8–89.7%; P < 0.01).

          Conclusion:

          This retrospective analysis established pivotal prognostic factors for HRL in Japanese patients. The HIV-JPI, derived exclusively from Japanese patients, highlights the potential for stratified treatments and emphasizes the need for broader studies to further refine this clinical prediction model.

          Abstract

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          Most cited references82

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          Investigation of the freely available easy-to-use software ‘EZR' for medical statistics

          Y Kanda (2012)
          Although there are many commercially available statistical software packages, only a few implement a competing risk analysis or a proportional hazards regression model with time-dependent covariates, which are necessary in studies on hematopoietic SCT. In addition, most packages are not clinician friendly, as they require that commands be written based on statistical languages. This report describes the statistical software ‘EZR' (Easy R), which is based on R and R commander. EZR enables the application of statistical functions that are frequently used in clinical studies, such as survival analyses, including competing risk analyses and the use of time-dependent covariates, receiver operating characteristics analyses, meta-analyses, sample size calculation and so on, by point-and-click access. EZR is freely available on our website (http://www.jichi.ac.jp/saitama-sct/SaitamaHP.files/statmed.html) and runs on both Windows (Microsoft Corporation, USA) and Mac OS X (Apple, USA). This report provides instructions for the installation and operation of EZR.
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            MissForest--non-parametric missing value imputation for mixed-type data.

            Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch
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              Initiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection

              New England Journal of Medicine, 373(9), 795-807
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                Author and article information

                Journal
                AIDS
                AIDS
                AIDS
                AIDS (London, England)
                Lippincott Williams & Wilkins (Hagerstown, MD )
                0269-9370
                1473-5571
                01 September 2024
                24 June 2024
                : 38
                : 11
                : 1627-1637
                Affiliations
                [a ]Department of Hematology
                [b ]Division of Infectious Disease, Yokohama Municipal Citizen's Hospital, Yokohama, Japan.
                Author notes
                Correspondence to Shuhei Kurosawa, MD, PhD, Department of Hematology, Yokohama Municipal Citizen's Hospital, Mitsuzawa Nishimachi, Kanagawa-Ku, Yokohama, Kanagawa 221-0855, Japan. Tel: +81 45 316 4580; fax: +81 45 316 6580; e-mail: 1a.4ftnn6@ 123456gmail.com
                Article
                AIDS-D-24-00064 00003
                10.1097/QAD.0000000000003949
                11296280
                38831732
                8edb6d61-23f2-4635-a006-1b6a17a9ca14
                Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

                History
                : 9 February 2024
                : 24 April 2024
                : 28 May 2024
                Categories
                Clinical Science
                Custom metadata
                TRUE
                T

                aids,akaike information criterion,hiv-related lymphoma,hiv

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