A predictive model for HIV-related lymphoma

Author:

Kurosawa Shuhei1,Yoshimura Yukihiro2,Takada Yusuke1,Yokota Takako1,Hibi Masaki1,Hirahara Ayumi1,Yoshida Tsutomu1,Okubo So1,Masuda Moe1,So Yuna2,Miyata Nobuyuki2,Nakayama Hitomi1,Sakurai Aki1,Sato Kosuke2,Ito Chisako1,Aisa Yoshinobu1,Nakazato Tomonori1

Affiliation:

1. Department of Hematology

2. Division of Infectious Disease, Yokohama Municipal Citizen's Hospital, Yokohama, Japan.

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 8.0×104 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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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