ArticleAIDS (London, England)2025
A promising prognostic model for patients with AIDS-related lymphoma in the combination antiretroviral therapy era.
Article in AIDS (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- New Insights Into the Role of Neutrophils in Lymphoma.Cancer medicine · 2026Review
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8 authors.
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Abstract
objectiveFor patients with AIDS-related lymphoma (ARL), optimizing risk stratification is crucial to creating customized therapy regimens and enhancing their prognosis. This study aims to develop a more precisely predicted prognostic model for ARL patients.
designA 7-year retrospective cohort study (2016-2023) of 136 ARL patients at a single institution randomly allocated training ( n = 109) and validation ( n = 27) cohorts.
methodsWe assessed the relationship between HIV, lymphoma, and patient-specific factors and overall survival (OS) and progression-free survival (PFS) by univariate and multivariate analyses.
resultsThe median age was 48 (IQR: 40-56) years, 76.5% were men. The overall 2-year OS and PFS were 52.9 and 48.5%, respectively. In the multivariate analysis, Eastern Cooperative Oncology Group performance status (ECOG-PS), central nervous system (CNS) involvement, elevated lactate dehydrogenase (LDH), Hemoglobin (Hb), neutrophil-lymphocyte ratio (NLR), and chemotherapy cycles were independently related to OS. A new prognosis score was generated with these variables, including ECOG at least 2, CNS involvement, elevated LDH, Hb less than 130 g/l, NLR more than 5, and not exceeding 5 chemotherapy cycles, with 1 point for each variable, for a maximum of 6. The area under the curve and C-index of the new model were 0.79 and 0.76, respectively. Our model showed better risk stratification in ARL patients than aaIPI, NCCN-IPI, and ARL-IPI.
conclusionIn this study, we created a prognostic model for ARL patients that is clinically straightforward, feasible, and has good predictive power. Compared to the NCCN-IPI and the aaIPI, this model is more discriminative and predictively accurate in risk stratification and high-risk population identification.
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