ArticleJournal of inflammation research2025
Prognostic Implications of Red Blood Cell Distribution Width to Albumin Ratio in Myelofibrosis: A 10-Year Multicenter and Retrospective Study.
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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16 authors.
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Abstract
Introduction: Myelofibrosis (MF) is a rare myeloproliferative neoplasm (MPN) characterized by significant mortality and limited predictive biomarkers. The red cell distribution width to albumin ratio (RAR), a novel biomarker indicative of inflammation, has emerged as a strong prognostic indicator in the general population but remains unexplored in MF. Methods: We retrospectively enrolled 504 consecutive MF patients from 7 hematological centers over a 10-year period. Multivariate Cox regressions were performed to assess the prognostic value of the RAR. Kaplan-Meier and restricted cubic splines (RCS) analyses were further performed to examine the associations between RAR and outcomes. Interaction and subgroup analyses were conducted to explore potential effect modifiers. We developed a predictive nomogram combining RAR and DIPSS-plus, with its incremental improvements assessed by discrimination and calibration metrics. Results: Patients who experienced leukemic transformation and death had significantly higher RAR levels. RAR remained an independent predictor of poor survival (adjusted HR: 1.58, 95% CI: 1.36-1.85). RCS further suggested a positive non-linear association between RAR and overall survival. Adding RAR to DIPSS-plus score significantly improved prediction accuracy, as shown by an increased C-index from 0.709 to 0.762, a net reclassification improvement (31.1%, Conclusion: This large, multi-center cohort study is the first to reveal the prognostic significance of RAR in MF. The modified predictive nomogram combining DIPSS-plus score and RAR enhances prognostic discrimination and calibration, providing a simple yet cost-effective tool for refined risk stratification, especially in resource-limited settings.
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