| Literature DB >> 35443021 |
Aaron Etra1, Stephanie Gergoudis1, George Morales1, Nikolaos Spyrou1, Jay Shah1, Steven Kowalyk1, Francis Ayuk2, Janna Baez1, Chantiya Chanswangphuwana3, Yi-Bin Chen4, Hannah Choe5, Zachariah DeFilipp4, Isha Gandhi1, Elizabeth Hexner6, William J Hogan7, Ernst Holler8, Urvi Kapoor1, Carrie L Kitko9, Sabrina Kraus10, Jung-Yi Lin11, Monzr Al Malki12, Pietro Merli13, Attaphol Pawarode14, Michael A Pulsipher15, Muna Qayed16, Ran Reshef17, Wolf Rösler18, Tal Schechter19, Grace Van Hyfte11, Daniela Weber8, Matthias Wölfl20, Rachel Young1, Umut Özbek11, James L M Ferrara1, John E Levine1.
Abstract
We used a rigorous PRoBE (prospective-specimen collection, retrospective-blinded-evaluation) study design to compare the ability of biomarkers of systemic inflammation and biomarkers of gastrointestinal (GI) tissue damage to predict response to corticosteroid treatment, the incidence of clinically severe disease, 6-month nonrelapse mortality (NRM), and overall survival in patients with acute graft-versus-host disease (GVHD). We prospectively collected serum samples of newly diagnosed GVHD patients (n = 730) from 19 centers, divided them into training (n = 352) and validation (n = 378) cohorts, and measured TNFR1, TIM3, IL6, ST2, and REG3α via enzyme-linked immunosorbent assay. Performances of the 4 strongest algorithms from the training cohort (TNFR1 + TIM3, TNFR1 + ST2, TNFR1 + REG3α, and ST2 + REG3α) were evaluated in the validation cohort. The algorithm that included only biomarkers of systemic inflammation (TNFR1 + TIM3) had a significantly smaller area under the curve (AUC; 0.57) than the AUCs of algorithms that contained ≥1 GI damage biomarker (TNFR1 + ST2, 0.70; TNFR1 + REG3α, 0.73; ST2 + REG3α, 0.79; all P < .001). All 4 algorithms were able to predict short-term outcomes such as response to systemic corticosteroids and severe GVHD, but the inclusion of a GI damage biomarker was needed to predict long-term outcomes such as 6-month NRM and survival. The algorithm that included 2 GI damage biomarkers was the most accurate of the 4 algorithms for all endpoints.Entities:
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Year: 2022 PMID: 35443021 DOI: 10.1182/bloodadvances.2022007296
Source DB: PubMed Journal: Blood Adv ISSN: 2473-9529