In:
Annals of Surgery, Ovid Technologies (Wolters Kluwer Health), Vol. 274, No. 3 ( 2021-09), p. 411-418
Abstract:
This study investigated the ability of pre-transplant T-cell clonality to predict sepsis after liver transplant (LT). Summary Background Data: Sepsis is a leading cause of death in LT recipients. Currently, no biomarkers predict sepsis before clinical symptom manifestation. Methods: Between December 2013 and March 2018, our institution performed 478 LTs. After exclusions (eg, patients with marginal donor livers, autoimmune disorders, nonabdominal multi-organ, and liver retransplantations), 180 consecutive LT were enrolled. T-cell characterization was assessed within 48 hours before LT (immunoSEQ Assay, Adaptive Biotechnologies, Seattle, WA). Sepsis-2 and Sepsis-3 cases, defined by presence of acute infection plus ≥2 SIRS criteria, or clinical documentation of sepsis, were identified by chart review. Receiver-operating characteristic analyses determined optimal T-cell repertoire clonality for predicting post-LT sepsis. Kaplan-Meier and Cox proportional hazard modeling assessed outcome-associated prognostic variables. Results: Patients with baseline T-cell repertoire clonality ≥0.072 were 3.82 (1.25, 11.40; P = 0.02), and 2.40 (1.00, 5.75; P = 0.049) times more likely to develop sepsis 3 and 12 months post-LT, respectively, when compared to recipients with lower ( 〈 0.072) clonality. T-cell repertoire clonality was the only predictor of sepsis 3 months post-LT in multivariate analysis (C-Statistic, 0.75). Adequate treatment resulted in equivalent survival rates between both groups: (93.4% vs 96.2%, respectively, P = 0.41) at 12 months post-LT. Conclusions: T-cell repertoire clonality is a novel biomarker predictor of sepsis before development of clinical symptoms. Early sepsis monitoring and management may reduce post-LT mortality. These findings have implications for developing sepsis-prevention protocols in transplantation and potentially other populations.
Type of Medium:
Online Resource
ISSN:
0003-4932
,
1528-1140
DOI:
10.1097/SLA.0000000000004998
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2021
detail.hit.zdb_id:
2641023-0
detail.hit.zdb_id:
2002200-1
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