Biomarkers and predictive models of early allograft dysfunction in liver transplantation – A systematic review of the literature, meta‐analysis, and expert panel recommendations. Issue 10 (9th December 2022)

• Record Type: Journal Article
• Authors: Liu, Jiang; Martins, Paulo N.; Bhat, Mamatha; Pang, Li; Yeung, Oscar W.H.; Ng, Kevin T.P.; Spiro, Michael; Raptis, Dimitri Aristotle; Man, Kwan; Mas, Valeria R.
• Is Part Of: Clinical transplantation. Volume 36:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-09
• Subjects: biomarker -- early allograft dysfunction -- lactate -- liver transplantation; Transplantation of organs, tissues, etc -- Periodicals; 617.95
• DOI: 10.1111/ctr.14635
• Languages: English
• ISSNs: 0902-0063

Abstract

Abstract: Background: Prompt identification of early allograft dysfunction (EAD) is critical to reduce morbidity and mortality in liver transplant (LT) recipients. Objectives: Evaluate the evidence supporting biomarkers that can provide diagnostic and predictive value for EAD. Data sources: Ovid MEDLINE, Embase, Scopus, Google Scholar, and Cochrane Central. Methods: Systematic review following PRISMA guidelines and recommendations using the GRADE approach was derived from an international expert panel. Studies that investigated biomarkers or models for predicting EAD in adult LT recipients were included for in‐depth evaluation and meta‐analysis. Olthoff's criteria were used as the standard reference for the diagnostic accuracy evaluation. PROSPERO ID: CRD42021293838 Results: Ten studies were included for the systematic review. Lactate, lactate clearance, uric acid, Factor V, HMGB‐1, CRP to ALB ratio, phosphocholine, total cholesterol, and metabolomic predictive model were identified as potential early EAD predictive biomarkers. The sensitivity ranged between .39 and .92, while the specificity ranged from .63 to .90. Elevated lactate level was most indicative of EAD after adult LT (pooled diagnostic odds ratio of 7.15 (95%CI: 2.38‐21.46)). The quality of evidence (QOE) for lactate as indicator was moderate according to the GRADE approach, whereas the QOE for other biomarkers was very low to low likely as consequence of study design characteristics such as single study, small sample size, and large ranges of sensitivity or specificity. Conclusions: Lactate is an early indicator to predict EAD after LT (Quality of Evidence: Moderate | Grade of Recommendation: Strong). Further multicenter studies and the use of machine perfusion setting should be implemented for validation.