Biomarkers and predictive models of early allograft dysfunction in liver transplantation – A systematic review of the literature, meta‐analysis, and expert panel recommendations
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.112 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".