Primary nonfunction following liver transplantation: Learning of graft metabolites and building a predictive model

Dear Editor, Primary nonfunction (PNF) is defined as the need for emergent re-transplant when a graft never presented any evidence of initial function following liver transplantation (LT) after excluding other causes such as acute cellular rejection or hepatic artery thrombosis.1 The cause of PNF is believed to be associated with graft quality, but the mechanism is largely unknown.1–3 With the increasing demand for extended criteria donors due to organ shortage, the precise assessment of graft quality, prediction, and early prevention of PNF become a major challenge.2,4 This letter was written to present the first pilot-scale study, which determined the grafts’ metabolic profiling of developing PNF and constructed an integrated graft metabolites and clinical parameters-based PNF (GMCP-PNF) predictive model. TABLE 1 The potential risk factors of primary nonfunction Univariate Multivariatea OR (95%, CI) p-value OR (95%, CI) p-value Quantitative data Donor TB 1.019 (0.997, 1.041) 0.087 Donor AST 1.003 (1.000, 1.007) 0.045 Donor ALT 1.002 (1.000, 1.004) 0.065 Graft weight 1.001 (1.000, 1.003) 0.086 CIT 1.260 (1.100, 1.444) 0.001 GWIT 1.059 (1.025, 1.095) 0.001 Anhepatic time 1.037 (1.018, 1.056) 0.002 MELD score 1.052 (1.006, 1.100) 0.027 Categorical datab Donor TB > 2 ng/ml 4.443 (1.394, 14.16) 0.012 7.488 (1.834, 30.57) 0.005 Donor AST > 120 U/L 4.065 (1.245, 13.27) 0.020 Donor ALT > 40 U/L 3.460 (1.023, 11.70) 0.046 Graft weight > 1.5 kg 3.755 (1.271, 11.09) 0.017 4.448 (1.216, 16.28) 0.024 CIT > 10 h 7.054 (2.321, 21.44) 0.001 10.67 (2.547, 44.66) 0.001 GWIT > 60 min 5.267 (1.716, 16.17) 0.004 6.858 (1.885, 24.95) 0.003 Anhepatic time > 80 min 4.738 (1.552, 14.46) 0.006 MELD score > 25 5.047 (1.386, 18.38) 0.014 Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; CI, confidence interval; CIT, cold ischemia time; GWIT, graft warm ischemia time; MELD, model for end-stage liver diseases; OR, odds ratio; TB, total bilirubin. a Only categorical data showing significance in univariate analysis were entered into multivariate analysis. b Cut-off values were selected according to the ROC curve considered both sensitivity and specificity. [...]we performed principal component analysis and extracted the eight metabolites as a virtual super-biomarker (Figure 3D), which displayed an area under curve (AUC) of 0.930 in predicting PNF. The model was further tested with the leave-one-out cross-validation (Figure 3F).10 Notably, out of three cases