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| Validation and performance of three scoring systems for predicting primary non-function and early allograft failure after liver transplantation |
| Yu Nie a , # , Jin-Bo Huang b , c , d , # , Shu-Jiao He a , Hua-Di Chen b , c , d , Jun-Jun Jia e , Jing-Jing Li b , c , d , Xiao-Shun He b , c , d , Qiang Zhao b , c , d , ∗ |
a General Surgery Center, Department of Hepatobiliary Surgery II, Research Center for Artificial Organ and Tissue Engineering, Guangzhou Clinical Research and Transformation Center for Artificial Liver, Institute of Regenerative Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
b Organ Transplant Center, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
c Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou 510080, China
d Guangdong Provincial International Cooperation Base of Science and Technology, Guangzhou 510080, China
e Division of Hepatobiliary Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
∗Corresponding author at: Organ Transplant Center, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
E-mail address: zhaoq37@mail.sysu.edu.cn (Q. Zhao).
# Contributed equally. |
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Abstract Background: Primary non-function (PNF) and early allograft failure (EAF) after liver transplantation (LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recip- ients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function (MEAF), PNF score by King’s College (King-PNF) and Balance-and-Risk-Lactate (BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF.
Methods: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic (ROC) and the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses.
Results: Of all 720 patients, 28 (3.9%) developed PNF and 67 (9.3%) developed EAF in 3 months. The overall early allograft dysfunction (EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0 (3.5–6.3), -2.1 (-2.6 to -1.2), and 5.0 (2.0–11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves (AUCs) of 0.872 and 0.891, superior to BAR-Lac (AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF.
Conclusions: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.
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2024, Vol. 23 
