Validation and performance of three scoring systems for predicting primary non-function and early allograft failure after liver transplantationOA
Validation and performance of three scoring systems for predicting primary non-function and early allograft failure after liver transplantation
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 de-veloped 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 De-cember 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.
Yu Nie;Jin-Bo Huang;Shu-Jiao He;Hua-Di Chen;Jun-Jun Jia;Jing-Jing Li;Xiao-Shun He;Qiang Zhao
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,ChinaOrgan Transplant Center,the First Affiliated Hospital,Sun Yat-sen University,Guangzhou 510080,China||Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology,Guangzhou 510080,China||Guangdong Provincial International Cooperation Base of Science and Technology,Guangzhou 510080,ChinaDivision of Hepatobiliary Pancreatic Surgery,the First Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310003,China
Primary non-functionEarly allograft failureRisk predicting modelLiver transplantation
《国际肝胆胰疾病杂志(英文版)》 2024 (005)
463-471 / 9
This study was supported by grants from the National Nat-ural Science Foundation of China(81570587 and 81700557),the Guangdong Provincial Key Laboratory Construction Projection on Organ Donation and Transplant Immunology(2013A061401007 and 2017B030314018),Guangdong Provincial Natural Science Funds for Major Basic Science Culture Project(2015A030308010),Science and Technology Program of Guangzhou(201704020150),the Natural Science Foundations of Guangdong province(2016A030310141 and 2020A1515010091),Young Teachers Training Project of Sun Yat-sen University(K0401068)and the Guangdong Science and Technology Innovation Strategy(pdjh2022b0010 and pdjh2023a0002).
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