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Oral Presentation

🎗Identification and analysis of factors influencing the futility of liver transplantation in patients with acute-on-chronic liver failure: A nationwide multicenter cohort study

Qinfen Xie
Presenter
Qinfen Xie (CN)

Attending · Hepatobiliary and Pancreatic Surgery · Shulan (Hangzhou) Hospital · HANGZHOU

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Authors

Qinfen Xie1, Yimou Lin2, Jun Fang3,4, Yujian Zheng5, Taishi Fang6, Feiwen Deng7, Leibo Xu8, Qi Ling3,4, Zhiyong Guo2, Shusen Zheng1,3,4

Affiliations

1 Shulan (Hangzhou) Hospital
2 The First Affiliated Hospital, Sun Yat-sen University
3 The First Affiliated Hospital, Zhejiang University School of Medicine
4 NHC Key Laboratory of Combined Multi-organ Transplantation
5 General Hospital of Southern Theatre Command
6 Shenzhen Third People’s Hospital
7 The First People’s Hospital of Foshan
8 Sun Yat-sen Memorial Hospital, Sun Yat-sen University

Abstract

Background: Futility in liver transplantation has been defined as mortality within 3 months or in-hospital mortality. We aimed to establish a predictive model for the futility of liver transplantation in patients with acute-on-chronic liver failure through a nationwide multicenter cohort study, and develop an easy-to-use and widely applicable nomogram for liver transplantation to reduce the rate of futile transplants.
Methods: A screening was conducted on 4028 adult patients who received their first liver transplantation at 7 liver transplantation centers nationwide from January 2015 to December 2021 in China. Patients with HBV-ACLF (hepatitis B virus-related acute-on-chronic liver failure) were included according to the COSSH-ACLF criteria and divided into ACLF-1, ACLF-2, and ACLF-3 groups. Survival and prognosis of different grades were analyzed. Donor graft characteristics, recipient characteristics, and clinical outcomes of patients in the futile liver transplantation group and non-futile liver transplantation group were comparatively analyzed. Risk factors for futile transplantation were studied using univariate and multivariate Cox proportional hazards models, a nomogram affecting the prediction of futile transplantation outcomes was established to guide clinical decision-making.
Results: A total of 642 patients with HBV-ACLF were included. The survival rates at 28 days, 90 days, 1 year, 3 years, and 5 years after liver transplantation were 87.7% (563/642), 80.8% (519/642), 77.3% (496/642), 75.1% (390/519), and 71.0% (206/290), respectively. The higher the ACLF grade, the lower the survival rate. Among the 642 analyzed patients, 123 (19.2%) were classified into the futile transplant group, and 519 (80.8%) into the non-futile transplant group. We conducted a comparative analysis of donor graft characteristics, recipient characteristics, and clinical outcomes between the two groups. Univariate Cox proportional hazards model and multivariate logistic regression analysis were used to evaluate the risk factors for futile transplant. Finally, it was found that DCD donor liver, cold ischemia time, recipient age, fungal infection before transplantation, MELD score, and albumin were independent risk factors for futile transplant.

Figure 1
Futility Score nomogram
A Futility Score nomogram was established to predict the futility after liver transplantation (AUC: 0.69). This model performed better than MELD score (AUC: 0.62), Child score (AUC: 0.53), and MELD-Na score (AUC: 0.61) in predicting futility after liver transplantation.
Figure 2
Futility Score ROC curve

Conclusions: This nationwide multicenter retrospective cohort study confirmed the short-term and long-term survival benefits of liver transplantation in patients with HBV-ACLF. As the ACLF grade increases, the post-transplant survival rate of patients also decreases. The predictive performance of the Futility score nomogram, a HBV-ACLF-specific prognostic scoring model, is superior to existing MELD and MELD-Na scores. The Futility score not only considers recipient factors, but also incorporates donor organ quality factors , which helps in individualized liver transplantation, optimizing organ allocation, and predicting the risk of futile transplantation.