Literature DB >> 18945283

Survival outcomes following liver transplantation (SOFT) score: a novel method to predict patient survival following liver transplantation.

A Rana1, M A Hardy, K J Halazun, D C Woodland, L E Ratner, B Samstein, J V Guarrera, R S Brown, J C Emond.   

Abstract

It is critical to balance waitlist mortality against posttransplant mortality. Our objective was to devise a scoring system that predicts recipient survival at 3 months following liver transplantation to complement MELD-predicted waitlist mortality. Univariate and multivariate analysis on 21,673 liver transplant recipients identified independent recipient and donor risk factors for posttransplant mortality. A retrospective analysis conducted on 30,321 waitlisted candidates reevaluated the predictive ability of the Model for End-Stage Liver Disease (MELD) score. We identified 13 recipient factors, 4 donor factors and 2 operative factors (warm and cold ischemia) as significant predictors of recipient mortality following liver transplantation at 3 months. The Survival Outcomes Following Liver Transplant (SOFT) Score utilized 18 risk factors (excluding warm ischemia) to successfully predict 3-month recipient survival following liver transplantation. This analysis represents a study of waitlisted candidates and transplant recipients of liver allografts after the MELD score was implemented. Unlike MELD, the SOFT score can accurately predict 3-month survival following liver transplantation. The most significant risk factors were previous transplantation and life support pretransplant. The SOFT score can help clinicians determine in real time which candidates should be transplanted with which allografts. Combined with MELD, SOFT can better quantify survival benefit for individual transplant procedures.

Entities:  

Mesh:

Year:  2008        PMID: 18945283     DOI: 10.1111/j.1600-6143.2008.02400.x

Source DB:  PubMed          Journal:  Am J Transplant        ISSN: 1600-6135            Impact factor:   8.086


  88 in total

1.  Left-sided grafts for living-donor liver transplantation and split grafts for deceased-donor liver transplantation: their impact on long-term survival.

Authors:  Tomohide Hori; Shinji Uemoto; Lindsay B Gardner; Lena Sibulesky; Yasuhiro Ogura; Justin H Nguyen
Journal:  Clin Res Hepatol Gastroenterol       Date:  2011-09-28       Impact factor: 2.947

Review 2.  Development of organ-specific donor risk indices.

Authors:  Sanjeev K Akkina; Sumeet K Asrani; Yi Peng; Peter Stock; W Ray Kim; Ajay K Israni
Journal:  Liver Transpl       Date:  2012-04       Impact factor: 5.799

3.  Declining predictive performance of the MELD: Cause for concern or reflection of changes in clinical practice?

Authors:  Nadim Mahmud; David S Goldberg
Journal:  Am J Transplant       Date:  2019-10-23       Impact factor: 8.086

Review 4.  How important is donor age in liver transplantation?

Authors:  Alberto Lué; Estela Solanas; Pedro Baptista; Sara Lorente; Juan J Araiz; Agustin Garcia-Gil; M Trinidad Serrano
Journal:  World J Gastroenterol       Date:  2016-06-07       Impact factor: 5.742

Review 5.  Use of Hepatitis C-Positive Liver Grafts in Hepatitis C-Negative Recipients.

Authors:  Akshay Shetty; Adam Buch; Sammy Saab
Journal:  Dig Dis Sci       Date:  2018-12-17       Impact factor: 3.199

6.  Use of BAR score as predictor of short and long-term survival of liver transplantation patients.

Authors:  Chung-Mau Lo
Journal:  Hepatol Int       Date:  2014-10-31       Impact factor: 6.047

7.  Hepatobiliary quiz-9 (2014).

Authors:  Swastik Agrawal; Radha K Dhiman
Journal:  J Clin Exp Hepatol       Date:  2014-03

8.  Portal vein thrombosis and liver transplant survival benefit.

Authors:  Michael J Englesbe; Douglas E Schaubel; Shijie Cai; Mary K Guidinger; Robert M Merion
Journal:  Liver Transpl       Date:  2010-08       Impact factor: 5.799

9.  Outcomes following liver transplantation in intensive care unit patients.

Authors:  Lena Sibulesky; Michael G Heckman; C Burcin Taner; Juan M Canabal; Nancy N Diehl; Dana K Perry; Darren L Willingham; Surakit Pungpapong; Barry G Rosser; David J Kramer; Justin H Nguyen
Journal:  World J Hepatol       Date:  2013-01-27

10.  Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.

Authors:  Lawrence Lau; Yamuna Kankanige; Benjamin Rubinstein; Robert Jones; Christopher Christophi; Vijayaragavan Muralidharan; James Bailey
Journal:  Transplantation       Date:  2017-04       Impact factor: 4.939

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