Literature DB >> 32892294

The role of the comprehensive complication index for the prediction of survival after liver transplantation.

Quirino Lai1, Fabio Melandro2, Greg Nowak3, Daniele Nicolini4, Samuele Iesari5,6, Elisa Fasolo7, Gianluca Mennini2, Antonio Romano3, Federico Mocchegiani4, Kevin Ackenine5, Marina Polacco7, Laura Marinelli4, Olga Ciccarelli5, Giacomo Zanus7, Marco Vivarelli4, Umberto Cillo7, Massimo Rossi2, Bo-Göran Ericzon3, Jan Lerut5.   

Abstract

In the last years, several scoring systems based on pre- and post-transplant parameters have been developed to predict early post-LT graft function. However, some of them showed poor diagnostic abilities. This study aims to evaluate the role of the comprehensive complication index (CCI) as a useful scoring system for accurately predicting 90-day and 1-year graft loss after liver transplantation. A training set (n = 1262) and a validation set (n = 520) were obtained. The study was registered at https://www.ClinicalTrials.gov (ID: NCT03723317). CCI exhibited the best diagnostic performance for 90 days in the training (AUC = 0.94; p < 0.001) and Validation Sets (AUC = 0.77; p < 0.001) when compared to the BAR, D-MELD, MELD, and EAD scores. The cut-off value of 47.3 (third quartile) showed a diagnostic odds ratio of 48.3 and 7.0 in the two sets, respectively. As for 1-year graft loss, CCI showed good performances in the training (AUC = 0.88; p < 0.001) and validation sets (AUC = 0.75; p < 0.001). The threshold of 47.3 showed a diagnostic odds ratio of 21.0 and 5.4 in the two sets, respectively. All the other tested scores always showed AUCs < 0.70 in both the sets. CCI showed a good stratification ability in terms of graft loss rates in both the sets (log-rank p < 0.001). In the patients exceeding the CCI ninth decile, 1-year graft survival rates were only 0.7% and 23.1% in training and validation sets, respectively. CCI shows a very good diagnostic power for 90-day and 1-year graft loss in different sets of patients, indicating better accuracy with respect to other pre- and post-LT scores.Clinical Trial Notification: NCT03723317.

Entities:  

Keywords:  Allograft dysfunction; Graft survival; MELD; Retransplantation; Survival prediction

Mesh:

Year:  2020        PMID: 32892294      PMCID: PMC7889667          DOI: 10.1007/s13304-020-00878-4

Source DB:  PubMed          Journal:  Updates Surg        ISSN: 2038-131X


  32 in total

Review 1.  Multivariate regression: the pitfalls of automated variable selection.

Authors:  Kristin L Sainani
Journal:  PM R       Date:  2013-09       Impact factor: 2.298

2.  Application of the BAR score as a predictor of short- and long-term survival in liver transplantation patients.

Authors:  Ivan Dias de Campos Junior; Raquel Silveira Bello Stucchi; Elisabete Yoko Udo; Ilka de Fátima Santana Ferreira Boin
Journal:  Hepatol Int       Date:  2014-08-09       Impact factor: 6.047

3.  Multicentric evaluation of model for end-stage liver disease-based allocation and survival after liver transplantation in Germany--limitations of the 'sickest first'-concept.

Authors:  Tobias J Weismüller; Panagiotis Fikatas; Jan Schmidt; Ana P Barreiros; Gerd Otto; Susanne Beckebaum; Andreas Paul; Markus N Scherer; Hartmut H Schmidt; Hans J Schlitt; Peter Neuhaus; Jürgen Klempnauer; Johann Pratschke; Michael P Manns; Christian P Strassburg
Journal:  Transpl Int       Date:  2010-09-03       Impact factor: 3.782

Review 4.  The Comprehensive Complication Index is Related to Frailty in Elderly Surgical Patients.

Authors:  Manuel Artiles-Armas; Cristina Roque-Castellano; Alicia Conde-Martel; Joaquín Marchena-Gómez
Journal:  J Surg Res       Date:  2019-07-10       Impact factor: 2.192

Review 5.  A model to predict survival in patients with end-stage liver disease.

Authors:  P S Kamath; R H Wiesner; M Malinchoc; W Kremers; T M Therneau; C L Kosberg; G D'Amico; E R Dickson; W R Kim
Journal:  Hepatology       Date:  2001-02       Impact factor: 17.425

6.  Model for End-Stage Liver Disease (MELD) score does not predict outcomes of hepatitis B-induced acute-on-chronic liver failure in transplant recipients.

Authors:  B-W Duan; S-C Lu; J-S Wu; Q-L Guo; D-B Zeng; T Jiang; D-G Kong; J Ding
Journal:  Transplant Proc       Date:  2014-12       Impact factor: 1.066

7.  The new liver allocation system: moving toward evidence-based transplantation policy.

Authors:  Richard B Freeman; Russell H Wiesner; Ann Harper; Sue V McDiarmid; Jack Lake; Erick Edwards; Robert Merion; Robert Wolfe; Jeremiah Turcotte; Lewis Teperman
Journal:  Liver Transpl       Date:  2002-09       Impact factor: 5.799

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

Authors:  A Rana; M A Hardy; K J Halazun; D C Woodland; L E Ratner; B Samstein; J V Guarrera; R S Brown; J C Emond
Journal:  Am J Transplant       Date:  2008-09-25       Impact factor: 8.086

9.  D-MELD, a simple predictor of post liver transplant mortality for optimization of donor/recipient matching.

Authors:  J B Halldorson; R Bakthavatsalam; O Fix; J D Reyes; J D Perkins
Journal:  Am J Transplant       Date:  2008-12-15       Impact factor: 8.086

10.  Comprehensive Complication Index Predicts Cancer-Specific Survival of Patients with Postoperative Complications after Curative Resection of Gastric Cancer.

Authors:  Ru-Hong Tu; Jian-Xian Lin; Ping Li; Jian-Wei Xie; Jia-Bin Wang; Jun Lu; Qi-Yue Chen; Long-Long Cao; Mi Lin; Chao-Hui Zheng; Chang-Ming Huang
Journal:  Gastroenterol Res Pract       Date:  2018-11-19       Impact factor: 2.260

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  1 in total

1.  CONUT Score Predicts Early Morbidity After Liver Transplantation: A Collaborative Study.

Authors:  Gabriele Spoletini; Flaminia Ferri; Alberto Mauro; Gianluca Mennini; Giuseppe Bianco; Vincenzo Cardinale; Salvatore Agnes; Massimo Rossi; Alfonso Wolfango Avolio; Quirino Lai
Journal:  Front Nutr       Date:  2022-01-07
  1 in total

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