Development and validation of a machine-learning model to reduce futile procurements in donations after circulatory death in liver transplantation in the USA: a multicentre study
Authors:
- Rintaro YanagawaRYRintaro Yanagawa
- Kazuhiro IwadohKIKazuhiro Iwadoh
- Toshihiro NakayamaTNToshihiro Nakayama
- Daniel J FirlDFDaniel J Firl
- Chase J WehrleCWChase J Wehrle
- Yuki BekkiYBYuki Bekki
- Jiro KusakabeJKJiro Kusakabe
- Yuzuru SambommatsuYSYuzuru Sambommatsu
- Yutaka EndoYutaka Endo
- Kliment K BozhilovKBKliment K Bozhilov
Open Access
- 14 November 2025
- journal article
- Published by Elsevier in The Lancet Digital Health
- Vol. 7 (10) , 100918
Abstract
No abstract availableKeywords
This publication has 21 references indexed in Scilit:
- Disparities in Outcomes of Liver-Related Waiting List for Transplant—Is There an End in Sight?JAMA Network Open, 2024
- American Society of Transplant Surgeons recommendations on best practices in donation after circulatory death organ procurementAmerican Journal of Transplantation, 2023
- Liver Transplantation Today: Where We Are Now and Where We Are GoingLiver Transplantation, 2018
- Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional ExpectationJournal of Computational and Graphical Statistics, 2015
- Donation After Circulatory DeathTransplantation, 2014
- Prediction of potential for organ donation after cardiac death in patients in neurocritical state: a prospective observational studyThe Lancet Neurology, 2012
- External validation of a prognostic model predicting time of death after withdrawal of life support in neurocritical patients*Critical Care Medicine, 2012
- Clinical cognition and diagnostic error: applications of a dual process model of reasoningAdvances in Health Sciences Education, 2009
- Ischemic cholangiopathy following liver transplantation from donation after cardiac death donorsLiver Transplantation, 2008
- Sources of Power: How People Make DecisionsLeadership and Management in Engineering, 2001