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Articles tagged "Prediction models"

  • 2022 American Transplant Congress

    Distinct Phenotypes of Kidney Transplant Recipients Aged 80 Years or Older in the United States by Machine Learning Consensus Clustering

    S. A. Mao1, C. Thongprayoon2, M. A. Mao3, C. C. Jadlowiec4, N. Leeaphorn5, M. Cooper6, W. Cheungpasitporn2

    1Transplant Surgery, Mayo Clinic, Jacksonville, FL, 2Nephrology and Hypertension, Mayo Clinic, Rochester, MN, 3Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL, 4Transplant Surgery, Mayo Clinic, Phoenix, AZ, 5Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke's Health System, Kansas City, MO, 6MedStar Georgetown Transplant Institute, Georgetown University School of Medicine, Washington, DC

    *Purpose: Our study aimed to cluster very elderly kidney transplant recipients aged 80 years and above using an unsupervised machine learning approach.*Methods: We performed consensus…
  • 2022 American Transplant Congress

    Meld 3.0 for Liver Allocation: Results From the Liver Simulated Allocation Model

    A. Kwong1, T. Weaver2, D. Schladt2, A. Wey2, K. Audette2, S. Biggins3, J. Snyder2, A. Israni2, J. Lake4, W. Kim1

    1Stanford University, Stanford, CA, 2Hennepin Healthcare Research Institute, Minneapolis, MN, 3University of Washington, Seattle, WA, 4University of Minnesota, Minneapolis, MN

    *Purpose: Priority on the US liver transplant waitlist is determined by the model for end-stage liver disease (MELD), a score composed of serum bilirubin, creatinine,…
  • 2022 American Transplant Congress

    Evaluating the Performance and External Validity of Machine Learning-Based Prediction Models in Liver Transplantation: An International Study

    T. Ivanics1, D. So2, M. P. Claasen3, D. Wallace4, M. Patel5, A. Gravely6, K. Walker7, T. Cowling7, L. Erdman8, G. Sapisochin3

    1University of Toronto - University Health Network, Toronto, ON, Canada, 2The Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada, 3Multi-organ transplant program, University Health Network, Toronto, ON, Canada, 4Department of Health Services Research and policy, London School of Hygiene and Tropical Medicine, London, United Kingdom, 5Division of Surgical Transplantation, University of Texas Southwestern Medical Center, Dallas, TX, 6Multi-organ transplant program, University of Toronto - University Health Network, Toronto, ON, Canada, 7Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom, 8The Centre of Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada

    *Purpose: National liver transplant (LT) registries are curated in many countries. We compared data from three national registries and developed machine learning algorithm (MLA)-based models…
  • 2022 American Transplant Congress

    Computer vs Human-Based Prediction and Stratification of the Risk of Long-Term Kidney Allograft Failure

    G. Divard1, M. Raynaud1, V. Tataputii2, B. Abdalla3, C. Legendre1, C. Lefaucheur1, O. Aubert1, A. Loupy1

    1Paris Transplant Group, Paris, France, 2NYU Langone Health, New York, NY, 3UCLA, Los Angeles, CA

    *Purpose: Clinical decision-making process after transplantation is mainly driven by patient individual risk of allograft failure prediction assessed by physicians. However, this task remains difficult…
  • 2022 American Transplant Congress

    A Hybrid Model Combining Survival Analysis, Knapsack Optimization and Supervised Learning to Extrapolate the Evolution of Kidney Transplantation Patients from Donors with Expanded Criteria After Controlled Circulatory Death

    F. Santos Arteaga1, D. Di Caprio2, O. Bestard3, N. Montero4, F. Moreso3, M. Crespo5, C. Facundo6, J. Reinoso-Moreno7, D. Cucchiari7, B. Bayes7, E. Poch7, J. M. Campistol7, F. Oppenheimer7, F. Diekmann7, I. Revuelta7

    1Faculty of Economics and Management, Universidad Complutense de Madrid, Madrid, Spain, 2Department of Economics and Management, University of Trento, Trento, Italy, 3Department of Nephrology and Kidney Transplant, Hospital Universitari Vall Hebrón, Barcelona, Spain, 4Department of Nephrology and Kidney Transplant, Hospital Universitari de Bellvitge, Barcelona, Spain, 5Department of Nephrology and Kidney Transplant, Hospital del Mar, Barcelona, Spain, 6Department of Nephrology and Kidney Transplant, Fundació Puigvert, Barcelona, Spain, 7Department of Nephrology and Kidney Transplant, Hospital Clinic, Barcelona, Spain

    *Purpose: Kidney transplantation (KT) with expanded criteria donors (ECD) after controlled circulatory death (cDCD) in high-risk patients is being debated. We categorize patients via a…
  • 2021 American Transplant Congress

    Poor Reliability of Karnofsky Performance Score in Kidney Transplant Candidates

    M. R. Stedman, D. J. Watford, G. M. Chertow, J. C. Tan

    Medicine, Stanford University, Palo Alto, CA

    *Purpose: The Karnofsky Performance Status (KPS) Scale has been used as a proxy for frailty and as a predictor of transplant outcomes, however reliability of…
  • 2021 American Transplant Congress

    Creatinine Reduction Ratio at 2 Postoperative Day as a Predicting Factor of Long-Term Outcomes After Living Donor Kidney Transplantation

    Y. Kinoshita, T. Shinzato, T. Shimizu, D. Iwami

    Division of Renal Surgery and Transplantation, Department of Urology, Jichi Medical University Hospital, Shimotsuke, Tochigi, Japan

    *Purpose: Creatinine reduction ratio from 1 to 2 postoperative days (CRR2) under 30% has been defined as a slow graft function (SGF) and used to…
  • 2021 American Transplant Congress

    Artificial Neural Network Application for MELDNa Prediction

    L. Pruinelli1, M. Nguyen1, S. Olson1, J. Zhou1, J. Schold2, T. Pruett1, S. Ma1, G. Simon1

    1University of Minnesota, Minneapolis, MN, 2Cleveland Clinic Foundation, Cleveland, OH

    *Purpose: The adoption of MELDNa decreased 90-days mortality on patients waiting for liver transplant (LT); however, there are no tools available to predict MELDNa trajectories…
  • 2021 American Transplant Congress

    Incidence and Risk Factors for Nonmelanoma Skin Cancer in Lung Transplant Recipients

    G. Holdren1, E. Lushin1, M. Duncan2, C. Hage2

    1Pharmacy, Indiana University Health, Indianapolis, IN, 2Pulmonary Critical Care, Indiana University Health, Indianapolis, IN

    *Purpose: The purpose of this study is to identify the incidence of nonmelanoma skin cancer (NMSC) post lung transplantation and to examine the relationship between…
  • 2021 American Transplant Congress

    Transplant Data Platform – An Augmented Clinical Intelligence Framework

    C. Focht, W. Tian, J. Zeng, N. Dzebisashvili, S. Ghosh

    CareDx, Brisbane, CA

    *Purpose: UNOS, SRTR, USRDS registries are rich in patient baseline data. However, all suffer from data attrition as patients move further out post-transplant or between…
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