Articles tagged "Prediction models"

2022 American Transplant Congress
Prediction Model of Post-Donation Renal Function Using Dynamic Kidney CT Volumetry in Living Donor
S. Lim¹, J. Kwon², Y. Ko¹, H. Kwon¹, J. Jung¹, H. Kwon¹, Y. Kim¹, J. Park², K. Lee², S. Shin¹
¹Asan Medical Center, Seoul, Songpa, Korea, Republic of, ²Samsung Medical Center, Seoul, Gangnam, Korea, Republic of
*Purpose: The risk of renal failure after live kidney donation can be predicted based on various clinical information. We tried introduce a model to predict…
2022 American Transplant Congress
Validation of a Prediction System for Risk of Allograft Loss (iBOX) in Pediatric Kidney Transplant Recipients
J. Hogan¹, G. Divard², R. Garro³, O. Boyer⁴, M. Seifert⁵, J. Smith⁶, B. Toenshoff⁷, K. Twombley⁸, B. Warady⁹, P. Weng¹⁰, R. Zaar¹¹, R. Patzer¹², A. Loupy²
¹Pediatric Nephrology, Robert Debré Hospital, APHP, Paris, France, ²Paris Transplant Group, INSERM, Paris, France, ³Pediatric Nephrology, Children Healthcare of Atlanta, Atlanta, GA, ⁴Pediatric Nephrology, Necker Hospital, Paris, France, ⁵UAB School of Medicine, Birmingham, AL, ⁶Pediatric Nephrology, Seattle Children, Seattle, WA, ⁷University Children's Hospital, Heidelberg, Germany, ⁸Medical University of South Carolina, Charleston, SC, ⁹Childrens Mercy Hospital, Kansas City, MO, ¹⁰Pediatric Nephrology, David Geffen School of Medicine at UCLA, Los Angeles, CA, ¹¹Pediatric Nephrology, Le Bonheur Children's Hospital, Memphis, TN, ¹²Emory Transplant Center, Emory University, Atlanta, GA
*Purpose: Kidney allograft loss is a common cause of end-stage renal disease but accurate prediction models of kidney allograft loss are lacking in children. The…
2022 American Transplant Congress
Multidimensional Prognostication Tool for Kidney Transplant Patient Survival: The Mortality Mbox
C. Debiais-Deschamps¹, O. Aubert¹, D. Yoo¹, G. Divard¹, C. Lefaucheur², C. Legendre³, A. Loupy¹
¹Université de Paris, PARCC, INSERM, Paris, France, ²Service de Transplantation Rénale, AP-HP, Hôpital Saint-Louis, Paris, France, ³Service de Transplantation Rénale Adulte, AP-HP, Hopital Necker, Paris, France
*Purpose: Predicting kidney transplant patient mortality has been hampered by registry-based studies and low level phenotyped cohorts without specific design towards mortality prediction. This represents…
2022 American Transplant Congress
Prediction of Waitlist Mortality in Liver Transplant Candidates: Do Time-Varying Models Help?
A. Kwong¹, W. Kim¹, D. Schladt², A. Wey², J. Snyder², A. Israni², J. Lake³
¹Stanford University, Stanford, CA, ²Hennepin Healthcare Research Institute, Minneapolis, MN, ³University of Minnesota, Minneapolis, MN
*Purpose: In the United States, priority on the liver transplant waitlist is determined by the model for end-stage liver disease (MELD)-Na, a score composed of…
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…
2022 American Transplant Congress
Dynamic Risk Prediction of Kidney Graft Failure After Deceased Donor Transplant
D. G. Hu¹, H. Thiessen Philbrook¹, S. Mohan², I. Hall³, M. Doshi⁴, K. F. Kerr⁵, C. R. Parikh¹
¹Johns Hopkins University, Baltimore, MD, ²Columbia University Medical Center, New York, NY, ³University of Utah, Salt Lake City, UT, ⁴University of Michigan, Ann Arbor, MI, ⁵University of Washington, Seattle, WA
*Purpose: Identifying kidney transplant recipients at risk of graft failure allows for early intervention in clinical care. We developed a dynamic risk prediction model based…