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…