Articles tagged "Prediction models"

2022 American Transplant Congress
Six-Month Outcomes Among Lung Transplant Patients with COVID-19
L. D. Mahan, J. Rose, M. Mohanka, A. Banga
UT Southwestern Medical Center, Dallas, TX
*Purpose: There is limited data on the outcomes beyond the acute illness among lung transplant (LT) patients with Coronavirus disease 2019 (COVID-19). The current study…
2022 American Transplant Congress
Tracking of Donor Reactive T Cell Clones Can Be Used to Diagnose or Even Predict Kidney Transplant Rejection
J. M. Mathew¹, Y. Sambandam¹, M. Kandpal¹, J. He¹, G. Perottino¹, B. Banbury², P. Fields², L. Gallon¹, J. R. Leventhal¹
¹Northwestern University, Chicago, IL, ²Adaptive Biotechnologies, Seattle, WA
*Purpose: Transplant (Tx) rejection currently requires an invasive biopsy for diagnosis. We have tested if serial quantitation of donor reactive T cell clones (DRTC) can…
2022 American Transplant Congress
Mortality in Solid Organ Transplant Recipients with a Pretransplant Cancer Diagnosis
A. Hart¹, R. Pfeiffer², B. Morawski³, C. Lynch⁴, Y. Zeng⁵, K. Pawlish⁶, K. Yu⁷, D. Hurley⁸, E. Engels⁷
¹Hennepin Healthcare, University of Minnesota, Minneapolis, MN, ²Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, ³Cancer Registry of Idaho, Boise, ID, ⁴Epidemiology, University of Iowa, Iowa City, IA, ⁵North Dakota Statewide Cancer Registry, Grand Forks, ND, ⁶New Jersey State Cancer Registry, Trenton, NJ, ⁷Infections and Immunoepidemiology Branch, National Cancer Institute, Bethesda, MD, ⁸University of South Carolina, Columbia, SC
*Purpose: Transplant-associated immunosuppression may cause cancer recurrence in patients with a pretransplant cancer, but little is known about outcomes among these recipients.*Methods: We used data…
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…

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