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…
2021 American Transplant Congress
Predictive Index for Liver Retransplantation
C. Christmann, G. Handing, M. McDonald, A. Anand, S. Keeling, N. T. Galván, R. Cotton, C. O'Mahony, J. Goss, A. Rana
Baylor College of Medicine, Houston, TX
*Purpose: Given the increase in demand for donor livers, it has become more critical than ever to predict mortality in order to identify the best…
2021 American Transplant Congress
Days of Delayed Graft Function and Its Impact on Graft Outcomes in Deceased Donor Kidney Transplant
P. Budhiraja¹, R. J. Butterfield², S. S. Misra¹, K. S. Reddy³, L. Kodali¹, H. A. Khamash¹, S. Nair¹, G. K. Mour¹, H. A. Chakkera¹, C. C. Jadlowiec³, H. Janna¹, R. L. Heilman¹
¹Department of Medicine, Mayo Clinic Arizona, Phoenix, AZ, ²Department of Statistics, Mayo Clinic Arizona, Phoenix, AZ, ³Department of Surgery, Mayo Clinic Arizona, Phoenix, AZ
*Purpose: There has been controversy regarding impact of delayed graft function (DGF) on risk of acute rejection and graft survival. This is likely due to…
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…