title: Choosing the order of deceased donor and living donor kidney transplantation in pediatric recipients: a Markov decision process model.
abstract: BACKGROUND
  Most pediatric kidney transplant recipients eventually require retransplantation, and the most advantageous timing strategy regarding deceased and living donor transplantation in candidates with only 1 living donor remains unclear.
  
  
  METHODS
  A patient-oriented Markov decision process model was designed to compare, for a given patient with 1 living donor, living-donor-first followed if necessary by deceased donor retransplantation versus deceased-donor-first followed if necessary by living donor (if still able to donate) or deceased donor (if not) retransplantation. Based on Scientific Registry of Transplant Recipients data, the model was designed to account for waitlist, graft, and patient survival, sensitization, increased risk of graft failure seen during late adolescence, and differential deceased donor waiting times based on pediatric priority allocation policies. Based on national cohort data, the model was also designed to account for aging or disease development, leading to ineligibility of the living donor over time.
  
  
  RESULTS
  Given a set of candidate and living donor characteristics, the Markov model provides the expected patient survival over a time horizon of 20 years. For the most highly sensitized patients (panel reactive antibody > 80%), a deceased-donor-first strategy was advantageous, but for all other patients (panel reactive antibody < 80%), a living-donor-first strategy was recommended.
  
  
  CONCLUSIONS
  This Markov model illustrates how patients, families, and providers can be provided information and predictions regarding the most advantageous use of deceased donor versus living donor transplantation for pediatric recipients.
authors.affiliation: 1 Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD. 2 Department of Pediatrics, University of Washington School of Medicine, Seattle, WA. 3 Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD.
authors.forename: Kyle J
authors.initials: KJ
authors.lastname: Van Arendonk
authors.forename: Eric K H
authors.initials: EK
authors.lastname: Chow
authors.forename: Nathan T
authors.initials: NT
authors.lastname: James
authors.forename: Babak J
authors.initials: BJ
authors.lastname: Orandi
authors.forename: Trevor A
authors.initials: TA
authors.lastname: Ellison
authors.forename: Jodi M
authors.initials: JM
authors.lastname: Smith
authors.forename: Paul M
authors.initials: PM
authors.lastname: Colombani
authors.forename: And Dorry L
authors.initials: AD
authors.lastname: Segev
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D002648:Child; D003198:Computer Simulation; D003661:Decision Support Techniques; D046148:Donor Selection; D004608:Eligibility Determination; D005260:Female; D006085:Graft Survival; D006680:HLA Antigens / Q000276:immunology; D006648:Histocompatibility; D006801:Humans; D007518:Isoantibodies / Q000097:blood; D016030:Kidney Transplantation / Q000009:adverse effects / Q000379:methods* / Q000401:mortality; D019520:Living Donors / Q000600:supply & distribution*; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012042:Registries; D012086:Reoperation; D012307:Risk Factors; D013269:Stochastic Processes; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States; D014850:Waiting Lists; D055815:Young Adult
chemical_list: D006680:HLA Antigens; D007518:Isoantibodies