title: Bayesian modeling of pretransplant variables accurately predicts kidney graft survival.
abstract: BACKGROUND
  Machine learning can enable the development of predictive models that incorporate multiple variables for a systems approach to organ allocation. We explored the principle of Bayesian Belief Network (BBN) to determine whether a predictive model of graft survival can be derived using pretransplant variables. Our hypothesis was that pretransplant donor and recipient variables, when considered together as a network, add incremental value to the classification of graft survival.
  
  
  METHODS
  We performed a retrospective analysis of 5,144 randomly selected patients (age ≥18, deceased donor kidney only, first-time recipients) from the United States Renal Data System database between 2000 and 2001. Using this dataset, we developed a machine-learned BBN that functions as a pretransplant organ-matching tool.
  
  
  RESULTS
  A network of 48 clinical variables was constructed and externally validated using an additional 2,204 patients of matching demographic characteristics. This model was able to predict graft failure within the first year or within 3 years (sensitivity 40%; specificity 80%; area under the curve, AUC, 0.63). Recipient BMI, gender, race, and donor age were amongst the pretransplant variables with strongest association to outcome. A 10-fold internal cross-validation showed similar results for 1-year (sensitivity 24%; specificity 80%; AUC 0.59) and 3-year (sensitivity 31%; specificity 80%; AUC 0.60) graft failure.
  
  
  CONCLUSIONS
  We found recipient BMI, gender, race, and donor age to be influential predictors of outcome, while wait time and human leukocyte antigen matching were much less associated with outcome. BBN enabled us to examine variables from a large database to develop a robust predictive model.
authors.affiliation: Regenerative Medicine Department, Naval Medical Research Center, Silver Spring, MD, USA.
authors.forename: Trevor S
authors.initials: TS
authors.lastname: Brown
authors.forename: Eric A
authors.initials: EA
authors.lastname: Elster
authors.forename: Kristin
authors.initials: K
authors.lastname: Stevens
authors.forename: J Christopher
authors.initials: JC
authors.lastname: Graybill
authors.forename: Suzanne
authors.initials: S
authors.lastname: Gillern
authors.forename: Samuel
authors.initials: S
authors.lastname: Phinney
authors.forename: Moro O
authors.initials: MO
authors.lastname: Salifu
authors.forename: Rahul M
authors.initials: RM
authors.lastname: Jindal
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D001185:Artificial Intelligence; D001499:Bayes Theorem; D015992:Body Mass Index; D005260:Female; D005544:Forecasting / Q000379:methods*; D006085:Graft Survival; D006801:Humans; D007676:Kidney Failure, Chronic / Q000601:surgery*; D016030:Kidney Transplantation; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D057234:Preoperative Period; D044469:Racial Groups; D012737:Sex Factors; D014481:United States; D055815:Young Adult