title: Who is the high-risk recipient? Predicting mortality after lung transplantation using pretransplant risk factors.
abstract: OBJECTIVE
  The purpose of this study was to create a preoperative risk stratification score (RSS) based on pretransplant recipient characteristics that could be used to predict mortality following lung transplantation.
  
  
  METHODS
  United Network for Organ Sharing provided de-identified patient-level data. The study population included 8780 adult recipients (age > 12 years) having lung transplantation from January 1, 1999, to December 31, 2006. Multivariate logistic regression (backward, P > .10) was performed. Using the odds ratio for each identified variable, an RSS was devised. The RSS included only pretransplant recipient variables and excluded donor variables.
  
  
  RESULTS
  The strongest negative predictors of 1-year survival included extracorporeal membrane oxygenation, decreased estimated glomerular filtration rate, total bilirubin >2.0 mg/dL, recipient age, hospitalization at time of transplant, O(2) dependence, cardiac index <2, steroid dependence, donor:recipient weight ratio <0.7, all non-cystic fibrosis/chronic obstructive pulmonary disease etiologies, and female donor-to-male recipient. Threshold analysis identified 4 discrete groups: low risk, moderate, elevated risk, and high risk. The 1-year actuarial survival was 80.4% for the entire group, compared with 56.8% in the high-risk group (RSS > 7.2, n = 490; 6%).
  
  
  CONCLUSIONS
  Pretransplant recipient variables significantly influence both early and late survival following lung transplantation. Some patients face a higher than average risk of mortality during their first year posttransplant, which challenges the goals of equitable organ allocation. RSS may improve organ allocation strategies by avoiding the potential negative impact of performing transplantation in extremely high-risk candidates.
authors.affiliation: Division of Cardiothoracic Surgery, Department of Surgery, College of Physicians and Surgeons, Columbia University, New York, NY, USA.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Russo
authors.forename: Ryan R
authors.initials: RR
authors.lastname: Davies
authors.forename: Kimberly N
authors.initials: KN
authors.lastname: Hong
authors.forename: Alexander
authors.initials: A
authors.lastname: Iribarne
authors.forename: Steven
authors.initials: S
authors.lastname: Kawut
authors.forename: Matthew
authors.initials: M
authors.lastname: Bacchetta
authors.forename: Frank
authors.initials: F
authors.lastname: D'Ovidio
authors.forename: Selim
authors.initials: S
authors.lastname: Arcasoy
authors.forename: Joshua R
authors.initials: JR
authors.lastname: Sonett
mesh_terms: D000367:Age Factors; D001663:Bilirubin / Q000032:analysis; D016009:Chi-Square Distribution; D015199:Extracorporeal Membrane Oxygenation; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D016015:Logistic Models; D016040:Lung Transplantation / Q000401:mortality*; D008297:Male; D018579:Patient Selection; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012737:Sex Factors; D015996:Survival Rate
chemical_list: D001663:Bilirubin