title: Prediction of early death after induction therapy for newly diagnosed acute myeloid leukemia with pretreatment risk scores: a novel paradigm for treatment assignment.
abstract: OBJECTIVE
  Outcome in acute myeloid leukemia (AML) worsens with age, at least in part because of higher treatment-related mortality (TRM) in older patients. Eligibility for intensive AML treatment protocols is therefore typically based on age as the implied principal predictor of TRM, although other health- and disease-related factors modulate this age effect.
  
  
  METHODS
  We empirically defined TRM using estimated weekly hazard rates in 3,365 adults of all ages administered intensive chemotherapy for newly diagnosed AML. We used the area under the receiver operator characteristic curve (AUC) to quantify the relative effects of age and other covariates on TRM in a subset of 2,238 patients. In this approach, an AUC of 1.0 denotes perfect prediction, whereas an AUC of 0.5 is analogous to a coin flip.
  
  
  RESULTS
  Regardless of age, risk of death declined once 4 weeks had elapsed from treatment start, suggesting that patients who die during this time comprise a qualitatively distinct group. Performance status (PS) and age were the most important individual predictors of TRM (AUCs of 0.75 and 0.65, respectively). However, multicomponent models were significantly more accurate in predicting TRM (AUC of 0.83) than PS or age alone. Elimination of age from such multicomponent models only minimally affected their predictive accuracy (AUC of 0.82).
  
  
  CONCLUSIONS
  These data suggest that age is primarily a surrogate for other covariates, which themselves add significantly to predictive accuracy, thus challenging the wisdom of using age as primary or sole basis for assignment of intensive, curative intent treatment in AML.
authors.affiliation: Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, D2-190, Seattle, WA 98109-1024, USA. rwalter@fhcrc.org
authors.forename: Roland B
authors.initials: RB
authors.lastname: Walter
authors.forename: Megan
authors.initials: M
authors.lastname: Othus
authors.forename: Gautam
authors.initials: G
authors.lastname: Borthakur
authors.forename: Farhad
authors.initials: F
authors.lastname: Ravandi
authors.forename: Jorge E
authors.initials: JE
authors.lastname: Cortes
authors.forename: Sherry A
authors.initials: SA
authors.lastname: Pierce
authors.forename: Frederick R
authors.initials: FR
authors.lastname: Appelbaum
authors.forename: Hagop A
authors.initials: HA
authors.lastname: Kantarjian
authors.forename: Elihu H
authors.initials: EH
authors.lastname: Estey
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D060828:Induction Chemotherapy; D015470:Leukemia, Myeloid, Acute / Q000188:drug therapy* / Q000401:mortality; D008297:Male; D008875:Middle Aged; D012306:Risk; D013997:Time Factors