title: A Decision analysis to determine the appropriate treatment for low-risk myelodysplastic syndromes.
abstract: BACKGROUND
  The myelodysplastic syndromes (MDS) are divided into low-risk and high-risk diseases. Predictive models for response to growth factors (GF) have been developed based on red blood cell transfusion needs and erythropoietin levels. For low-risk MDS the optimal initial therapy (GF vs nongrowth factor [NGF] therapies, including differentiation and immunomodulatory agents) based on response rates to NGF and GF and survival, has not been defined.
  
  
  METHODS
  A Markov decision analysis was performed on 799 low-risk MDS patients treated with either GF or NGF to determine the appropriate initial therapy. The treatment strategies analyzed included initial GF or NGF therapies, assuming 3 different states: Patients were either in the good GF predictive group (low transfusion needs and low erythropoietin levels), intermediate, or the poor GF predictive group (high transfusion needs and high erythropoietin levels).
  
  
  RESULTS
  In the good GF predictive group, initial therapy with GF improved survival compared with NGF therapies at 3.38 years vs 2.57 years for a typical MDS patient. The advantage of GF to NGF was lost when NGF therapies produced a response in >or=46% of patients. In the intermediate or poor GF predictive groups, NGF maximized survival, provided response rates for NGF were >14% and 4%, respectively, for each predictive group. Quality of life adjustment did not alter the preferred strategy.
  
  
  CONCLUSIONS
  Modeling estimates suggest that patients who fall into a good GF predictive group should almost always receive GF initially, whereas those in intermediate and poor predictive groups should almost always be treated with NGF.
authors.affiliation: Department of Hematologic Oncology and Blood Disorders, Taussig Cancer Center, Cleveland Clinic, Cleveland, Ohio 44195, USA. sekerem@ccf.org
authors.forename: Mikkael A
authors.initials: MA
authors.lastname: Sekeres
authors.forename: Alex Z
authors.initials: AZ
authors.lastname: Fu
authors.forename: Jaroslaw P
authors.initials: JP
authors.lastname: Maciejewski
authors.forename: Ali-Reza
authors.initials: AR
authors.lastname: Golshayan
authors.forename: Matt E
authors.initials: ME
authors.lastname: Kalaycio
authors.forename: Michael W
authors.initials: MW
authors.lastname: Kattan
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D036341:Intercellular Signaling Peptides and Proteins / Q000627:therapeutic use*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009190:Myelodysplastic Syndromes / Q000175:diagnosis* / Q000188:drug therapy* / Q000401:mortality; D009414:Nerve Growth Factors / Q000627:therapeutic use*; D012306:Risk; D016896:Treatment Outcome
chemical_list: D036341:Intercellular Signaling Peptides and Proteins; D009414:Nerve Growth Factors