title: Comorbidities and FLT3-ITD abnormalities as independent prognostic indicators of survival in elderly acute myeloid leukaemia patients.
abstract: Elderly acute myeloid leukaemia (AML) patients have a dismal prognosis due to biological features of disease in itself and to presence of comorbidities. Aim of this study was to evaluate the prognostic impact of comorbidity prognostic score systems applied in our population of patients. as well as other clinical-biological features. We retrospectively considered the outcome of 120 patients aged >65 years diagnosed as having AML between January 2001 and December 2005. Comorbidities were evaluated by using Charlson comorbidity index (CCI), Hematopoietic cell transplantation comorbidity index (HCTCI) and a score proposed by Dombret et al. in 2007. Median patient age was 67 years. Forty-six patients were treated with intensive chemotherapy and 23 reached a complete remission. Seventy-four patients received only supportive therapies or low-dose chemotherapy. Multivariate analysis showed the effects of leukocytosis (p = 0.0013), antecedent Myelodysplastic syndrome (MDS) (p = 0.011), FLT3 abnormalities (p = 0.032), CCI (p = 0.0037) and Dombret et al. score (p = 0.045) as independent prognostic parameters for survival. Based on these variables we were able to stratify patients in low and high risk, with different median overall survival: patients were considered as low risk if they had none or only one of the above mentioned adverse factors for survival, with a median overall survival of 447 days. Patients with two or more adverse factors were categorized as high risk: this subgroup had a median overall survival of 227 days (p = 0.001). Comorbidities are independent factors that influence survival. Application of CCI and Dombret score may help to better identify patients at diagnosis who can benefit from intensive chemotherapy.
authors.affiliation: Department of Cellular Biotechnology and Hematology, University La Sapienza, 00161 Rome, Italy. breccia@bce.uniroma1.it
authors.forename: Massimo
authors.initials: M
authors.lastname: Breccia
authors.forename: Anna Maria
authors.initials: AM
authors.lastname: Frustaci
authors.forename: Laura
authors.initials: L
authors.lastname: Cannella
authors.forename: Caterina
authors.initials: C
authors.lastname: Stefanizzi
authors.forename: Roberto
authors.initials: R
authors.lastname: Latagliata
authors.forename: Claudio
authors.initials: C
authors.lastname: Cartoni
authors.forename: Daniela
authors.initials: D
authors.lastname: Diverio
authors.forename: Anna
authors.initials: A
authors.lastname: Guarini
authors.forename: Mauro
authors.initials: M
authors.lastname: Nanni
authors.forename: Angela
authors.initials: A
authors.lastname: Rago
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Cimino
authors.forename: Giuliana
authors.initials: G
authors.lastname: Alimena
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D005260:Female; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000175:diagnosis / Q000453:epidemiology* / Q000401:mortality*; D007964:Leukocytosis / Q000453:epidemiology*; D008297:Male; D015999:Multivariate Analysis; D009154:Mutation; D009190:Myelodysplastic Syndromes / Q000453:epidemiology*; D011379:Prognosis; D012189:Retrospective Studies; D051941:fms-Like Tyrosine Kinase 3 / Q000032:analysis / Q000235:genetics*
chemical_list: C497970:FLT3 protein, human; D051941:fms-Like Tyrosine Kinase 3