Individual quality assessment of autografting by probability evaluation: a model estimated by analysis of graft-related end points in 204 patients with malignancies.
Haematological toxicity is considered a secondary end point important for graft evaluation - in today's practice graft evaluation focuses on the primary impact of health economic end points. This report illustrates the benefit of combining CD34 enumeration and demographic as well as disease-related variables in models for individual quality assessment of autografting following high-dose therapy. A total of 24 centres in Scandinavia enrolled 204 patients younger than 67 years who received high-dose therapy with autologous peripheral blood stem cell transplantation. Using the binary Logistic Regression Analysis, the prognostic value of diagnostic demographic variables, therapy and graft-related factors was entered into a multivariate analysis and the final significant models were used to estimate probabilities for acceptable or unacceptable outcome among different patient scenarios. The model that estimated post-transplant efficacy by selected primary end points (time on antibiotics and use of transfusions) includes six independent variables related to sex, age, disease, conditioning, growth factor administration, and graft CD34+ cell number. The model that estimated transplantation-related toxicity by selected secondary end points (time to blood cell recovery) included four independent variables related to age, disease, growth factor administration and graft CD34+ cell number.
Baech
J
J
The Department of Clinical Immunology, Aalborg University Hospital, Denmark.
Roer
O
O
Johnsen
H E
HE
D000293:Adolescent; D000328:Adult; D000368:Aged; D018952:Antigens, CD34 / Q000032:analysis; D001943:Breast Neoplasms / Q000628:therapy; D005260:Female; D018380:Hematopoietic Stem Cell Transplantation / Q000592:standards* / Q000706:statistics & numerical data*; D006689:Hodgkin Disease / Q000628:therapy; D006801:Humans; D007962:Leukocytes / Q000737:chemistry / Q000166:cytology; D016015:Logistic Models; D008228:Lymphoma, Non-Hodgkin / Q000628:therapy; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009101:Multiple Myeloma / Q000628:therapy; D009369:Neoplasms / Q000628:therapy*; D011336:Probability; D011379:Prognosis; D011785:Quality Assurance, Health Care / Q000379:methods*; D013736:Testicular Neoplasms / Q000628:therapy; D014182:Transplantation, Autologous
D018952:Antigens, CD34