ESTIMATE OF EXPECTED SURVIVAL AT DIAGNOSIS IN HODGKINS-DISEASE - A MEANS OF WEIGHING PROGNOSTIC FACTORS AND A TOOL FOR TREATMENT CHOICE ANDCLINICAL RESEARCH - A REPORT FROM THE INTERNATIONAL DATABASE ON HODGKINS-DISEASE (IDHD)

Citation
Pg. Gobbi et al., ESTIMATE OF EXPECTED SURVIVAL AT DIAGNOSIS IN HODGKINS-DISEASE - A MEANS OF WEIGHING PROGNOSTIC FACTORS AND A TOOL FOR TREATMENT CHOICE ANDCLINICAL RESEARCH - A REPORT FROM THE INTERNATIONAL DATABASE ON HODGKINS-DISEASE (IDHD), Haematologica, 79(3), 1994, pp. 241-255
Citations number
57
Categorie Soggetti
Hematology
Journal title
ISSN journal
03906078
Volume
79
Issue
3
Year of publication
1994
Pages
241 - 255
Database
ISI
SICI code
0390-6078(1994)79:3<241:EOESAD>2.0.ZU;2-3
Abstract
Purpose. The aim was to identify a mathematical model that, when fitte d with the survival time distribution of a Hodgkin's disease populatio n, would provide a reliable estimate of expected survival at diagnosis for any new Hodgkin patient. This model would be based upon a multiva riable selection of the best prognostic factors evaluable at diagnosis and its forecast could be of assistance in the choice of treatment. M ethods. The study sample consisted of the 5,023 patients whose basic c linical information was collected into the IDHD. These were people tre ated with standard protocols over the last two decades in 18 instituti ons. Several survival time distributions (exponential, Weibull, Gomper tz, log-logistic and log-normal) were investigated to find the one tha t best fit the data and to relate its parameters to patient prognostic characteristics. Results. The log-normal model provided the best fit for the data. The most statistically significant prognostic covariates were stage, age, histotype, B symptoms, serum albumin, sex and involv ed area distribution. Mediastinal, extranodal or bone marrow involveme nt, erythrocyte sedimentation rate, hemoglobin, serum alkaline phospha tase and lactate dehydrogenase did not add significant information. An equation containing these seven variables was derived to estimate med ian survival. Five distinct prognostic classes were identified by four cut-off values for this estimate. Conclusions. Direct use of estimate d median survival or allocating each patient into one of the five prog nostic classes allows better tailoring of clinical strategies accordin g to prognostic characteristics, more accurate patient stratification and evaluation of results in clinical trials and meta-analyses. Instru ctions are given for using this tool for both clinical and investigati onal purposes.