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)
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
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.