Use of logistic regression analysis to improve prediction of prognosis in acute myeloid leukaemia

Abstract

The prognostic usefulness of a range of factors has been examined for patients with acute myeloid leukaemia. Although there was a statistical association between some of these factors and remission rate, the association was only partial. To improve the usefulness of the data, multiple logistic regressional analysis was used. The features selected for use in the analysis were age, blood blast count, FAB classification and colony growth pattern. The last three features could be used as categorical variables, since blood blast counts of > 100 × 10 9/l, FAB group 1 and a prolific pattern of colony growth were associated with a low remission rate. Age was used as a continuous variable. Using these features, eight regression groups were defined. Thus when this data for an individual patient is analysed, it is possible to obtain a value for the probability of that patient achieving remission.