Objective: The present study compared two different approaches for deriving patient profiles on their ability to predict treatment outcome to a pain medicine program for migraine headache.
Design/methods: Using visual analog scale measures of pain intensity and functional limitations and the Beck Depression Inventory (BDI), as a measure of depression, 235 migraine patients were classified into statistical clusters. The same patients were also classified using the Multidimensional Pain Inventory (MPI) algorithm into three subgroups: Adaptive copers (AC), characterized by lower reported levels of pain intensity, life interference, and distress, as well as higher levels of perceived life control; interpersonally distressed (ID), characterized by more intermediate levels of pain, distress, and interference, with a predominant perception of inadequate support and punishing responses from significant others; and dysfunctional (Dys), characterized by high levels of pain severity, life interference, and distress and low levels of perceived life control and activity.
Results: The results of the K-cluster analysis yielded a three-cluster solution: The low impact cluster, was characterized by low pain, low functional limitations and low depression and showed significant reductions in pre-to-posttreatment pain; the moderate impact cluster displayed higher levels of pain and functional limitations and low depression and showed only slight pre-to-posttreatment pain reduction; and the high impact cluster displayed the highest levels of pain, functional limitations, and depression and showed significant increases in pre-to-posttreatment pain. Unlike the K-clustered groups, MPI subgroups failed to differentially predict treatment outcome. When the K-clustered groups were crosstabulated with the MPI subgroups, the predictive validity of the MPI subgroups was enhanced.
Conclusion: This study questions the validity of the MPI subgroup classification algorithm. The results indicate that the K-clustering approach is more useful than the MPI in deriving meaningful patient clusters that differentially predict treatment outcome in a migraine population.