Multidimensional subgroups in migraine: differential treatment outcome to a pain medicine program.

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 De...

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Publicado en:Pain Medicine Vol. 4; no. 3; pp. 215 - 223
Autores principales: Davis PJ, Reeves JL II, Graff-Radford SB, Hastie BA, Naliboff BD
Formato: research tables/charts Journal Article
Publicado: Oxford University Press / USA Sep2003
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Sep2003
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      pub: Oxford University Press / USA
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        atl: Multidimensional subgroups in migraine: differential treatment outcome to a pain medicine program.
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          Davis PJ
          Reeves JL II
          Graff-Radford SB
          Hastie BA
          Naliboff BD
        affil: UCLA Department of Psychiatry and Biobehavioral Sciences, Los Angeles, CA
      sug:
        subj:
          Migraine Classification
          Pain Measurement
          Migraine Psychosocial Factors
          Treatment Outcomes
          Migraine Therapy
          Visual Analog Scaling
          Clinical Assessment Tools
          Psychological Tests
          Cluster Analysis
          Data Analysis Software
          Chi Square Test
          One-Way Analysis of Variance
          Adolescence
          Adult
          Middle Age
          Aged
          Aged, 80 and Over
          Human
          Adolescent: 13-18 years
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Aged: 65+ years
          Aged, 80 & over
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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