Presurgical Behavioral Medicine Evaluation for Implantable Devices for Pain Management: Clinical Effectiveness for Predicting Outcomes




Schocket, Kimberly Gardner

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The current study attempts to apply a presurgical psychological screening algorithm to a subset of patients being considered to receive implantable pain management devices, specifically spinal cord stimulators and intrathecal drug delivery systems. The Presurgical Behavioral Medicine Evaluation (PBME) algorithm was designed to evaluate patients prior to spine surgery. The algorithm showed strong outcome predictability in previous studies (Block et al., 2003). A PBME was administered to 60 patients being evaluated for implantable devices at a major pain center that provides interdisciplinary pain management to patients. Patients were classified into one of five prognostic categories including Green, Yellow-I, Yellow-II, Red-I, and Red-II. This study sought to elucidate the characteristics of patients falling into the separate prognostic categories. Analyses revealed that males were more likely than females to fall in the Green and Yellow-I groups and patients receiving disability were more commonly found in the Red and Yellow-II groups. The biopsychosocial profiles of each category were examined using various physical/functional and psychosocial measures. As hypothesized, the Green group, with the lowest mean scores for each measure, yielded the most positive biopsychosocial profile at initial evaluation. The Green group reported low levels of depression and little impairment in physical functioning. The Red group obtained the highest mean scores, indicating decreased biopsychosocial functioning at initial evaluation. More specifically, the Red group experienced more depressive symptomatology and decreased physical functioning at the time of the initial evaluation. Additionally, the Red group had a greater number of medical risk factors and the presence of adverse clinical features at onset, and was more likely to use catastrophizing as a coping strategy. The patients were also compared at follow-up showing improvements on most physical/functional and psychosocial measures. Lastly, regression analyses were conducted to elucidate those factors most predictive of prognostic assignments. Thus, the algorithm was able to correctly classify those patients who were and were not appropriate candidates for surgery by collecting and analyzing data with regard to the overall biopsychosocial functioning of patients.

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