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Erschienen in:
06.04.2017 | Original Article
Assessing the prognostic features of a pain classification system in advanced cancer patients
Erschienen in: Supportive Care in Cancer | Ausgabe 9/2017
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Purpose
The Edmonton Classification System for Cancer Pain (ECS-CP) has been shown to predict pain management complexity based on five features: pain mechanism, incident pain, psychological distress, addictive behavior, and cognitive function. The main objective of our study was to explore the association between ECS-CP features and pain treatment outcomes among outpatients managed by a palliative care specialist-led interdisciplinary team.
Methods
Initial and follow-up clinical information of 386 eligible supportive care outpatients were retrospectively reviewed and analyzed.
Results
Between the initial consultation and the first follow-up visit, the median ESAS pain intensity improved from 6 to 4.5 (p < 0.0001) and the median total symptom distress score (0–100) improved from 38 to 31 (p < 0.0001). At baseline, patients with neuropathic pain (p < 0.001) and those with at least one ECS-CP feature (p = 0.006) used a higher number of adjuvant medications. At follow-up, patients with neuropathic pain were less likely to achieve their personalized pain goal (PPG) (29 vs 72%, p = 0.015). No statistically significant association was found between increasing sum of ECS-CP features and any of the pain treatment outcomes at follow-up.
Conclusion
Neuropathy was found to be a poor prognostic feature in advanced cancer pain management. Increasing sum of ECS-CP features was not predictive of pain management complexity at the follow-up visit when pain was managed by a palliative medicine specialist. Further research is needed to further explore these observations.