Is Pain Intensity a Predictor of the Complexity of Cancer Pain Management?

Abstract

Purpose The lack of a standardized cancer pain (CP) classification system prompted the development of the Edmonton Classification System for Cancer Pain (ECS-CP). Its five features have demonstrated value in predicting pain management complexity. Pain intensity (PI) at initial assessment has been proposed as having additional predictive value. We hypothesized that patients with moderate to severe CP would take longer to achieve stable pain control, use higher opioid doses, and require more complicated analgesic regimens than would patients with mild CP at initial assessment. Methods A secondary analysis of a multicenter ECS-CP validation study involving patients with advanced cancer was conducted (n = 591). Associations between PI and length of time to stable pain control (Cox regression), final opioid dose (Kruskal-Wallis one-way analysis of variance), and number of adjuvant modalities (χ2) were calculated. PI at initial assessment was defined using a numerical scale as mild (0 to 3), moderate (4 to 6), or severe (7 to 10). Results Patients with moderate and severe pain required a significantly longer time to achieve stable pain control (P < .0001). PI was a significant predictor of length of time to stable pain control in the univariate regression analysis. The four significant predictors in the multivariate model were moderate and severe PI (P < .0001), age (P = .001), and neuropathic pain (P = .002). Patients with moderate to severe pain required significantly higher final opioid doses (P < .0001) and more adjuvant modalities (P = .015). Conclusion PI at initial assessment is a significant predictor of pain management complexity and length of time to stable pain control. Incorporation of this feature into the ECS-CP needs additional consideration.