Study defines domains needed in a cancer pain classification system
MedWire News: A study published in Pain highlights key domains that may be useful for classifying cancer pain.
"Standardized assessment of key domains is crucial for the correct classification of cancer pain. However, assessment methods used in clinical practice and in research vary considerably," write Anne Knudsen (Norwegian University of Science and Technology, Trondheim, Norway) and colleagues.
For this study they used observational data collected by the Italian Cancer Pain Outcome Research Study Group (CPOR).
Two samples were analyzed: the first included 1529 patients receiving opioids at inclusion (sample one), and the second included 352 patients just about to be admitted to palliative care (sample two).
Data collected at baseline and from the weekly sessions for the fortnight following were used in the present study. At each session the healthcare provider recorded medical history, physical examination data, medications, recent therapies, functional status, and Edmonton Classification System for Cancer Pain results.
The patients completed a pain questionnaire at each visit assessing symptoms and opioid side effects. Pain was measured using questions from the Italian Brief Pain Inventory assessed using 11-point rating scales, and symptoms and side effects were measured using items from the Therapy Impact Questionnaire (a 4-point rating scale ranging from 1 [absent] to 4 [very much]).
The outcome measures analyzed were average pain, worst pain, and pain relief at inclusion.
Knudsen and team found that the domains associated with one or more outcome(s) in sample one were: incident pain, pain localization, morphine equivalent daily dose, use of nonsteroidal anti-inflammatory drugs, and sleep. Incident pain and localization of pain were also pain predictors in sample two, as well as initial pain intensity, initial pain relief, cancer diagnosis, and age.
Overall, the domains identified in this study accounted for 16% to 24% of the variation in pain outcome, with initial pain intensity as the strongest predictor of outcome.
The researchers suggest that these domains be considered for inclusion in a cancer pain classification system, in addition to the four currently used (pain intensity, pain mechanism, incident pain, and psychologic distress).
"A classification or prognostic system must be considered dynamic," say the research team. They conclude: "New domains should be added when new evidence identifies domains that improve the predictive ability of the cancer pain classification system."
By Chloe McIvor