Originally published as JCO Early Release 10.1200/JCO.2008.17.1660 on December 22 2008

This Article Full Text Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Rights & Permissions Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Fainsinger, R. L. Articles by Hanson, J. Search for Related Content PubMed PubMed Citation Articles by Fainsinger, R. L. Articles by Hanson, J. Social Bookmarking                            What's this?

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

From the Division of Palliative Care Medicine; Division of Radiation Oncology; and Alberta Heritage Foundation for Medical Research, Department of Oncology, University of Alberta; and the Department of Oncology, Cross Cancer Institute, Edmonton, Alberta, Canada.

Corresponding author: Robin Fainsinger, MD, Division of Palliative Care Medicine, Grey Nuns Community Hospital, 217 Health Services Centre, 1090 Youville Dr West, Edmonton, Alberta, Canada T6L 5X8; e-mail: Robin.Fainsinger{at}capitalhealth.ca.

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 (²) 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.

Supported by a grant from the Royal Alexandra Hospital Foundation and funding from the Alberta Cancer Board Palliative Care Research Initiative. R.L.F. and C.N. are supported by the Canadian Institutes of Health Research through grant support for the Canadian Institutes of Health Research New Emerging Team in Difficult Pain Problems (CIHR PET69772), and an international multicenter validation study of a pain classification system for patients with advanced cancer.

Presented at the 4th Research Forum of the European Association for Palliative Care, May 25-27, 2006, Venice, Italy.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

CiteULike    Complore    Connotea    Del.icio.us    Digg    Facebook    Reddit    Technorati    Twitter    What's this?