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Consistency and Accuracy of Multiple Pain Scales Measured in Cancer Patients From Multiple Ethnic Groups
Journal article   Open access   Peer reviewed

Consistency and Accuracy of Multiple Pain Scales Measured in Cancer Patients From Multiple Ethnic Groups

Ok-Kyung Ham, Youjeong Kang, Helen Teng, Yaelim Lee and Eun-Ok Im
Cancer nursing, v 38(4), pp 305-311
Jul 2015
PMID: 25068188
url
https://europepmc.org/articles/pmc4305507View
Accepted (AM)Open Access (License Unspecified) Open

Abstract

Life Sciences & Biomedicine Nursing Oncology Science & Technology
Background: Standardized pain-intensity measurement across different tools would enable practitioners to have confidence in clinical decision making for pain management. Objectives: The purpose was to examine the degree of agreement among unidimensional pain scales and to determine the accuracy of the multidimensional pain scales in the diagnosis of severe pain. Methods: A secondary analysis was performed. The sample included a convenience sample of 480 cancer patients recruited from both the Internet and community settings. Cancer pain was measured using the Verbal Descriptor Scale (VDS), the visual analog scale (VAS), the Faces Pain Scale (FPS), the McGill Pain Questionnaire-Short Form (MPQ-SF), and the Brief Pain Inventory-Short Form (BPI-SF). Data were analyzed using a multivariate analysis of variance and a receiver operating characteristic curve. Results: The agreement between the VDS and VAS was 77.25%, whereas the agreement was 71.88% and 71.60% between the VDS and FPS, and VAS and FPS, respectively. The MPQ-SF and BPI-SF yielded high accuracy in the diagnosis of severe pain. Cutoff points for severe pain were more than 8 for the MPQ-SF and more than 14 for the BPI-SF, which exhibited high sensitivity and relatively low specificity. Conclusion: The study found substantial agreement between the unidimensional pain scales and high accuracy of the MPQ-SF and the BPI-SF in the diagnosis of severe pain. Implications for Practice: Use of 1 or more pain screening tools that have validated diagnostic accuracy and consistency will help classify pain effectively and subsequently promote optimal pain control in multiethnic groups of cancer patients.

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Nursing
Oncology
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