Determination of Pain Phenotypes in Knee Osteoarthritis: A Latent Class Analysis Using Data from the Osteoarthritis Initiative

Andrew J. Kittelson, Jennifer E. Stevens-Lapsley, Sarah J. Schmiege

Research output: Contribution to journalArticlepeer-review

Abstract

Objective Knee osteoarthritis (OA) is a broadly applied diagnosis that may describe multiple subtypes of pain. The purpose of this study was to identify phenotypes of knee OA, using measures from the following pain-related domains: 1) knee OA pathology, 2) psychological distress, and 3) altered pain neurophysiology. Methods Data were selected from a total of 3,494 participants at visit 6 of the Osteoarthritis Initiative study. Latent class analysis was applied to the following variables: radiographic OA severity, quadriceps strength, body mass index, the Charlson Comorbidity Index (CCI), the Center for Epidemiologic Studies Depression Scale, the Coping Strategies Questionnaire-Catastrophizing subscale, number of bodily pain sites, and knee joint tenderness at 4 sites. The resulting classes were compared on the following demographic and clinical factors: age, sex, pain severity, disability, walking speed, and use of arthritis-related health care. Results A 4-class model was identified. Class 1 (4% of the study population) had higher CCI scores. Class 2 (24%) had higher knee joint sensitivity. Class 3 (10%) had greater psychological distress. Class 4 (62%) had lesser radiographic OA, little psychological involvement, greater strength, and less pain sensitivity. Additionally, class 1 was the oldest, on average. Class 4 was the youngest, had the lowest disability, and least pain. Class 3 had the worst disability and most pain. Conclusion Four distinct pain phenotypes of knee OA were identified. Psychological factors, comorbidity status, and joint sensitivity appear to be important in defining phenotypes of knee OA-related pain.

Original languageEnglish
Pages (from-to)612-620
Number of pages9
JournalArthritis Care and Research
Volume68
Issue number5
DOIs
StatePublished - May 1 2016

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