Predicting depression in rheumatoid arthritis: the signal importance of pain extent and fatigue, and comorbidity

Abstract

Objective: To determine the incidence of self-reported depression (SRD) in rheumatoid arthritis and to identify and rank clinically useful predictors of depression.

Methods: We assessed 22,131 patients for SRD between 1999 and 2008. We collected demographic, clinical and treatment data, household income, employment and work disability status, comorbidity, scales for function, pain, global, and fatigue, the Regional Pain Scale (RPS), the Symptom Intensity (SI) scale (a linear combination of the RPS and the fatigue scales) and linear combinations of the Health Assessment Questionnaire, pain and global severity. We used logistic regression analyses with multivariable fractional polynomial predictors, and Random Forest analysis to determine the importance of the predictors.

Results: The cross-sectional prevalence of self-reported depression was 15.2% (95% confidence interval [95% CI] 14.7-15.7%) and the incidence rate was 5.5 (95% CI 5.3-5.7) per 100 patient years of observation. The cumulative risk of SRD after 9 years was 38.3% (95% CI 36.6-40.1%). Almost all variables were significant predictors in logistic models. In Random Forest analyses, the SI scale, followed by comorbidity, best predicted self-reported depression, and no other variable or combination of variables improved prediction compared with the SI scale.

Conclusion: Pain extent and fatigue (SI scale) are the dominant predictors of SRD. These variables, also of central importance in the symptomatology of fibromyalgia, are powerful markers of distress. A strong case can be made for the inclusion of these assessments in routine rheumatology practice. In addition, actual knowledge of comorbidity provides important insights into the patient's global health and associated perceptions.