Racial differences in osteoarthritis pain and function: potential explanatory factors

SummaryObjectiveThis study examined factors underlying racial differences in pain and function among patients with hip and/or knee osteoarthritis (OA).MethodsParticipants were n=491 African Americans and Caucasians enrolled in a clinical trial of telephone-based OA self-management. Arthritis Impact Measurement Scales-2 (AIMS2) pain and function subscales were obtained at baseline. Potential explanatory variables included arthritis self-efficacy, AIMS2 affect subscale, problem- and emotion-focused pain coping, demographic characteristics, body mass index, self-reported health, joint(s) with OA, symptom duration, pain medication use, current exercise, and AIMS2 pain subscale (in models of function). Variables associated with both race and pain or function, and which reduced the association of race with pain or function by ≥10%, were included in final multivariable models.ResultsIn simple linear regression models, African Americans had worse scores than Caucasians on AIMS2 pain (B=0.65, P=0.001) and function (B=0.59, P<0.001) subscales. In multivariable models race was no longer associated with pain (B=0.03, P=0.874) or function (B=0.07, P=0.509), indicating these associations were accounted for by other covariates. Variables associated with worse AIMS2 pain and function were: worse AIMS2 affect scores, greater emotion-focused coping, lower arthritis self-efficacy, and fair or poor self-reported health. AIMS2 pain scores were also significantly associated with AIMS2 function.ConclusionFactors explaining racial differences in pain and function were largely psychological, including arthritis self-efficacy, affect, and use of emotion-focused coping. Self-management and psychological interventions can influence these factors, and greater dissemination among African Americans may be a key step toward reducing racial disparities in pain and function

Precision medicine for long-term depression outcomes using the Personalized Advantage Index approach:cognitive therapy or interpersonal psychotherapy?

Background Psychotherapies for depression are equally effective on average, but individual responses vary widely. Outcomes can be improved by optimizing treatment selection using multivariate prediction models. A promising approach is the Personalized Advantage Index (PAI) that predicts the optimal treatment for a given individual and the magnitude of the advantage. The current study aimed to extend the PAI to long-term depression outcomes after acute-phase psychotherapy. Methods Data come from a randomized trial comparing cognitive therapy (CT, n = 76) and interpersonal psychotherapy (IPT, n = 75) for major depressive disorder (MDD). Primary outcome was depression severity, as assessed by the BDI-II, during 17-month follow-up. First, predictors and moderators were selected from 38 pre-treatment variables using a two-step machine learning approach. Second, predictors and moderators were combined into a final model, from which PAI predictions were computed with cross-validation. Long-term PAI predictions were then compared to actual follow-up outcomes and post-treatment PAI predictions. Results One predictor (parental alcohol abuse) and two moderators (recent life events; childhood maltreatment) were identified. Individuals assigned to their PAI-indicated treatment had lower follow-up depression severity compared to those assigned to their PAI-non-indicated treatment. This difference was significant in two subsets of the overall sample: those whose PAI score was in the upper 60%, and those whose PAI indicated CT, irrespective of magnitude. Long-term predictions did not overlap substantially with predictions for acute benefit. Conclusions If replicated, long-term PAI predictions could enhance precision medicine by selecting the optimal treatment for a given depressed individual over the long term