Abstract| Volume 14, ISSUE 4, SUPPLEMENT , S15, April 2013

Predicting pain after joint surgery

      Predicting which patients will endure greater postoperative pain after joint surgery and the level of benefit these patients will receive from pain reduction interventions is often difficult. This study grouped psychosocial predictors into larger domains for enhanced prediction. Adult patients (> 18 years-old) scheduled for joint surgery (e.g., hip or knee replacement) were enrolled. N = 51 patients completed a packet of baseline questionnaires including the Pain Medication Attitudes Questionnaire, Pain Anxiety Symptoms Scale, Pain Disability Index, Pain Self-Efficacy Questionnaire, State-Trait Anxiety Inventory, Pain Locus of Control Scale, Center for Epidemiologic Studies Depression Scale, Marital Adjustment Test, Daily Stress Inventory, and McGill Pain Questionnaire. To determine if items/subscales could be grouped into latent domains for use in future prediction research, a principal component analysis was conducted using the subscales for each questionnaire. A daily pain diary was then completed by the patients for 2 months after surgery and analyzed using hierarchical linear models. Eight psychosocial domains were identified that represented large constructs that predicted several aspects of pain after surgery. Initial levels of pain were predicted by domains such as affective distress, external locus of control, distrust of healthcare, and high perceived need of medication (p’s < 0.05). Quicker reductions in pain were predicted by several domains. For example, each standard deviation increase in affective distress was associated with a -0.70 diminished reduction in McGill Pain Questionnaire total pain score in each day after surgery (95% CI: -0.90 to -0.50, p <0.0001). The results support the utility of latent domains for predicting pain reduction after joint surgery. Because these super domains are more reliable than individual scales and cover unique dimensions of patient experience, they have a very large potential to be used in predicting pain outcomes in future studies.