Objectives:
Pain, distress, and depression are predictors of posttrauma pain and recovery. We hypothesized that pretrauma characteristics of the person could predict posttrauma severity and recovery.
Methods:
Sex, age, body mass index, income, education level, employment status, pre-existing chronic pain or psychopathology, and recent life stressors were collected from adults with acute musculoskeletal trauma through self-report. In study 1 (cross-sectional, n=128), pain severity was captured using the Brief Pain Inventory (BPI), distress through the Traumatic Injuries Distress Scale (TIDS) and depression through the Patient Health Questionnaire-9 (PHQ-9). In study 2 (longitudinal, n=112) recovery was predicted using scores on the Satisfaction and Recovery Index (SRI) and differences within and between classes were compared with identify pre-existing predictors of posttrauma recovery.
Results:
Through bivariate, linear and nonlinear, and regression analyses, 8.4% (BPI) to 42.9% (PHQ-9) of variance in acute-stage predictors of chronicity was explainable through variables knowable before injury. In study 2 (longitudinal), latent growth curve analysis identified 3 meaningful SRI trajectories over 12 months. Trajectory 1 (start satisfied, stay satisfied [51%]) was identifiable by lower TIDS, BPI, and PHQ-9 scores, higher household income and less likely psychiatric comorbidity. The other 2 trajectories (start dissatisfied, stay dissatisfied [29%] versus start dissatisfied, become satisfied [20%]) were similar across most variables at baseline save for the “become satisfied” group being mean 10 years older and entering the study with a worse (lower) SRI score.
Discussion:
The results indicate that 3 commonly reported predictors of chronic musculoskeletal pain (BPI, TIDS, PHQ-9) could be predicted by variables not related to the injurious event itself. The 3-trajectory recovery model mirrors other prior research in the field, though 2 trajectories look very similar at baseline despite very different 12-month outcomes. Researchers are encouraged to design studies that integrate, rather than exclude, the pre-existing variables described here.