Table 1.
Potential predictors of intercept (I), piecewice 1 (PW1), piecewice 2 (PW2), linear (L), quadratic (Q) and cubic (C) components for average and worst pain.
Table 2.
Demographic, clinical, symptom, and psychological characteristics of patients (N = 203) prior to surgery.
Table 3.
Hierarchical linear models of the trajectories for average and worst postoperative pain.
Fig 1.
Trajectories of average pain (A), and worst pain (B) using an unconditional model.
Fig 2.
Trajectories of average pain by number of comorbidities (A), CRP levels (B), BPI interference scores (C), perceived consequences of osteoarthritis (D), and average preoperative pain intensity (E) from before surgery until postoperative day 4. Higher/lower differences in Fig 2 A to F were calculated based on 1 standard deviation above/below the mean. The coefficients are adjusted for all other variables in the model.
Fig 3.
Trajectories of worst pain by age (A), fatigue (B), identity scores (C), and emotional response of osteoarthritis (D) frombefore surgery until postoperative day 4. Higher/lower differences in Fig 3 B to D were calculated based on 1 standard deviation above/below the mean. The coefficients are adjusted for all other variables in the model.
Fig 4.
Trajectories of worst pain by gender (A), average preoperative pain intensity prior to surgery (B), and worst preoperative pain intensity (C) from before surgery until postoperative day 4. Higher/lower differences in Fig 4 B to C were calculated based on 1 standard deviation above/below the mean. The coefficients are adjusted for all other variables in the model.