Persistent postmastectomy pain after breast surgery is variable in duration and severity across patients, due in part to interindividual variability in pain processing. The Rapid OPPERA Algorithm (ROPA) empirically identified 3 clusters of patients with different risk of chronic pain based on 4 key psychophysical and psychosocial characteristics. We aimed to test this type of group-based clustering within in a perioperative cohort undergoing breast surgery to investigate differences in postsurgical pain outcomes. Women (N = 228) scheduled for breast cancer surgery were prospectively enrolled in a longitudinal observational study. Pressure pain threshold (PPT), anxiety, depression, and somatization were assessed preoperatively. At 2-weeks, 3, 6, and 12-months after surgery, patients reported surgical area pain severity, impact of pain on cognitive/emotional and physical functioning, and pain catastrophizing. The ROPA clustering, which used patients’ preoperative anxiety, depression, somatization, and PPT scores, assigned patients to 3 groups: Adaptive (low psychosocial scores, high PPT), Pain Sensitive (moderate psychosocial scores, low PPT), and Global Symptoms (high psychosocial scores, moderate PPT). The Global Symptoms cluster, compared to other clusters, reported significantly worse persistent pain outcomes following surgery. Findings suggest that patient characteristic-based clustering algorithms, like ROPA, may generalize across diverse diagnoses and clinical settings, indicating the importance of “person type” in understanding pain variability.

This article presents the practical translation of a previously developed patient clustering solution, based within a chronic pain cohort, to a perioperative cohort of women undergoing breast cancer surgery. Such preoperative characterization could potentially help clinicians apply personalized interventions based on predictions concerning postsurgical pain.