Abstract

Modelling acute post-operative pain trajectories may improve the prediction of persistent pain after breast cancer surgery (PPBCS). This study aimed to investigate the predictive accuracy of early post-operative pain (EPOP) trajectories in the development of PPBCS. This observational study was conducted in a French Comprehensive Cancer Centre and included patients who underwent breast cancer surgery from December 2017 to November 2018. Perioperative and follow-up data were obtained from medical records, and anaesthesia and perioperative charts. EPOP was defined as pain intensity during the first 24 h after surgery, and modelled by a pain trajectory. K-means clustering method was used to identify patient subgroups with similar EPOP trajectories. The prevalence of moderate-to-severe PPBCS (numeric rating scale ≥4) was evaluated until 24 months after surgery. A total of 608 patients were included in the study, of which 18% (n= 108) and 9% (n= 52) reported mild and moderate-to-severe PPBCS, respectively. Based on EPOP trajectories, we were able to identify a low (64%, n = 388), resolved (30%, n = 182), and unresolved (6%, n = 38) pain group. Multivariate analysis identified younger age, axillary lymph node dissection, and unresolved EPOP trajectory as independent risk factors for moderate-to-severe PPBCS development. When compared to patients reporting mild PPBCS, moderate-to-severe PPBCS patients experienced significantly more neuropathic pain features, pain-related interference, and delayed opioid cessation. EPOP trajectories can distinguish between resolved and unresolved acute pain after breast cancer surgery, allowing early identification of patients at risk to develop significant PPBCS.

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