Abstract
Background Chronic pelvic pain (CPP) remains a common and clinically challenging outcome after uterus-preserving surgery for adenomyosis. Identifying patients at higher risk of postoperative CPP may facilitate individualised postoperative management.Methods The clinical data of 150 patients with adenomyosis admitted to our hospital from January 2022 to January 2025 were prospectively collected for observation. Uterine volume, Pictorial Blood Loss Assessment Chart (PBAC) score, Cox Menstrual Symptom Scale (CMSS) score, cancer antigen 125 (CA125) level, haemoglobin (Hb) level, and three-level version of the EuroQol five-dimensional questionnaire (EQ-5D-3L) score were recorded preoperatively and at 3 and 6 months postoperatively. Patients were divided into CPP (n = 38) and non-CPP (n = 112) groups based on 6‑month CPP status. Independent risk factors were identified using logistic regression, and a predictive model was developed and validated.Results Significant improvements were observed in uterine volume, PBAC score, CMSS score, CA125 level, and Hb level postoperatively (P < 0.05). EQ-5D-3L scores improved in usual activities, pain/discomfort, and anxiety/depression (P < 0.05). Preoperative CMSS score, CA125 level, and diffuse-type adenomyosis were identified as independent factors associated with CPP, while preoperative hormonal pre-treatment and postoperative regular hormonal therapy showed significant associations in univariate analysis. The predictive model incorporating these variables showed good calibration (Hosmer-Lemeshow χ2 = 15.477, P = 0.051) and strong discrimination (AUC = 0.820, 95% CI: 0.743–0.897). At the maximum Youden’s index (0.567), sensitivity was 76.30% and specificity was 80.40% (P < 0.001).Conclusion High preoperative CMSS score, high CA125 level, and diffuse-type adenomyosis are independent risk factors for postoperative CPP, while preoperative hormonal pre-treatment and postoperative regular hormonal therapy may be associated with a lower risk of CPP. The predictive model effectively identifies high-risk patients, supporting targeted nursing interventions.