Influencing factors and prediction model construction for recurrence in patients with ovarian endometriosis after laparoscopic conservative surgery

Objective: To investigate the factors influencing recurrence following laparoscopic conservative surgery in patients with ovarian endometriosis (OEM) and to develop a predictive model.

Methods: In this retrospective study, the clinical data from 212 OEM patients who underwent laparoscopic conservative surgery at Suzhou Ninth People's Hospital from May 2013 to December 2021 were meticulously reviewed. According to disease recurrence over a 2-year follow-up period, the patients were divided into a recurrence group and a non-recurrence group. Univariate and multivariate logistic regression analyses were performed to identify factors associated with postoperative recurrence in OEM patients. A nomogram prediction model for postoperative recurrence in OEM patients was constructed using R 3.4.3 software. The discriminative power of the model was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), with goodness of fit evaluated using the H-L goodness-of-fit test and Bootstrap method (self-sampling method). Clinical net benefit was analyzed through decision curve analysis.

Results: Over a two-year follow-up, 36 cases of recurrence were observed, yielding a recurrence rate of 16.98%. Bilateral cysts (OR = 2.257, P = 0.005), high r-ASRM stage (OR = 2.651, P = 0.001), and elevated postoperative TNF-α levels (OR = 3.607, P = 0.004) were identified as risk factors for recurrence after laparoscopic conservative surgery in patients with OEM, while older age (OR = 0.566, P = 0.018) and postoperative adjuvant medication (OR = 0.509, P = 0.016) were protective factors. The nomogram prediction model, based on the above indicators, had an AUC of 0.895 for postoperative recurrence risk in OEM patients, with no overfitting phenomenon indicated by the goodness-of-fit test (χ² = 1.786, P = 0.987). The Bootstrap validation (1000 samples) showed an average absolute error of 0.018 between predicted and actual probabilities. Decision curve analysis showed that the model effectively predicted a clinically relevant net benefit for postoperative recurrence risk.

Conclusion: A nomogram prediction model incorporating age, cyst distribution, r-ASRM staging, postoperative TNF-α levels, and postoperative adjuvant drugs effectively assesses the recurrence risk in OEM patients.