Construction of a clinical predictive model for risk factors after laparoscopic surgery in patients with endometriosis based on pathologic characteristics.

Abstract

Objective: To investigate the correlation between preoperative pathologic characteristics and the risk of postoperative recurrence in endometriosis (EMs) patients, and to develop a clinical predictive model.

Methods: A retrospective analysis was conducted on 164 EMs patients who underwent laparoscopic surgery between January 2022 and December 2023 at Peking University First Hospital Ningxia Women and Children's Hospital. Demographic and clinicopathologic data were collected, and patients were stratified by one-year recurrence status. Multivariable logistic regression identified independent recurrence risk factors, and a predictive nomogram was constructed. Model performance was evaluated using ROC curves, the Hosmer-Lemeshow goodness-of-fit test (HLGOF), calibration curves, and decision curve analysis.

Results: Postoperative recurrence occurred in 46 patients (28%) within one year. Univariate analysis revealed associations between recurrence and factors including dysmenorrhea history, abortion, pathologic type, American Society for Reproductive Medicine (ASRM) stage, abnormal uterine bleeding, posterior fornix tender nodules, uterine enlargement, accessory area thickening, and delivery history (all P<0.05). Multivariate analysis confirmed that abortion (OR=1.31), ASRM stage ≥III (OR=1.03), abnormal uterine bleeding (OR=1.72), and posterior fornix tender nodules (OR=1.34) were independent predictors (all P<0.05). The nomogram (Logit (P)=-3.30+1.31X₁+1.03X₂+1.72X₃+1.34X₄) demonstrated an AUC of 0.802, with 71% sensitivity and 76% specificity. The HLGOF and calibration curves indicated that the predicted values were not significantly different from the observed values, showing good model fit (H-L, P>0.05).

Conclusion: Preoperative pathologic features are significant predictors of recurrence after laparoscopic surgery for EMs. Monitoring these markers can help clinicians identify high-risk patients and provide more targeted treatment.