A logistic regression model for predicting postoperative recurrence of cellular uterine leiomyoma

The risk factors for the postoperative recurrence of cellular uterine leiomyoma (CUL) remain unclear.This study aimed to identify independent risk factors for recurrence and to develop a predictive model based on clinical characteristics to assess individual recurrence risk.

This retrospective study included patients who underwent uterine myomectomy at Fujian Maternal and Child Health Hospital between January 1, 2012, and December 31, 2021, and were pathologically confirmed to have CUL. The 176 patients who met the inclusion criteria were grouped into two categories: the recurrence group (76 cases) and the non-recurrence group (100 cases). Univariate and multivariate logistic regression analyses were performed to identify the risk factors for postoperative recurrence, and a nomogram prediction model was built.

Patients were followed for a median of 4 years (range: 1.75-7.5 years), during which 76 patients (43.2%) experienced recurrence.Tumor number ≥ 2, body mass index (BMI) ≥ 24.0 kg/m², and concurrent endometriosis were significantly associated with recurrence (p < 0.05). Three factors were recognized as independent predictors of recurrence through multivariate logistic regression. The area under the receiver operating characteristic curve of the constructed nomogram model was 0.791 (95% confidence interval: 0.724-0.858). Decision curve analysis showed a good clinical net benefit, and calibration curves showed a high consistency between the predicted and actual recurrence probabilities.

Patients with CUL who have risk factors require close monitoring for postoperative recurrence. The developed nomogram model exhibited good predictive performance, thereby offering a reference for treatment choice and recurrence prediction.