Development and validation of a risk prediction model for lower limb lymphedema in postoperative cervical cancer patients.

Objective: Lower limb lymphedema (LLL) is a common postoperative complication in patients with cervical cancer. Here, we analyzed the independent risk factors of LLL and constructed a nomogram prediction model for the early detection of LLL in postoperative cervical cancer patients.

Methods: A cross-sectional study was conducted at a tertiary hospital in China between January 2020 and December 2023. A univariate analysis was carried out to determine the risk factors possibly related to LLL, and a logistic regression analysis was utilized to determine the independent risk factors related to LLL. Area under the receiver-operating characteristic curve (AUC) calibration plots and decision curve analysis were used to assess the performance of the nomogram model.

Results: Independent predictors for LLL risk included body mass index, hypertension, urinary tract infection, number of lymph nodes dissected, radiotherapy, chemotherapy, and functional exercise frequency. The clinical prediction model was established based on the above seven risk factors and showed superior predictive power both in the training cohort (AUC = 0.861) and the validation cohort (AUC = 0.837). The nomogram was well-calibrated. The decision curve analysis demonstrated that the LLL risk nomogram was clinically applicable.

Conclusion: The model has good discrimination and accuracy for LLL risk assessment, which can provide a reference for individualized clinical prediction of the risk of LLL in postoperative cervical cancer patients. Multicenter prospective trials are required to verify the predictive value of the model.