Optimizing Lymphedema Management After Breast Cancer: Predictive Risk Models in Clinical Practice

Lymphedema secondary to multimodal breast cancer treatment is a relatively common complication that significantly impacts patients' quality of life. Despite identifying several associated risk factors, accurately assessing individual risk remains challenging. This study aims to develop predictive tools integrating patient characteristics, tumor attributes, and treatment modalities to optimize clinical surveillance, enhance prevention, and enable earlier diagnosis.

Data were analyzed from 309 patients referred to the Lymphedema Unit of Rehabilitation Service who underwent lymphadenectomy for breast cancer between January 2016 and December 2021. Collected variables included patient demographics, tumor clinicopathological features, and treatment details. A lymphedema incidence study was conducted, complemented by univariate and multivariate regression analyses to identify risk factors. A nomogram was developed to predict high-risk patients, facilitating personalized prevention and management strategies.

The cumulative incidence of lymphedema was 18.4%. Independent risk factors included high body mass index, sedentary lifestyle, number of positive nodes (N stage), and radiotherapy, particularly targeting the breast, axilla, and supra-infraclavicular regions. The logistic regression model demonstrated an area under the ROC curve (AUC) of 0.75, with acceptable calibration, validating the predictive model.

The predictive tools developed provide healthcare professionals with a means to identify patients at elevated risk of lymphedema, supporting individualized prevention and management.