Cano-Lallave Enrique, Frutos-Bernal Elisa, Anciones-Polo María, Serrano-Sánchez Esther, Rodríguez-Guerrero Ian, Cuenda-Gamboa Paula, Muñoz-Bellvis Luis, Eguía-Larrea Marta
Rehabilitation Service, University Hospital of Salamanca, Salamanca, Spain.
Department of Statistics, University of Salamanca, Salamanca, Spain.
J Surg Oncol. 2025 Jun;131(8):1628-1636. doi: 10.1002/jso.28146. Epub 2025 May 13.
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.
多模式乳腺癌治疗继发的淋巴水肿是一种相对常见的并发症，会显著影响患者的生活质量。尽管已确定了多个相关风险因素，但准确评估个体风险仍具有挑战性。本研究旨在开发整合患者特征、肿瘤属性和治疗方式的预测工具，以优化临床监测、加强预防并实现早期诊断。
对2016年1月至2021年12月期间转诊至康复服务淋巴水肿科并接受乳腺癌淋巴结清扫术的309例患者的数据进行分析。收集的变量包括患者人口统计学信息、肿瘤临床病理特征和治疗细节。开展了淋巴水肿发病率研究，并辅以单因素和多因素回归分析以确定风险因素。绘制了列线图以预测高危患者，从而促进个性化的预防和管理策略。
淋巴水肿的累积发病率为18.4%。独立风险因素包括高体重指数、久坐的生活方式、阳性淋巴结数量（N分期）以及放疗，尤其是针对乳腺、腋窝和锁骨上下区域的放疗。逻辑回归模型的ROC曲线下面积（AUC）为0.75，校准可接受，验证了该预测模型。
所开发的预测工具为医护人员提供了一种识别淋巴水肿高危患者的方法，支持个性化预防和管理。
