Nomograms can predict 5-year lymphedema risk

Estimating the risk of lymphedema after axillary dissection for breast cancer is difficult, but a group of researchers has used a large data set to develop graphical statistical tools known as nomograms to help predict 5-year probability.

Jose L. Bevilacqua, MD, PhD, a surgical oncologist at Hospital Sírio-Libanês in São Paulo, Brazil, and colleagues collected clinicopathologic features from a prospective cohort study of 1,054 women with unilateral breast cancer. All subjects had undergone axillary dissection, between August 2001 and November 2002, as part of their surgical treatment

The investigators determined the cumulative incidence of lymphedema and predicted the risk of developing the condition based on the available data at each of three timepoints: preoperatively, within 6 months postoperatively, and 6 months or more postoperatively. They defined lymphedema as a volume difference of at least 200 mL between arms at 6 months or more after surgery.

As reported at the American Society of Clinical Oncology 2011 Breast Cancer Symposium, held September 8-10, 2011, in San Francisco, California, Bevilacqua and associates found the 5-year cumulative incidence of lymphedema to be 30.3% (www.asco.org/ASCOv2/Meetings/Abstracts?&vmview=abst_detail_view&confID=111&abstractID=86647). Independent risk factors for the condition were age, body mass index (BMI), ipsilateral arm chemotherapy infusions, level of axillary dissection, location of radiotherapy field, development of postoperative seroma, infection, and early edema.

The predicted 5-year risks calculated by the nomograms were more than 70% accurate for all three timepoints assessed—70.6% for the preoperative model, 72.9% for the within-6-months-postoperatively model, and 73.6% for the 6-months-or-more-postoperatively model.

The investigators concluded that the proposed nomograms can help clinicians and patients predict the 5-year probability of lymphedema after axillary dissection for breast cancer, knowledge that could identify patients who may benefit from early intervention.

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