BACKGROUND: Reduction mammaplasty is a highly effective procedure for treatment of symptomatic macromastia. Prediction of resection weight is important for the surgeon and the patient, but none of the current prediction models is widely accepted. Insurance carriers are arbitrarily using resection weight to determine medical necessity, despite published literature supporting that resection weight does not correlate with symptomatic relief. What is the most accurate method of predicting resection weight and what is its role in breast reduction surgery? METHODS: The authors conducted a retrospective review of patients who underwent reduction mammaplasty at a single institution from 2012 to 2017. A senior biostatistician performed multiple regression analysis to identify predictors of resection weight, and linear regression models were created to compare each of the established prediction scales to actual resected weight. Patient outcomes were evaluated. RESULTS: Three-hundred fourteen patients were included. A new prediction model was created. The Galveston scale performed the best (R = 0.73; p < 0.001), whereas the Schnur scale performed the worst (R = 0.43; p < 0.001). The Appel and Descamps scales had variable performance in different subcategories of body mass index and menopausal status (p < 0.01). Internal validation confirmed the Galveston scale's best predictive value; 38.6 percent and 28.9 percent of actual breast resection weights were below Schnur prediction and 500-g minimum, respectively, yet 97 percent of patients reported symptomatic improvement or relief. CONCLUSIONS: The authors recommend a patient-specific and surgeon-specific approach for prediction of resection weight in breast reduction. The Galveston scale fits the best for older patients with higher body mass indices and breasts requiring large resections. Medical necessity decisions should be based on patient symptoms, physical examination, and the physician's clinical judgment. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.
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