Redefining the Role of Resection Weight Prediction in Breast Reduction Surgery
Stefanos Boukovalas, MD, Pablo L. Padilla, MD, Jacquelynn P. Tran, BS, Heidi Spratt, PhD, Linda G. Phillips, MD.
The University of Texas Medical Branch, Galveston, TX, USA.
PURPOSE: Amount of breast tissue removed in reduction mammaplasty does not determine symptom relief, however, insurance companies continue to arbitrarily use this as a criterion to justify medical necessity. The question remains: are current prediction scales accurate and what's their role in patient care?
METHODS: A retrospective review was performed of all patients who underwent reduction mammaplasty by a single surgeon from 2012-2016. Multivariate analysis was performed to identify predictors of resection weight. Linear regression models were created to compare each of the established prediction scales to actual resected weight. Patient outcomes were evaluated.
RESULTS: 183 patients were included. Schnur scale performed the worst (R2=0.43) and Appel scale performed the best (R2=0.70) (P<0.01). Appel and Descamps scales had variable performance in the different obesity categories (P<0.01). Multivariate analysis demonstrated that specific breast measurements, body mass index and age were predictive of breast tissue resection weight. A new Galveston prediction scale is described. 38.6% and 28.9% of actual breast resection weights were below the Schnur prediction and 500-grams minimum respectively, which are the most common weight requirements used by insurance carriers. Nevertheless, 96% of patients reported symptomatic relief.
CONCLUSION: Prediction scales should be used with caution when determining medical necessity of breast reduction. Schnur scale and 500-grams minimum resection are unreliable tools and should be avoided by insurance carriers. Prediction scales are still valuable, as a counseling tool for the patient and as an intraoperative guide for the plastic surgeon. An algorithmic approach for accurate resection weight prediction is provided.
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