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SN-N and N-IMF Predict Breast Reduction Resection Weights and Attainment of Insurance-mandated Minimums
Xiao Zhu, MD, Jennifer Hall, BS, Michael Gimbel, MD, Vu Nguyen, MD.
University of Pittsburgh Medical Center, Pittsburgh, PA, USA.

Background: Despite evidence that the symptomatic benefits of reduction mammaplasty transcend resection weights, insurance providers still utilize the Schnur scale and 500g minimums for insurance coverage approval or denial. The aim of this study was to generate a novel equation based on pre-operative clinic variables, and compare its accuracy against other published formulas. An equation that accurately predicts both resection weights and attaining insurance minimums would be a useful clinical tool.
Methods: A single center retrospective review of bilateral reduction mammaplasty patients from two surgeon from 2007-2019 was performed. 268 patients were included. Multiple linear regression (MLR) and exponential models (EM) were created, and evaluated against published Appel, Descamps, and Galveston equations. Predictive performance relative to Schnur scale minimum, 500g minimum, and actual resection weights were assessed. Further subgroup analyses were performed based on WHO BMI classification.
Results: 71% and 68% of patients had resection weights above the Schnur and 500g minimums, respectively. While both MLR and EM (based off SN-N and N-IMF alone) performed significantly better than surgeons in predicting attainment of both the Schnur minimum (MLR 79% accuracy, p < 0.05; EM 82% accuracy, p < 0.001) and the 500g minimum (MLR 87% accuracy, p < 0.001; EM 89% accuracy, p < 0.001), EM outperformed MLR. The Appel, Descamps, and Galveston equations all performed significantly better in the 500g minimum category, but none were significantly better than surgeons in predicting attainment of Schnur minimums. MLR best correlated with resection weights (R2 = 0.746); however, EM had the lowest mean prediction error overall (172.8 ± 211.5g). On BMI subgroup analyses, increase in average resection weight outpaces increase in Schnur minimum, thus making it easier for heavier women to achieve the Schnur minimum. Moreover, the 500g minimum significantly discriminates against non-obese women (BMI < 30.0), as average resection weight for normal (BMI 18.5-24.9) and overweight (BMI 25.0-29.9) women were less than 500g.
Conclusions: Both the Schnur scale and 500g minimum are biased toward obese women. While this and other studies highlight inherent flaws to these minimums, third-party payers still frequently utilize them as a measure to approve or deny insurance coverage. The EM equation (39.023e0.0586(SN-N+N-IMF)) based off of pre-operative SN-N and N-IMF alone accurately predicts both actual resection weights, as well as attainment of these common insurance-mandated minimums. This may prove useful in the pre-operative setting to better estimate and counsel patients on resection weights and likelihood of insurance coverage.


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