The Anatomical Breast Burden Model: A Schnur Scale Alternative for Identifying Need for Therapeutic Reduction Mammaplasty
Year: 2025
Type: article
Study design: Observational Study
Abstract: The Schnur sliding scale is used by many insurance companies to determine eligibility for reduction mammaplasty. However, it overlooks anatomical features, symptoms, and physical findings, leading to inequities in coverage determinations. The aim of the study was to develop and validate the anatomical breast burden (ABB) model—a 0-6-point scoring system that quantifies the burden of breasts using breast measurements, physical findings, and symptoms—as an alternative tool to the Schnur scale. A retrospective chart review was conducted for 84 patients who underwent breast reduction at a single academic center. Resection weights, Schnur threshold weights, demographics, and preoperative breast measurements were recorded. ABB cutoff values for sternal notch-to-nipple, nipple-to-inframammary fold, and base width distances were determined statistically, and a score was assigned to each patient. Spearman correlation tested the associations between ABB score and BMI, body surface area, breast measurements, ptosis, resection weight, and the discrepancy between actual resection weight and the Schnur threshold weight. Among patients with an ABB score of 6 (very severe burden), 44% were ineligible for coverage under the Schnur scale, whereas 33% of patients with an ABB score of 2 (mild-to-moderate burden) were eligible. When stratified as low (0-2) and high (3-6) burden, the Schnur scale showed 47.5% sensitivity and 66.7% specificity for identifying patients with significant breast burden. Our model demonstrated stronger correlations with anatomical measurements and resection weight than the Schnur scale. The ABB model better reflects true breast burden and offers a more equitable tool for guiding coverage decisions in breast reduction. Level of Evidence: 3 (Therapeutic)
Authors: Eliana Jolkovsky, Meghan N Miller, Weston McClain, Samantha Rabinovich, Ainaz Dory Barkhordarzadeh, Derrick Lin, Stacy Piva, Addee Lerner, Ginger C Slack
Institutions: University of California, Los Angeles, UCLA Health
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,051
OpenAlex ID: W4417449642
