Preoperatively Predicting Reduction Mammaplasty Insurance Coverage
Xiao Zhu, M.D., Tarek Elgendy, M.D., Vu T. Nguyen, M.D..
University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
BACKGROUND: Dr. Schnur published a landmark paper in 1991, which was the first study to objectively evaluate indications for reduction mammaplasty. Many have rebuked the validity of its findings; however, the vast majority of insurance providers still utilize the Schnur 22nd percentile resection minimum for insurance approval or denial. Although multiple studies have sought to better predict resection weights, none have looked to predict insurance coverage based on commonly used resection minimums mandated by third party payers.
METHODS: This was a single center retrospective review of bilateral reduction mammaplasty patients from one surgeon performed from 2007-2018. 152 total patients were included in this study. Multiple linear regression, exponential modeling, and published Appel and Deschamps equations were used to predict resection weights based off commonly obtained pre-op office measurements. These predicted weights were then compared to the Schnur 22nd percentile cutoff and commonly used 500g minimum resection weight.
RESULTS: The surgeon was 70% accurate with respect to obtaining resection weights above the Schnur and 500g minimums. While multiple linear regression best predicted actual resection weights (R2=0.76), exponential modeling based off SN-N and N-IMF measurements alone (R2=0.70) performed the best in predicting whether or not the patient would meet Schnur and 500g resection minimums with accuracies of 85% and 89% respectively. Multiple linear regression had respective accuracies of 79% and 88%, Appel (R2=0.70) had respective accuracies of 80% and 88%, while Deschamps (R2=0.64) had respective accuracies of 77% and 82%.
CONCLUSIONS: Other studies have demonstrated similar surgeon accuracies of approximately 70% in obtaining resection minimum weights. While this performs well, it leaves the remaining 30% at risk for insurance coverage denial. When applied to this surgeon's patients, utilizing a simple exponential equation based off of SN-N and N-IMF can increase accuracies to at least 85% in predicting whether or not resection weights will meet Schnur or 500g minimums. This may prove useful for junior surgeons who may not have extensive experience in reduction mammaplasty, or senior surgeons in better counseling potential patients the likelihood of insurance coverage pre-operatively.
Back to 2019 Posters