Putting Together the Pieces: Development and Validation of a Predictive Risk Assessment Model and Calculator for Nipple-Sparing Mastectomy
Jordan D. Frey, MD, Ara A. Salibian, MD, Mihye Choi, MD, Nolan S. Karp, MD.
NYU Langone Health, New York, NY, USA.
Purpose: Optimizing outcomes and assessing appropriate candidates for breast reconstruction after nipple-sparing mastectomy (NSM) is an ongoing goal for plastic surgeons.
Methods: All patients undergoing NSM from 2006 to June 2018 were reviewed and randomly divided into test and validation groups. A logistic regression model calculating the odds ratio for any complication from 12 risk factors was derived from the test group while the validation group was used to validate this model.
Results: The test group was comprised of 537 NSMs (50.2%) with an overall complication rate of 27.2% (146 NSMs). The validation group was comprised of 533 NSMs (49.8%) with an overall complication rate of 22.9% (122 NSMs). A logistic regression model predicting overall complications was derived from the test group. NSMs in the test group were divided into deciles based on predicted risk in the model. Risk increased with probability decile; decile 1 was significantly protective while deciles 9 and 10 were significantly predictive for complications (p<0.0001). The relative risk in decile 1 was significantly decreased (0.39; p=0.006); relative risk in deciles 9/10 was significantly increased (2.71; p<0.0001). In the validation group, relative risk of any complication in decile 1 was decreased at 0.55 (p=0.057); relative risk in deciles 9/10 was significantly increased (1.89; p<0.0001). In a receiver operating characteristic curve, the area under the curve was 0.668 (p<0.0001), demonstrating diagnostic meaningfulness of the model.
Conclusions: We establish and validate a predictive risk model and calculator for NSM with far-reaching impact for surgeons and patients alike.
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