Putting Together the Pieces: Development and Validation of a Risk-Assessment Model for Nipple-Sparing Mastectomy.

BACKGROUND

Optimizing outcomes and assessing appropriate candidates for breast reconstruction after nipple-sparing mastectomy is an ongoing goal for plastic surgeons.

METHODS

All patients undergoing nipple-sparing mastectomy from 2006 to June of 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, whereas the validation group was used to validate this model.

RESULTS

The test group was composed of 537 nipple-sparing mastectomies (50.2 percent), with an overall complication rate of 27.2 percent (146 nipple-sparing mastectomies). The validation group was composed of 533 nipple-sparing mastectomies (49.8 percent), with an overall complication rate of 22.9 percent (122 nipple-sparing mastectomies). A logistic regression model predicting overall complications was derived from the test group. Nipple-sparing mastectomies 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, whereas 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); the relative risk in deciles 9 and 10 was significantly increased (2.71; p < 0.0001). In the validation group, the relative risk of any complication in decile 1 was decreased at 0.55 (p = 0.057); the relative risk in deciles 9 and 10 was significantly increased (1.89; p < 0.0001). In a receiver operating characteristic curve analysis, the area under the curve was 0.668 (p < 0.0001), demonstrating diagnostic meaningfulness of the model.

CONCLUSION

The authors establish and validate a predictive risk model and calculator for nipple-sparing mastectomy with far-reaching impact for surgeons and patients alike.