Predicting Complications in Breast Reconstruction: Development and Prospective Validation of a Machine Learning Model.

IMPORTANCE

Necrosis of the nipple-areolar complex (NAC) is the Achilles heel of nipple-sparing mastectomy (NSM), and it can be difficult to assess which patients are at risk of this complication (Ann Surg Oncol 2014;21(1):100-106).

OBJECTIVE

To develop and validate a model that accurately predicts NAC necrosis in a prospective cohort.

DESIGN

Data were collected from a retrospectively reviewed cohort of patients who underwent NSM and immediate breast reconstruction between January 2015 and July 2019 at our institution, a high -volume, tertiary academic center. Preoperative clinical characteristics, operative variables, and postoperative complications were collected and linked to NAC outcomes. These results were utilized to train a random-forest classification model to predict necrosis. Our model was then validated in a prospective cohort of patients undergoing NSM with immediate breast reconstruction between June 2020 and June 2021.

RESULTS

Model predictions of NAC necrosis in the prospective cohort achieved an accuracy of 97% (95% confidence interval [CI], 0.89-0.99; P = 0.009). This was consistent with the accuracy of predictions in the retrospective cohort (0.97; 95% CI, 0.95-0.99). A high degree of specificity (0.98; 95% CI, 0.90-1.0) and negative predictive value (0.98; 95% CI, 0.90-1.0) were also achieved prospectively. Implant weight was the most predictive of increased risk, with weights greater than 400 g most strongly associated with NAC ischemia.

CONCLUSIONS AND RELEVANCE

Our machine learning model prospectively predicted cases of NAC necrosis with a high degree of accuracy. An important predictor was implant weight, a modifiable risk factor that could be adjusted to mitigate the risk of NAC necrosis and associated postoperative complications.