Surgical Predictive Model for Breast Cancer Patients Assessing Acute Postoperative Complications: The Breast Cancer Surgery Risk Calculator.

BACKGROUND Prognostic tools, such as risk calculators, improve the patient-physician informed decision-making process. These tools are limited for breast cancer patients when assessing surgical complication risk preoperatively. OBJECTIVE In this study, we aimed to assess predictors associated with acute postoperative complications for breast cancer patients and then develop a predictive model that calculates a complication probability using patient risk factors. METHODS We performed a retrospective cohort study using the National Surgical Quality Improvement Program (NSQIP) database from 2005 to 2017. Women diagnosed with ductal carcinoma in situ or invasive breast cancer who underwent either breast conservation or mastectomy procedures were included in this predictive modeling scheme. Four models were built using logistic regression methods to predict the following composite outcomes: overall, infectious, hematologic, and internal organ complications. Model performance, accuracy and calibration measures during internal/external validation included area under the curve, Brier score, and Hosmer-Lemeshow statistic, respectively. RESULTS A total of 163,613 women met the inclusion criteria. The area under the curve for each model was as follows: overall, 0.70; infectious, 0.67; hematologic, 0.84; and internal organ, 0.74. Brier scores were all between 0.04 and 0.003. Model calibration using the Hosmer-Lemeshow statistic found all p-values to be > 0.05. Using model coefficients, individualized risk can be calculated on the web-based Breast Cancer Surgery Risk Calculator (BCSRc) platform ( www.breastcalc.org ). CONCLUSION We developed an internally and externally validated risk calculator that estimates a breast cancer patient's unique risk of acute complications following each surgical intervention. Preoperative use of the BCSRc can potentially help stratify patients with an increased complication risk and improve expectations during the decision-making process.