Massachusetts Chapter of the American College of Surgeons

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Predicting acute post-operative complications: the Breast Cancer Surgery Risk Calculator
Michael M. Jonczyk, MD, MS1,2, Abhishek Chatterjee, MD, MBA2
1Department of Surgery, Lahey Hospital & Medical Center, Burlington, MA; 2Department of Surgery, Tufts Medical Center, Boston, MA

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 pre-operatively. Here 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.

We performed a retrospective cohort study using the NSQIP database from 2005-2017. 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, the brier score and Hosmer-Lemeshow statistic; respectively.

A total of 163,613 women met inclusion criteria. Area under the curve for each model was: Overall 0.70, Infectious 0.67, Hematologic 0.84, and Internal Organ 0.74. Brier scores were all between 0.04-0.003. Model calibration using the Hosmer- Lemeshow statistic found all p-values >0.05. Using model coefficients, individualized risk can be calculated on the web-based breast cancer surgical risk calculator (BCSRc) platform;

We developed an internally and externally-validated risk calculator that estimates a breast cancer patientís 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.

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