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

  • Breast Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

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.

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Funding

The described study was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, Award No. TL1TR002546, and American Cancer Society Grant No. CRP-17-112-06-COUN.

Author information

Authors and Affiliations

  1. Department of Surgery, Tufts Medical Center, Boston, MA, USA

    Michael M. Jonczyk MD, MSCTS

  2. Clinical and Translational Science Graduate Program, Tufts University’s Graduate School of Biomedical Sciences, Boston, MA, USA

    Michael M. Jonczyk MD, MSCTS & Abhishek Chatterjee MD, MBA

  3. Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA

    Carla Suzanne Fisher MD

  4. Plastic Surgery of Southern New England, PC, Fall River, MA, USA

    Russell Babbitt MD

  5. Department of Medicine and Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA, USA

    Jessica K. Paulus ScD & Karen M. Freund MD, MPH

  6. Department of Breast Oncology, Moffitt Cancer Center, Tampa, FL, USA

    Brian Czerniecki MD, PhD

  7. Department of Surgery, Washington University School of Medicine, St Louis, MO, USA

    Julie A. Margenthaler MD

  8. Division of Plastic and Reconstructive Surgery, Emory University School of Medicine, Atlanta, GA, USA

    Albert Losken MD

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  1. Michael M. Jonczyk MD, MSCTS
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Contributions

The authors would like to thank Tigran Melkonian, MS (Northeastern University), for his invaluable contributions in the development of the Shiny risk calculator and online web platform. They also thank Jason Nelson, MPH [Tufts Medical Center, Predictive Analytics and Comparative Effectiveness (PACE) Center] and Dr David M. Kent, MD, CM, MSC (Tufts University Graduate School: Director, Clinical and Translational Science Graduate Program) for their invaluable feedback and guidance in the statistical methodology quality assurance measures.

Corresponding author

Correspondence to Michael M. Jonczyk MD, MSCTS.

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Disclosure

Michael M. Jonczyk, Carla Suzanne Fisher, Russell Babbitt, Jessica K. Paulus, Karen M. Freund, Brian Czerniecki, Julie A. Margenthaler, Albert Losken, and Abhishek Chatterjee report no conflicts of interest.

Role of the funder/sponsor

The ACS NSQIP was the sole source of data used for this study. They have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors. This project does not represent the views or plans of the ACS or the ACS NSQIP. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health.

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Jonczyk, M.M., Fisher, C.S., Babbitt, R. et al. Surgical Predictive Model for Breast Cancer Patients Assessing Acute Postoperative Complications: The Breast Cancer Surgery Risk Calculator. Ann Surg Oncol 28, 5121–5131 (2021). https://doi.org/10.1245/s10434-021-09710-8

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