Michael M Jonczyk1,2, Carla Suzanne Fisher3, Russell Babbitt4, Jessica K Paulus5, Karen M Freund5, Brian Czerniecki6, Julie A Margenthaler7, Albert Losken8, Abhishek Chatterjee9. 1. Department of Surgery, Tufts Medical Center, Boston, MA, USA. mjonczyk@tuftsmedicalcenter.org. 2. Clinical and Translational Science Graduate Program, Tufts University's Graduate School of Biomedical Sciences, Boston, MA, USA. mjonczyk@tuftsmedicalcenter.org. 3. Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA. 4. Plastic Surgery of Southern New England, PC, Fall River, MA, USA. 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. 6. Department of Breast Oncology, Moffitt Cancer Center, Tampa, FL, USA. 7. Department of Surgery, Washington University School of Medicine, St Louis, MO, USA. 8. Division of Plastic and Reconstructive Surgery, Emory University School of Medicine, Atlanta, GA, USA. 9. Clinical and Translational Science Graduate Program, Tufts University's Graduate School of Biomedical Sciences, Boston, MA, USA.
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.
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.
Authors: Stacey A Carter; Genevieve R Lyons; Henry M Kuerer; Roland L Bassett; Scott Oates; Alastair Thompson; Abigail S Caudle; Elizabeth A Mittendorf; Isabelle Bedrosian; Anthony Lucci; Sarah M DeSnyder; Gildy Babiera; Min Yi; Donald P Baumann; Mark W Clemens; Patrick B Garvey; Kelly K Hunt; Rosa F Hwang Journal: Ann Surg Oncol Date: 2016-07-12 Impact factor: 5.344
Authors: Stephanie M Wong; Rachel A Freedman; Yasuaki Sagara; Fatih Aydogan; William T Barry; Mehra Golshan Journal: Ann Surg Date: 2017-03 Impact factor: 12.969
Authors: Christopher J Anker; Richard V Hymas; Ravinder Ahluwalia; Kristine E Kokeny; Vilija Avizonis; Kenneth M Boucher; Leigh A Neumayer; Jayant P Agarwal Journal: Breast J Date: 2015-03-15 Impact factor: 2.431
Authors: Claudia R Albornoz; Evan Matros; Clara N Lee; Clifford A Hudis; Andrea L Pusic; Elena Elkin; Peter B Bach; Peter G Cordeiro; Monica Morrow Journal: Plast Reconstr Surg Date: 2015-06 Impact factor: 4.730
Authors: S Jane Henley; Cheryll C Thomas; Denise Riedel Lewis; Elizabeth M Ward; Farhad Islami; Manxia Wu; Hannah K Weir; Susan Scott; Recinda L Sherman; Jiemin Ma; Betsy A Kohler; Kathleen Cronin; Ahmedin Jemal; Vicki B Benard; Lisa C Richardson Journal: Cancer Date: 2020-03-12 Impact factor: 6.921
Authors: Jacquelyn Dillon; Samantha M Thomas; Laura H Rosenberger; Gayle DiLalla; Oluwadamilola M Fayanju; Carolyn S Menendez; E Shelley Hwang; Jennifer K Plichta Journal: Ann Surg Oncol Date: 2021-07-26 Impact factor: 4.339