Development of a Classification Tree to Predict Implant-Based Reconstruction Failure with or without Postmastectomy Radi
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ORIGINAL ARTICLE – RECONSTRUCTIVE ONCOLOGY
Development of a Classification Tree to Predict Implant-Based Reconstruction Failure with or without Postmastectomy Radiation Therapy for Breast Cancer Jie Jane Chen, MD1, Rie von Eyben, MS2, Paulina M. Gutkin, BS2, Erin Hawley, BA2, Frederick M. Dirbas, MD3, Gordon K. Lee, MD4, and Kathleen C. Horst, MD2 1
Harvard Medical School, Boston, MA; 2Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA; 3Department of General Surgery, Stanford University School of Medicine, Stanford, CA; 4Department of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA
ABSTRACT Purpose. The aim of this study was to determine the complications, incidence, and predictors of implant-based reconstruction failure (RF) among patients treated with mastectomy for breast cancer. Methods. We retrospectively reviewed 108 patients who underwent mastectomy, tissue expander, and implant-based breast reconstruction with or without radiation therapy (RT) at our institution (2000–2014). Descriptive statistics determined complication incidences, with major complications defined as any complications requiring surgical intervention or inpatient management. Chi square and Fisher’s exact tests determined differences in RF incidences, defined as implant loss. Logistic regression analyses identified predictors of RF. Results. Median follow-up was 42.5 months. Sixty patients (55.6%) experienced major complications. Overall, 27 patients (25%) experienced RF. Incidences of RF were significantly increased in patients who had any major complication (43.3% vs. 2.1%; p \ 0.0001), especially infection (61.3% vs. 10.4%; p \ 0.0001), delayed wound healing (83.3% vs. 21.7%; p = 0.004), and implant exposure (80.0% vs. 19.4%; p = 0.0002). Receiving RT, but not timing of RT, significantly predicted RF [odds ratio (OR) 4.00, 95% confidence interval (CI) 1.11–14.47; p = 0.03].
Ó Society of Surgical Oncology 2020 First Received: 21 May 2020 Accepted: 10 August 2020 K. C. Horst, MD e-mail: [email protected]
On multivariable analysis, infection (OR 7.69, 95% CI 2.12–27.89; p = 0.002) and delayed wound healing (OR 17.86, 95% CI 1.59–200.48; p = 0.02) independently predicted for RF. Our newly developed classification tree, which includes stepwise assessment of major infection, delayed wound healing, implant exposure, age C 50 years, and total number of lymph nodes removed C 10, accurately predicted 74% of RF events and 75% of non-RF events. Conclusions. Infection or delayed wound healing requiring surgical intervention or hospitalization and receipt of RT, but not radiation timing, were significant predictors of RF. Our classification tree demonstrated [ 70% accuracy for stepwise prediction of RF.
Since implementation of the Women’s Health and Cancer Rights Act, which mandated coverage of breast reconstruction after mastectomy for breast cancer by all insurers in the United States (US), the incidence of immediate postmastectomy breast reconstruction has increased
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