Ian Chow1, Philip J Hanwright1, Nora M Hansen2, Solmaz N Leilabadi1, John Y S Kim1. 1. Division of Plastic and Reconstructive Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. 2. Lynn Sage Comprehensive Breast Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Abstract
BACKGROUND: Recent healthcare legislation has made unplanned hospital readmission an important metric of health care quality, and current efforts center on reducing this complication in order to avoid fiduciary penalties. OBJECTIVE: There is currently a paucity of data delineating risk factors for readmission following mastectomy. To this end, we sought to develop a predictive model of unplanned readmissions following mastectomy. METHODS: The 2011 and 2012 National Surgical Quality Improvement Program (NSQIP) datasets were retrospectively queried to identify patients who underwent mastectomy. Multivariate logistic regression modeling was used to identify risk factors for readmission. RESULTS: Of 21,271 patients meeting inclusion criteria, 1,190 (5.59%) were readmitted. The most commonly cited reasons for readmission included surgical site complications (32.85%), infection not localized to the surgical site (2.72%), and venous thromboembolism (4.39%). Independent predictors of readmission included BMI, active smoking status, and skin-sparing mastectomy. Significantly, concurrent breast reconstruction and bilateral mastectomy were not independent predictors of readmission. CONCLUSIONS: This is the first study of readmission rates after mastectomy. Awareness of specific risk factors for readmission, particularly those that are modifiable, may serve to identify and manage high risk patients, aid in the development of pre- and postoperative clinical care guidelines, and ultimately improve patient care.
BACKGROUND: Recent healthcare legislation has made unplanned hospital readmission an important metric of health care quality, and current efforts center on reducing this complication in order to avoid fiduciary penalties. OBJECTIVE: There is currently a paucity of data delineating risk factors for readmission following mastectomy. To this end, we sought to develop a predictive model of unplanned readmissions following mastectomy. METHODS: The 2011 and 2012 National Surgical Quality Improvement Program (NSQIP) datasets were retrospectively queried to identify patients who underwent mastectomy. Multivariate logistic regression modeling was used to identify risk factors for readmission. RESULTS: Of 21,271 patients meeting inclusion criteria, 1,190 (5.59%) were readmitted. The most commonly cited reasons for readmission included surgical site complications (32.85%), infection not localized to the surgical site (2.72%), and venous thromboembolism (4.39%). Independent predictors of readmission included BMI, active smoking status, and skin-sparing mastectomy. Significantly, concurrent breast reconstruction and bilateral mastectomy were not independent predictors of readmission. CONCLUSIONS: This is the first study of readmission rates after mastectomy. Awareness of specific risk factors for readmission, particularly those that are modifiable, may serve to identify and manage high risk patients, aid in the development of pre- and postoperative clinical care guidelines, and ultimately improve patient care.
Authors: Indranil Sinha; Andrea L Pusic; Edwin G Wilkins; Jennifer B Hamill; Xiaoxue Chen; Hyungjin M Kim; Gretchen Guldbrandsen; Yoon S Chun Journal: Plast Reconstr Surg Date: 2017-01 Impact factor: 4.730
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Authors: Carme Miret; Laia Domingo; Javier Louro; Teresa Barata; Marisa Baré; Joana Ferrer; Maria Carmen Carmona-García; Xavier Castells; Maria Sala Journal: BMC Health Serv Res Date: 2019-12-05 Impact factor: 2.655