Affiliations: [a] Division of Plastic and Reconstructive Surgery, Feinberg School of Medicine,
Northwestern University, Chicago, IL, USA | [b] Lynn Sage Comprehensive Breast Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
Correspondence:
[*]
Corresponding author: John Y.S. Kim, Division of Plastic and Reconstructive Surgery, Feinberg School of Medicine, Northwestern University, 675 North St. Clair Street, Galter Suite 19-250, Chicago 60611, IL, USA. Tel.: +1 312 695 6022; Fax: +1 312 695 5672; E-mail:[email protected]
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.
