A Validated Multi-Institutional Approach To Optimizing Outcomes Of Reduction Mammoplasty: A Critical Analysis Of 7,068 Patients.
Pablo A. Baltodano, MD1, M. Eliann Reinhardt, BM, BS2, Ashar Ata, PhD1, Wesley Wong, BS1, Malcolm Z. Roth, MD1, Ashit Patel, MBChB1.
1Albany Medical Center, Albany, NY, USA, 2Johns Hopkins University, Baltimore, MD, USA.
Purpose: To develop a validated risk model to identify patients at high risk for postoperative surgical site morbidity (SSM) after reduction mammoplasty.
Methods: Retrospective review of all females undergoing reduction mammoplasties from the ACS-NSQIP2 2005-2012 data. SSM included surgical site infection (SSI) and wound disruption events. Stepwise multivariable logistic regression was used to identify the risk factors associated with SSM. The model was validated using bootstrap replications (n=100) and Hosmer-Lemeshow test. The model was converted into a clinical risk score (CRS) predictive of SSM.
Results: We identified 7,068 reduction mammoplasties. Rate of 30-day SSM was 3.98%. Independent risk factors included resident participation(OR=1.5,95%CI:1.1-2.0,p=0.004), BMI(for every 5 unit increase: OR=1.3,95%CI:1.1-1.4,p<0.001), smoking(OR=1.6,95%CI:1.1-2.4,p=0.014), steroid use(OR=3.5,95%CI:1.4-8.4,p=0.006), and operation in 3rd quarter of the year(OR=1.5,95%CI:1.1-1.9,p=0.014). The factors were integrated into a CRS ranging from 0-16 (Table1). Predicted probability of SSM associated with each risk score was estimated (Table2). Predicted and observed risks of SSM were highly comparable (Figure1).
Conclusion: We present the first validated risk stratification tool for predicting 30-day SSM following reduction mammoplasty using data available to the clinician.
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