Complications and Morbidity Following Breast Reconstruction--a Review of 16,063 Cases from the 2005-2010 NSQIP Datasets

Post-operative complications pose a significant set-back for patients undergoing breast reconstruction. This study aims to characterize factors associated with postoperative complications following breast reconstruction using the National Surgical Quality Improvement Program (ACS-NSQIP) database from 2005-2010. The 2005-2010 ACS-NSQIP databases were reviewed, identifying encounters for CPT codes including either implant-based reconstruction (immediate, delayed, and tissue expander) or autologous reconstruction (pedicled transverse rectus abdominus myocutaneous (TRAM), free TRAM, and latissimus dorsi flap with or without implant). Complications were characterized into three categories: major surgical complications, wound complications, and medical complications. During the study period 16,063 breast reconstructions were performed. Autologous reconstructions were performed in 20.7% of patients and implant-based in 79.3%. The incidence of major surgical complications was 8.4%, whereas the incidence of medical and wound complications was 1.6% and 3.5%, respectively. Independent risk factors for major surgical complications included: immediate and autologous reconstructions, obesity, smoking, previous percutaneous cardiac surgery (PCS), recent weight loss, bleeding disorder, recent surgery, ASA ≥ 3, intra-operative transfusion, and prolonged operative times. Risk factors for medical complications included: autologous reconstruction, obesity, tumor involving CNS, bleeding disorders, recent surgery, ASA ≥ 3, intra-operative transfusion, and prolonged operative times. This study characterizes the incidence of surgical and medical complications following breast reconstruction using a large, prospective multicentre dataset. Key identifiable risk factors associated with both surgical and medical morbidity included: autologous breast reconstruction, obesity, ASA ≥ 3, bleeding disorders, and prolonged operative time. Data derived from this cohort can be used to risk-stratify patients and to enhance perioperative decision-making.