3.9 Article

Complications following Nipple-Sparing Mastectomy and Immediate Acellular Dermal Matrix Implant-based Breast Reconstruction-A Systematic Review and Meta-analysis

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LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/GOX.0000000000001625

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  1. Research Unit at the Department of Plastic and Reconstructive Surgery, Odense University Hospital, Denmark

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Background: Acellular dermal matrix was introduced in breast reconstruction in 2001 and is gradually becoming a standard component for immediate breast reconstruction and nipple-sparing mastectomy. The reconstructive technique allows for improved aesthetic outcomes. However, there seems to be uncertainty regarding complication rates. The aim of this review was to systematically evaluate complication rates related to this method. Methods: This systematic review was conducted according to the recommendations outlined in the Cochrane Handbook for reviews and reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Relevant databases were searched for in the literature concerning the use of acellular dermal matrix in implant-based nipple-sparing mastectomy and immediate breast reconstruction. All studies underwent detailed quality assessment. Summarized outcome rates were computed using meta-analysis. Results: Nine of 1,039 studies were eligible for inclusion yielding 778 procedures. The quality was acceptable for all included studies. The meta-analysis found the rate of skin necrosis to be 11%, nipple necrosis 5%, infection in 12%, hematoma in 1%, treated seroma in 5%, explantation 4%, and unplanned return to the operating room in 9%. Conclusion: The use of acellular dermal matrix in nipple-sparing mastectomy and implant-based breast reconstruction can be done with acceptable complication rates in selected patients. We recommend future studies to include specific definitions when reporting complication rates. Furthermore, future studies should elaborate on demographic characteristics of the included study samples and include predictor analysis to enhance knowledge of high risk patients.

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