A comparison of acellular dermal matrices (ADM) efficacy and complication profile in women undergoing implant-based breast reconstruction: a systematic review and network meta-analysis.

INTRODUCTION

Breast cancer is the leading cause of cancer amongst women in the United Kingdom, with implant-based reconstruction (IBR) using Acellular Dermal Matrices (ADM) gaining popularity for post-mastectomy procedures. This study compares outcomes of different ADMs that are commonly used in women undergoing IBR, this was short and long-term complications.

METHODS

A systematic search of MEDLINE, Embase, CENTRAL, and CDSR databases was performed according to the PRISMA guidelines, focusing on women undergoing IBR with FlexHD, AlloDerm, Bovine, or Porcine ADMs. A network meta-analysis (NMA) was also conducted.

RESULTS

A total of 51 studies were captured by the search, of which 27 were included in the network meta-analysis. Alloderm was the most used ADM (54%), followed by Porcine (17%), Bovine (11%), DermAcell (11%), and FlexHD (7%). The mean follow-up was 27.8 months. The complication rates varied. Porcine ADMs had the highest rate of seroma formation (10.3%) and of haematoma formation (2.7%). AlloDerm FD had the highest rate of wound dehiscence (3.1%). Implant failure was highest in AlloDerm FD ADMs (11.8%), followed by Porcine ADMs (11.2%). Infections were most common in Porcine (11.2%) and AlloDerm FD ADMs (11.0%). Capsular contracture was rare across all ADM types, with no significant differences observed. In the NMA, AlloDerm FD showed significantly higher risks of infection, explantation, and wound dehiscence compared to AlloDerm RTU.

CONCLUSION

The overall complication profiles of ADMs used in IBR are similar, except for the higher risks associated with AlloDerm FD compared to RTU. These findings suggest that the choice of ADM may not significantly impact overall outcomes, except in specific cases like AlloDerm FD. Further high-quality, long-term, double-arm studies are necessary to confirm comparative profile of specific ADM types and to account for potential confounding variables through multivariable regression analysis.