Comparison of Outcomes Following Prepectoral and Subpectoral Implants for Breast Reconstruction: Systematic Review and Meta-Analysis.
Vladimir MégevandMatteo ScampaHelen McEvoyDaniel F KalbermattenCarlo Maria OrangesPublished in: Cancers (2022)
(1) Background: Implant-based breast reconstruction following mastectomy helps to restore quality of life while aiming at providing optimal cosmetic outcomes. Both prepectoral (PP) and subpectoral (SP) breast implants are widely used to fulfill these objectives. It is, however, unclear which approach offers stronger postoperative benefits. (2) Methods: We performed a systematic review of the literature through PubMed, Cochrane Library, and ResearchGate, following the PRISMA guidelines. Quantitative analysis for postoperative pain as the primary outcome was conducted. Secondary outcomes included patient satisfaction and postoperative complications such as seroma, implant loss, skin necrosis, wound infection, and hematoma. (3) Results: Nine articles involving 1119 patients were retrieved. Our results suggested increased postoperative pain after SP implants and significantly higher rates of seroma following PP implants ( p < 0.05). Patient satisfaction was found to be similar between the two groups; however, the heterogeneity of measurement tools did not allow us to pool these results. The rates of implant loss, skin necrosis, wound infection, and hematoma showed no significant differences between the two cohorts. (4) Conclusion: Our data suggest that both implant placements are safe and effective methods for breast reconstruction following mastectomy. However, homogeneity in outcome measurements would allow one to provide stronger statistical results.
Keyphrases
- breast reconstruction
- soft tissue
- patient satisfaction
- postoperative pain
- end stage renal disease
- ejection fraction
- patients undergoing
- systematic review
- prognostic factors
- wound healing
- peritoneal dialysis
- randomized controlled trial
- single cell
- type diabetes
- surgical site infection
- adipose tissue
- machine learning
- deep learning
- metabolic syndrome
- weight loss
- meta analyses
- glycemic control
- patient reported