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Risk factors for capsular contracture after breast reconstruction with tissue expanders and silicone implants in nonirradiated patients: A retrospective observational cohort study.

Yoshiko IwahiraGojiro NakagamiHiromi Sanada
Published in: Medicine (2022)
Breast reconstruction surgery with tissue expanders and silicone implants is widely performed; however, risk factors for late complications such as capsular contracture have not been fully investigated despite their high prevalence. We investigated the association between expander and implant positions and the development of capsular contracture in patients who underwent breast reconstruction surgery over 10 years previously. In this retrospective observational study, we analyzed 239 patients, among whom 69 (28.9%) had developed capsular contracture of Baker Classification grade II or higher. The position of the expander was classified into six categories based on the inferior margin of the healthy breast. The position of the implant was defined as an upward movement from the position of the expander and was classified into three categories based on the inferior margin of the breast at the time of expander insertion. Using multivariate logistic regression analysis, we assessed whether the misalignment of the expander and silicone implant positions affected capsular contracture development. Both expander and implant positions were significantly different between the groups. The odds ratios, adjusted for confounding variables, were 3.4 and 5.2 for an expander position of 1 and 2 lateral fingers upward, respectively, and 4.8 and 45.4 for a silicone implant position of 2 and 3 lateral fingers upward, respectively. We identified malposition of expanders and silicone implants as risk factors for developing capsular contracture. Correct insertion and adequate dilatation of the expander in the correct position could reduce the risk for capsular contracture.
Keyphrases
  • breast reconstruction
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  • minimally invasive
  • ejection fraction
  • newly diagnosed
  • chronic kidney disease
  • peritoneal dialysis
  • machine learning
  • deep learning
  • patient reported