Population-based study shows logistic regression identifies revision predictors in women post-bariatric surgery, suggesting a useful tool for surgical planning.
Background Previous studies suggest higher revision rates after primary breast augmentation after massive weight loss (MWL), yet specific risk factors remain unclear. Robust predictive models are lacking. Objectives The study aimed to create a nomogram to predict five-year revision risk after primary breast augmentation in women with massive weight loss, providing a clinical tool to guide surgical decision-making. Methods A nationwide, population-based study was conducted within the Breast Reconstruction After Bariatric Surgery project (ClinicalTrials.gov NCT07059104). Data from the Scandinavian Obesity Surgery Registry and the Swedish Breast Implant Registry (2008–2022) were linked with national healthcare and prescription databases. Univariable logistic regression and group LASSO with 1,000 bootstrap iterations were applied to identify predictors of revision within five years. A final multivariable model informed the development of a clinical nomogram. Results Among 810 women undergoing 1,604 primary breast augmentations after massive weight loss, 84 (10%) required revision during a median follow-up of 5.5 years. Revisions were associated with smaller implant volumes (p = 0.032) and shorter intervals between bariatric surgery and augmentation (p = 0.042). In bootstrap analyses, implant volume (92% selection), surgical timing, and medications impairing wound healing were consistently retained. The final model included these three predictors, forming the basis of the nomogram. Conclusions Implant volume, timing of augmentation, and relevant medications were independent predictors for revision. The nomogram provides a tool to support surgical planning and patient counselling in this high-risk population.
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