Predicting Device-Related Complications Among Women Undergoing Breast Implant Surgeries

Background Breast implant surgery is a high-volume procedure, yet predicting device-related complications requiring revision surgery remains challenging, with current registry-based methods lacking accuracy. This research aims to develop and validate machine learning (ML) models for predicting revision due to complications following primary breast implant surgery, using a large multi-centre data from the Australian Breast Device Registry (ABDR). Methods This cohort study analysed ABDR data from January 2012 to December 2023, including 106,413 cosmetic and 22,107 reconstructive primary implant procedures across 239 sites in Australia. Demographic, clinical, surgical, device-related, and socioeconomic factors were evaluated. Separate ML models were developed for the cosmetic and reconstructive (further stratified by direct to implant and two-stage tissue expander to implant) cohorts. The primary outcome was revision surgery due to any complications. Exploratory analyses looked at individual types of complications. Model performance was assessed using area under the receiver operating characteristic curve (AUC) and other metrics. Results In the cosmetic cohort, the Random Forest (RF) model achieved the highest performance (AUC = 0.92), with key predictors of complication revisions including implant manufacturer (Allergan), implant shell type (macrotextured) and older patient age. In the reconstructive cohort, the RF model similarly performed best (AUC = 0.91), with implant manufacturer (Allergan), incision site, use of acellular dermal matrix, older age and concurrent mastectomy as significant predictors of complication revisions. Socioeconomic advantage was also positively associated with revision in both groups. Conclusions This large multi-centre study demonstrates that ML models can accurately predict revisions due to complications following breast implant surgery, highlighting patient, surgical, device, and contextual factors as key contributors. Integration of these models into registry workflows may help improve patient safety and regulatory oversight.