“Verifying Predictive Models for Determining Final Implant Volume in Two-Stage Implant-Based Breast Reconstruction”
Background: – Most breast reconstructions following mastectomy utilize a two-stage tissue expander (TE) to implant approach. Prior studies on prepectoral and subpectoral breast reconstructions have identified formulas for predicting final implant size using TE size and final fill. The aim of this study is to test the accuracy of these models. Methods: – A retrospective chart review of patients that underwent two-stage TE to implant breast reconstruction within the Duke University Health System between 2021 and 2024 was performed. Demographic, oncologic, and reconstructive data were collected. The equations 26.6 + 0.38*(TE final fill) + 0.61*(TE size) for prepectoral and 71.7 + 0.8*(TE final fill) + 0.1*(TE size) for subpectoral reconstructions were used to calculate predicted implant sizes, which were then compared to actual implant sizes. Results: – 70 prepectoral patients (117 breasts) and 39 subpectoral patients (67 breasts) met criteria for inclusion. All patients had at least 5 months of postoperative follow-up. The mean predicted implant size was 23cc less than the mean actual size (477 vs. 500cc). The root-mean-square errors (RMSEs) for prepectoral and subpectoral reconstructions were 73.6 and 54.9cc, respectively. Conclusions: – In general, both models underpredicted final implant size. Depending on the implant profile, a 55-75cc difference equates to the models being accurate within 3-4 sizes for prepectoral and 2-3 sizes for subpectoral reconstructions, suggesting their potential use as a starting point to guide surgeon decision-making. Being able to predict final implant size more accurately will optimize surgical planning, decrease the number of implants ordered for each case, and reduce costs.
Duke Scholars
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Related Subject Headings
- Surgery
- 3203 Dentistry
- 3202 Clinical sciences
- 1103 Clinical Sciences
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Related Subject Headings
- Surgery
- 3203 Dentistry
- 3202 Clinical sciences
- 1103 Clinical Sciences