# Enhancing Volume Precision in Breast Reconstruction: A BMI-Based Model for Predicting Flap Weight in Profunda Artery Perforator Flaps

## TL;DR

This study shows how body mass index (BMI) can predict flap weight in breast reconstruction using a specific surgical technique, helping surgeons plan better.

## Contribution

A novel BMI-based predictive model for flap weight in profunda artery perforator (PAP) flaps is introduced.

## Key findings

- BMI is a statistically significant predictor of PAP flap weight (p < 0.001).
- The predictive model is flap weight (g) = (19.252 × BMI) − 143.572.
- Patients with low BMI can still achieve adequate flap volumes for reconstruction.

## Abstract

Background: In recent years, the profunda artery perforator (PAP) flap has gained recognition as a viable technique in autologous breast reconstruction, demonstrating low donor site morbidity and favorable reconstructive outcomes. This study aims to highlight the recent refinements using the PAP flap for breast reconstruction focusing on preoperative volume prediction. Methods: A retrospective study was conducted to evaluate the outcomes of breast reconstruction surgeries using the PAP flap at our institution between May 2018 and December 2022. A total of 114 PAP flaps performed in 96 patients were included. Statistical analysis was performed to analyze flap volume in relation to body mass index (BMI). Surgical details, such as donor site-related complications and patient characteristics, were also collected. Results: BMI was identified as a statistically significant predictor (p < 0.001) for flap weight in the resulting model. The average BMI observed was 23.0, while the mean flap weight was 304.2 g. The predictive model for flap weight was determined as follows: flap weight (g) = (19.252 × BMI) − 143.572. This model underscores the relationship between BMI and flap weight, quantifying the influence of BMI on flap weight prediction. Conclusions: Our study indicates that it is feasible to harvest an adequate volume for breast reconstruction even in patients with a low BMI. To facilitate preoperative prediction of PAP flap volume, we developed an algorithm designed to enhance preoperative planning and estimate the need for supplementary procedures to achieve the desired volume.