Affiliation:
1. Division of Plastic Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
2. Onlume Surgical, Research Division, Madison, Wisconsin
Abstract
Abstract
Background Decreased autologous flap vascular perfusion can lead to secondary procedures. Fluorescence angiography during surgery reduces the probability of repeat surgery but suffers from interpretation variability. Recently, the OnLume Avata System was developed, which evaluates real-time vascular perfusion in ambient light. This study aims to predict complications in autologous breast reconstruction using measures of relative intensity (RI) and relative area (RA).
Methods Patients undergoing autologous breast reconstruction underwent intraoperative tissue perfusion assessment using the OnLume Avata System. Post-hoc image annotation was completed by labeling areas of the flap interpreted to be “Well Perfused,” “Questionably Perfused,” and “Under Perfused.” RIs and RAs were calculated for the marked areas. Primary complications of interest were overall complication rate, fat and mastectomy skin flap necrosis, and surgical revision. Logistic regression was applied to determine the odds of developing a complication based on RI and RA for each image.
Results A total of 25 patients (45 flaps) were included. In total, 17 patients (68%) developed at least one complication. Patients who developed any complication (p = 0.02) or underwent a surgical revision for complications (p = 0.02) had statistically lower RI of under-perfused portions of the flap. Patients with greater areas of under-perfused flap had a significantly higher risk of developing fat necrosis (odds ratio [OR]: 5.71, p = 0.03) and required a revision operation (OR: 1.10, p = 0.01).
Conclusion Image-based interpretation using the OnLume Avata System correlated with the risk of developing postoperative complications that standard fluorescence imaging systems may not appreciate. This information can benefit surgeons to improve perfusion assessment and intraoperative decision-making.
Funder
National Cancer Institute of the NIH