(1) Background: Near-infrared fluorescence imaging is a technique capable of assessing tissue perfusion and has been adopted in various fields including plastic surgery, vascular surgery, coronary arterial disease, and gastrointestinal surgery. While the usefulness of this technique has been broadly explored, there is a large variety in the calculation of perfusion parameters. In this systematic review, we aim to provide a detailed overview of current perfusion parameters, and determine the perfusion parameters with the most potential for application in near-infrared fluorescence imaging. (2) Methods: A comprehensive search of the literature was performed in Pubmed, Embase, Medline, and Cochrane Review. We included all clinical studies referencing near-infrared perfusion parameters. (3) Results: A total of 1511 articles were found, of which, 113 were suitable for review, with a final selection of 59 articles. Near-infrared fluorescence imaging parameters are heterogeneous in their correlation to perfusion. Time-related parameters appear superior to absolute intensity parameters in a clinical setting. (4) Conclusions: This literature review demonstrates the variety of parameters selected for the quantification of perfusion in near-infrared fluorescence imaging.
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Background:
Implant loss is the most severe complication of implant-based breast reconstructions. This study aimed to evaluate the incidence of implant loss and other complications, identify associated risk factors, and create a risk model for implant loss.
Methods:
This was a retrospective cohort study of all patients who underwent a mastectomy, followed by either a two-stage or a direct-to-implant breast reconstruction. Patient variables, operative characteristics, and postoperative complications were obtained from the patient records. A multivariate mixed-effects logistic regression model was used to create a risk model for implant loss.
Results:
A total of 297 implant-based breast reconstructions were evaluated. Overall, the incidence of implant loss was 11.8%. Six risk factors were significantly associated with implant loss: obesity, a bra cup size larger than C, active smoking status, a nipple-preserving procedure, a direct-to-implant reconstruction, and a lower surgeon’s volume. A risk model for implant loss was created, showing a predicted risk of 8.4%–13% in the presence of one risk factor, 21.9%–32.5% in the presence of two, 47.5%–59.3% in the presence of three, and over 78.2% in the presence of four risk factors.
Conclusions:
The incidence of implant loss in this study was 11.8%. Six associated significant risk factors were identified. Our risk model for implant loss revealed that the predicted risk increased over 78.2% when four risk factors were present. This risk model can be used to better inform patients and decrease the risk of implant loss by optimizing surgery using personalized therapy.
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