An Algorithm for Selecting Buoy Skin Paddle Design for Flap Monitoring in Total Autologous Breast Reconstruction After Nipple-Sparing Mastectomy

一种用于保留乳头乳房切除术后自体乳房重建中皮瓣监测的浮标皮肤桨设计选择算法

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Abstract

Introduction Monitoring buried flaps in reconstructive breast surgery is challenging, and the ideal technique is controversial. Established options include leaving an exterior ("buoy" or "sentinel") skin paddle versus invasive implantable devices to avoid removing the paddle later. Technical modifications and an algorithm for strategic skin paddle positioning to circumvent this while avoiding complex monitoring equipment are proposed. Patients and methods Patients in whom buoy skin paddles were utilized for breast flap monitoring by a single surgeon were reviewed. Indications, demographic details, precise monitoring paddle location, and flap outcomes were evaluated. An algorithm and classification system were then formulated. Results Thirteen buoy skin paddles were utilized in seven patients (mean age: 43.5 years; range: 31-65) to monitor reconstructive flaps performed for risk-reducing mastectomies (four patients and seven breasts), therapeutic mastectomy (one breast), and revision surgery (three patients and five breasts). The flaps comprised seven deep inferior epigastric artery perforators (DIEPs), four superficial inferior epigastric arteries (SIEAs), and two pedicled latissimus dorsi (LDs) (mean free flap weight: 809 g; average mastectomy weight (n = 10 breasts): 467 g; range: 248-864). The skin paddles were located horizontally along the inframammary crease or vertically inferior to the nipple-areola or both. All flap transfers were successful with no re-explorations. All patients declined the monitoring paddle excision, and none have requested breast mound revision for poor cosmesis or contour deformities. Conclusion Vertical and horizontal skin paddles proved reliable for buried flap monitoring without recourse to invasive and expensive equipment. When designed appropriately, they do not require revision surgery. An algorithmic classification of skin paddle location to enable this is proposed.

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