医学
肋软骨
鼻整形术
外科
软骨
整形外科
气胸
可视模拟标度
耳鼻咽喉科
鼻子
解剖
作者
Xulong Zhang,Yihao Xu,Ruobing Zheng,Wenfang Dong,Junsheng Guo,Huan Wang,Jianjun You,Fei Fan
标识
DOI:10.1007/s00266-022-02842-6
摘要
BackgroundAlthough costal cartilage is a reliable source of cartilage for rhinoplasty and provides a strong scaffold for total nasal reconstruction, traditional collection techniques may cause complications at the donor site. In this paper, we report a simple and safe technique for harvesting full-length costal cartilage and its application in total nasal reconstruction.MethodsFrom May 2018 to December 2020, 24 patients with nasal defects, including 16 females and eight males, received nasal reconstruction in the Rhinoplasty and Repair Center of the Plastic Surgery Hospital of the Chinese Academy of Medical Sciences. Clinical outcomes were evaluated during the postoperative stay at the hospital and at the 6–30-month follow-up.ResultsThe operative time of cartilage harvesting ranged from 30 to 60 min. The patients could walk freely one day after surgery. The average ± standard deviation of Visual Analog Scale scores for pain at the harvested site were 2.583 ± 0.717 (at rest) and 4.750 ± 0.794 (during coughing) 6 h after surgery. We observed no complications (e.g., pleural perforation, pneumothorax, or massive bleeding) due to rib grafts in any patients. During the 6–30 months of follow-up, all patients had complete healing of both donor and recipient sites. The surgical results were rated as satisfactory or good by the patients and surgeons.ConclusionsThis new cold light source-assisted costal cartilage harvest technique allows full-length costal cartilage to be obtained for total nasal reconstruction, with minimal donor site complications, short operation time, fast postoperative recovery, and high satisfaction among patients and surgeons.Level of Evidence IVThis journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.
科研通智能强力驱动
Strongly Powered by AbleSci AI