A multiple linear regression model calculates the anterior neck skin surface area in Chinese individuals: Implications for neck resurfacing and reconstruction
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Published on: August 3, 2011
Rui Li1, Jianmin Yang2, Zilong Cao3
1Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China; Scar and Wound Treatment Center, Plastic Surgery Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100144, China.
Background:
The normal value of anterior neck skin surface area (ANSSA) before scar contracture is essential for designing a skin flap of appropriate size to reconstruct the neck. However, to our knowledge, no relevant research has been reported. This study aimed to explore the normal value of ANSSA and to establish ANSSA calculation models.
Methods:
A total of 101 healthy volunteers were included in the training group, and 24 healthy volunteers and 24 patients with cervical scar contracture were included in the validation group. Anthropometric neck parameters were obtained using 3D stereophotogrammetry. Independent predictive factors were identified by multiple linear regression analyses, and ANSSA calculation models based on these factors were established.
Results:
The ANSSA of adult males and females was 13653.7 ± 1976.4 mm2 and 10142.1 ± 2199.8 mm2 in a neutral position, and 17846.5 ± 2365.7 mm2 and 14264.3 ± 1784.2 mm2 in an extended position, respectively. The ANSSA was significantly correlated with gender, weight, sternomental distance, and lateral neck length (p < 0.05). The adjusted r2 values of the neutral and extended ANSSA calculation models were 0.831 and 0.819 (p < 0.001).
Conclusions:
This study quantified neck morphological features, and established ANSSA calculation models to assist surgeons in designing flap and preventing limited neck mobility caused by insufficient flap size. To our knowledge, this study made it possible for the first time to accurately calculate the wound area and the required flap area preoperatively, eliminating the rough estimation based on experience.
