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颈部肌肉质量指数作为喉切除术后伤口并发症的预测指标

Neck Muscle Mass Index as a Predictor of Post-Laryngectomy Wound Complications.

作者信息

Bozkurt Gülpembe, Elhassan Hassan Ahmed, Mahmutoğlu Abdullah Soydan, Çelebi İrfan, Mcleod Robert W J, Soytaş Pınar, Erol Zeynep Nur, Sözen Esra

机构信息

1 Department of Otorhinolaryngology, Acıbadem University Hospital, Istanbul, Turkey.

2 Lewisham University Hospital, London.

出版信息

Ann Otol Rhinol Laryngol. 2018 Nov;127(11):841-847. doi: 10.1177/0003489418798660. Epub 2018 Sep 9.

Abstract

OBJECTIVE

We investigated the relationship between paravertebral muscle cross-sectional area (PVM CSA) at the third vertebra (C3) level using computerized tomography (CT) neck images and its relationship with complications after total laryngectomy.

DESIGN

Retrospective analysis of 60 advanced laryngeal cancer patients who underwent total laryngectomy was performed. The cross-sectional areas of paravertebral neck muscles using neck CT at C3 level images obtained preoperatively were analyzed.

RESULTS

A significant difference in PVM CSA between complication and no complication groups, F(1, 53 = 4.319, P = .043), was identified by ANCOVA. There were no significant differences in between-subject effects: T-stage ( F = 1.652, P = .204), body mass index ( F = 0.889, P = .35), albumin ( F = .359, P = .552), age ( F = 1.623 P = .208), and smoking ( F = 4.319, P = .41).

CONCLUSION

The PVM CSA measured at C3 level on pretreatment CT may help identify patients at higher risk of postoperative wound complications after total laryngectomy and who may particularly benefit from preoperative optimization of nutritional status.

摘要

目的

我们利用颈部计算机断层扫描(CT)图像研究了第三颈椎(C3)水平椎旁肌横截面积(PVM CSA)与全喉切除术后并发症之间的关系。

设计

对60例行全喉切除术的晚期喉癌患者进行回顾性分析。分析术前获得的C3水平颈部CT图像上椎旁颈部肌肉的横截面积。

结果

通过协方差分析确定并发症组和无并发症组之间的PVM CSA存在显著差异,F(1, 53) = 4.319,P = 0.043。受试者间效应无显著差异:T分期(F = 1.652,P = 0.204)、体重指数(F = 0.889,P = 0.35)、白蛋白(F = 0.359,P = 0.552)、年龄(F = 1.623,P = 0.208)和吸烟情况(F = 4.319,P = 0.41)。

结论

术前CT上C3水平测量的PVM CSA可能有助于识别全喉切除术后伤口并发症风险较高的患者,以及可能特别受益于术前营养状况优化的患者。

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