Prognostic Prediction of Multiterritory Flap: A Preliminary Clinical Study on the Classification of Priority Developing Artery and Arterial Anastomosis Utilizing Indocyanine Green Angiography

Author:

Cao Zilong,Jiao Hu,Gan Cheng,Tian Jia,Zhang Tiran,Li Rui,Yue Qiang,Liu Liqiang

Abstract

Background: Congestion and necrosis are frequent complications in multiterritory flaps. Indocyanine green angiography (ICGA) is a commonly utilized tool for evaluating blood flow and perforator location within the flap; however, there is currently no existing research investigating its potential to predict flap prognosis before surgery. Methods: The forehead skin of 50 surgical patients was assessed using preoperative ICGA, enabling observation and classification of the priority developing artery and arterial anastomosis among adjacent arterial perfusion territories during the arterial phase. Subsequently, 5 clinical cases of forehead flap transfer were studied to validate the classification method. Results: First, the priority developing artery can be classified into 4 types and 5 subtypes, encompassing type Ⅰa: Bilateral ST-As equalization type (9/50), type Ⅰb: unilateral ST-A dominance type (11/50), type II: SOT-As dominance type (14/50), type III: unilateral ST-A plus SOT-A dominance type (6/50), and type IV: bilateral ST-As plus SOT-As equilibrium type (10/50). Second, arterial anastomosis can be divided into 5 types: type I: complete choke anastomosis type (13/50), type II: complete true anastomosis type (7/50), type III: central choke anastomosis type (10/50), type IV: bilateral choke anastomosis type (8/50), and type V: unilateral choke anastomosis type (12/50). Finally, the clinical flap outcomes showed that the ICGA classification could serve as a good prognostic indicator. Conclusions: The hemodynamic classification of priority developing artery and arterial anastomosis employed by ICGA has the potential to predict flap prognosis and offer valuable insights for preoperative design and perioperative treatment strategies. More sample size is needed to optimize and validate this classification.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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