Multivariate Analysis As An Advantageous Approach For Prediction Of The Adverse Outcome In Head And Neck Microvascular Reconstructive Surgery

Keywords

Complications; Flap failure; Free flaps; Head and neck cancer; Microvascular surgery; Reconstruction

Abstract

Background The use of a free flap has become a mainstay of reconstruction following the ablative surgery in head and neck. The success rates are about 90%, however, several factors have been described to have an adverse effect on free flap survival. Methods We have performed a retrospective analysis of the treatment outcome of 93 microvascular flaps and evaluated the factors influencing the risk of flap loss including patients' age, body mass index, smoking, general medical history and previous oncological treatment. Results Out of 93 flaps the total necrosis have been observed in 15 flaps with gradual improvement in the consecutive years. In individual analysis the patients age, BMI, and comorbidities did not reveal any significant relation. The history of any previous oncological treatment represented a significant adverse factor of success rate (p = 0.035), and was even more significant when patients experienced all treatment modalities prior to the reconstructive procedure (p = 0.009). Multivariate logistic regression model indicated that only surgery (p = 0.0008), chemotherapy (p = 0.02), cardiovascular diseases (p = 0.05) and patient's age (p = 0.02) represented significant factors impairing the success rate. Conclusion Incorporating multivariate analysis represents important statistical approach for better prediction of free flaps survival in head and neck reconstructive surgery. Incorporation of additional collective information could provide more precise approach in the risk of the flap loss assessment.

Publication Date

3-1-2017

Publication Title

American Journal of Otolaryngology - Head and Neck Medicine and Surgery

Volume

38

Issue

2

Number of Pages

148-152

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.amjoto.2016.11.012

Socpus ID

85008225448 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85008225448

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