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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Head and Neck Cancer
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1473447
This article is part of the Research Topic Advanced Head and Neck Cancer: from Organ Preservation Strategies to extended resections and reconstruction View all 6 articles

Intraoperative Circulation Predict Prolonged Length of Stay after Head and Neck Free Flap Reconstruction: A Retrospective Study Based on Machine Learning

Provisionally accepted
Zhongqi Liu Zhongqi Liu Jinbei Wen Jinbei Wen Yingzhen Chen Yingzhen Chen Bin Zhou Bin Zhou Minghui Cao Minghui Cao Mingyan Guo Mingyan Guo *
  • Sun Yat-sen Memorial Hospital, Guangzhou, China

The final, formatted version of the article will be published soon.

    Background: Head and neck free flap reconstruction presents challenges in managing intraoperative circulation, potentially leading to prolonged length of stay (PLOS). Limited research exists on the associations between intraoperative circulation and PLOS given the difficulty of manual quantification of intraoperative circulation time-series data. Therefore, this study aimed to quantify intraoperative circulation data and investigate its association with PLOS after free flap reconstruction utilizing machine learning algorithms. Methods: 804 patients who underwent head and neck free flap reconstruction between September 2019 and February 2021 were included. Machine learning tools (Fourier transform, et al.) were utilized to extract features to quantify intraoperative circulation data. To compare the accuracy of quantified intraoperative circulation and manual intraoperative circulation assessments in the PLOS prediction, predictive models based on these 2 assessment methods were developed and validated. Results: Intraoperative circulation was quantified and a total of 114 features were extracted from intraoperative circulation data. Quantified intraoperative circulation models with a real-time predictive manner were constructed. A higher area under the receiver operating characteristic curve (AUROC) was observed in quantified intraoperative circulation data models (0.801 [95% CI, 0.733-0.869]) compared to manual intraoperative circulation assessment models (0.719 [95% CI, 0.641-0.797]) in PLOS prediction. Conclusion: Machine learning algorithms facilitated quantification of intraoperative circulation data. The developed realtime quantified intraoperative circulation prediction models based on this quantification offer a potential strategy to optimize intraoperative circulation management and mitigate PLOS following head and neck free flap reconstruction.

    Keywords: Intraoperative circulation, time series data, machine learning, Free flap reconstruction, Prolonged length of stay

    Received: 31 Jul 2024; Accepted: 03 Dec 2024.

    Copyright: © 2024 Liu, Wen, Chen, Zhou, Cao and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mingyan Guo, Sun Yat-sen Memorial Hospital, Guangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.