Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning
BackgroundHead 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 o...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1473447/full |
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| author | Zhongqi Liu Zhongqi Liu Jinbei Wen Yingzhen Chen Yingzhen Chen Bin Zhou Minghui Cao Minghui Cao Mingyan Guo Mingyan Guo |
| author_facet | Zhongqi Liu Zhongqi Liu Jinbei Wen Yingzhen Chen Yingzhen Chen Bin Zhou Minghui Cao Minghui Cao Mingyan Guo Mingyan Guo |
| author_sort | Zhongqi Liu |
| collection | DOAJ |
| description | BackgroundHead 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.Methods804 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.ResultsIntraoperative 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.ConclusionMachine learning algorithms facilitated quantification of intraoperative circulation data. The developed real-time 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. |
| format | Article |
| id | doaj-art-b9ed9d6b6a434fb4a46bc3eb8106129d |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-b9ed9d6b6a434fb4a46bc3eb8106129d2025-01-10T06:10:40ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.14734471473447Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learningZhongqi Liu0Zhongqi Liu1Jinbei Wen2Yingzhen Chen3Yingzhen Chen4Bin Zhou5Minghui Cao6Minghui Cao7Mingyan Guo8Mingyan Guo9Department of Anesthesiology, Shenshan Medical Central, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, ChinaDepartment of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Anesthesiology, Shenshan Medical Central, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, ChinaDepartment of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Anesthesiology, Shenshan Medical Central, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, ChinaDepartment of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Anesthesiology, Shenshan Medical Central, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, ChinaDepartment of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaBackgroundHead 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.Methods804 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.ResultsIntraoperative 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.ConclusionMachine learning algorithms facilitated quantification of intraoperative circulation data. The developed real-time 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.https://www.frontiersin.org/articles/10.3389/fonc.2024.1473447/fullintraoperative circulationtime series datamachine learningfree flap reconstructionprolonged length of stay |
| spellingShingle | Zhongqi Liu Zhongqi Liu Jinbei Wen Yingzhen Chen Yingzhen Chen Bin Zhou Minghui Cao Minghui Cao Mingyan Guo Mingyan Guo Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning Frontiers in Oncology intraoperative circulation time series data machine learning free flap reconstruction prolonged length of stay |
| title | Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning |
| title_full | Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning |
| title_fullStr | Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning |
| title_full_unstemmed | Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning |
| title_short | Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning |
| title_sort | intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction a retrospective study based on machine learning |
| topic | intraoperative circulation time series data machine learning free flap reconstruction prolonged length of stay |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1473447/full |
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