Machine learning-based prediction of respiratory depression during sedation for liposuction
Abstract Procedural sedation is often performed by non-anesthesiologists in various settings and can lead to respiratory depression. A tool that enables early detection of respiratory compromise could not only enhance patient safety during procedural sedation, but also reduce the risk of medical lia...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-04505-3 |
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| author | Jin-Woo Kim Jae Hee Woo Jaewon Seo Hajin Kim Sunho Lee Younchan Park Jaehyun Ahn Seonghun Hong Hye-Min Jeong Yuncheol Kang |
| author_facet | Jin-Woo Kim Jae Hee Woo Jaewon Seo Hajin Kim Sunho Lee Younchan Park Jaehyun Ahn Seonghun Hong Hye-Min Jeong Yuncheol Kang |
| author_sort | Jin-Woo Kim |
| collection | DOAJ |
| description | Abstract Procedural sedation is often performed by non-anesthesiologists in various settings and can lead to respiratory depression. A tool that enables early detection of respiratory compromise could not only enhance patient safety during procedural sedation, but also reduce the risk of medical liability. In this study, we aimed to develop a machine learning model that integrates detailed body composition data from patients undergoing liposuction to enhance the prediction of respiratory depression during procedural sedation. Features from bioelectrical impedance analysis, 3D body scanning, and manual measurements were extracted and used to train machine learning models. SHAP analysis, an explainable AI approach, was conducted to visually interpret feature importance. The XGBoost model, particularly when incorporating 3D body scanning data, demonstrated superior predictive performance, achieving an AUROC of 0.856 and a sensitivity of 0.805. The main predictors identified were upper abdominal volume, BMI, and age, highlighting the importance of the acquisition of detailed body composition data for assessing respiratory risks during sedation. The developed model effectively predicts the risk of respiratory depression in patients undergoing liposuction, offering a potential for personalized sedation protocols. |
| format | Article |
| id | doaj-art-0800541baae94d86873b099f89042e38 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0800541baae94d86873b099f89042e382025-08-20T02:05:48ZengNature PortfolioScientific Reports2045-23222025-06-0115111210.1038/s41598-025-04505-3Machine learning-based prediction of respiratory depression during sedation for liposuctionJin-Woo Kim0Jae Hee Woo1Jaewon Seo2Hajin Kim3Sunho Lee4Younchan Park5Jaehyun Ahn6Seonghun Hong7Hye-Min Jeong8Yuncheol Kang9Department of Oral and Maxillofacial Surgery, College of Medicine, Ewha Womans UniversityDepartment of Anesthesiology and Pain Medicine, College of Medicine, Ewha Womans University365mc Daegu Liposuction Hospital365mc Seoul Liposuction HospitalGlobal 365mc Deajon Liposuction Hospital365mc Busan Liposuction HospitalGlobal 365mc Incheon Liposuction Hospital365mc Busan Liposuction HospitalDepartment of Artificial Intelligence Convergence, Ewha Womans UniversitySchool of Business, Ewha Womans UniversityAbstract Procedural sedation is often performed by non-anesthesiologists in various settings and can lead to respiratory depression. A tool that enables early detection of respiratory compromise could not only enhance patient safety during procedural sedation, but also reduce the risk of medical liability. In this study, we aimed to develop a machine learning model that integrates detailed body composition data from patients undergoing liposuction to enhance the prediction of respiratory depression during procedural sedation. Features from bioelectrical impedance analysis, 3D body scanning, and manual measurements were extracted and used to train machine learning models. SHAP analysis, an explainable AI approach, was conducted to visually interpret feature importance. The XGBoost model, particularly when incorporating 3D body scanning data, demonstrated superior predictive performance, achieving an AUROC of 0.856 and a sensitivity of 0.805. The main predictors identified were upper abdominal volume, BMI, and age, highlighting the importance of the acquisition of detailed body composition data for assessing respiratory risks during sedation. The developed model effectively predicts the risk of respiratory depression in patients undergoing liposuction, offering a potential for personalized sedation protocols.https://doi.org/10.1038/s41598-025-04505-3 |
| spellingShingle | Jin-Woo Kim Jae Hee Woo Jaewon Seo Hajin Kim Sunho Lee Younchan Park Jaehyun Ahn Seonghun Hong Hye-Min Jeong Yuncheol Kang Machine learning-based prediction of respiratory depression during sedation for liposuction Scientific Reports |
| title | Machine learning-based prediction of respiratory depression during sedation for liposuction |
| title_full | Machine learning-based prediction of respiratory depression during sedation for liposuction |
| title_fullStr | Machine learning-based prediction of respiratory depression during sedation for liposuction |
| title_full_unstemmed | Machine learning-based prediction of respiratory depression during sedation for liposuction |
| title_short | Machine learning-based prediction of respiratory depression during sedation for liposuction |
| title_sort | machine learning based prediction of respiratory depression during sedation for liposuction |
| url | https://doi.org/10.1038/s41598-025-04505-3 |
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