Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery
Abstract Assessing the risk of postoperative cardiovascular events before performing non-cardiac surgery is clinically important. The current risk score systems for preoperative evaluation may not adequately represent a small subset of high-risk populations. Accordingly, this study aimed at applying...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78482-4 |
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| author | Tomohisa Seki Toru Takiguchi Yu Akagi Hiromasa Ito Kazumi Kubota Kana Miyake Masafumi Okada Yoshimasa Kawazoe |
| author_facet | Tomohisa Seki Toru Takiguchi Yu Akagi Hiromasa Ito Kazumi Kubota Kana Miyake Masafumi Okada Yoshimasa Kawazoe |
| author_sort | Tomohisa Seki |
| collection | DOAJ |
| description | Abstract Assessing the risk of postoperative cardiovascular events before performing non-cardiac surgery is clinically important. The current risk score systems for preoperative evaluation may not adequately represent a small subset of high-risk populations. Accordingly, this study aimed at applying iterative random forest to analyze combinations of factors that could potentially be clinically valuable in identifying these high-risk populations. To this end, we used the Japan Medical Data Center database, which includes claims data from Japan between January 2005 and April 2021, and employed iterative random forests to extract factor combinations that influence outcomes. The analysis demonstrated that a combination of a prior history of stroke and extremely low LDL-C levels was associated with a high non-cardiac postoperative risk. The incidence of major adverse cardiovascular events in the population characterized by the incidence of previous stroke and extremely low LDL-C levels was 15.43 events per 100 person-30 days [95% confidence interval, 6.66–30.41] in the test data. At this stage, the results only show correlation rather than causation; however, these findings may offer valuable insights for preoperative risk assessment in non-cardiac surgery. |
| format | Article |
| id | doaj-art-b96751a2d0a4445a9299d49a07f00d5e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-b96751a2d0a4445a9299d49a07f00d5e2025-08-20T02:13:35ZengNature PortfolioScientific Reports2045-23222024-11-0114111110.1038/s41598-024-78482-4Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgeryTomohisa Seki0Toru Takiguchi1Yu Akagi2Hiromasa Ito3Kazumi Kubota4Kana Miyake5Masafumi Okada6Yoshimasa Kawazoe7Department of Healthcare Information Management, The University of Tokyo HospitalDepartment of Healthcare Information Management, The University of Tokyo HospitalDepartment of Biomedical Informatics, Graduate School of Medicine, The University of TokyoDepartment of Healthcare Information Management, The University of Tokyo HospitalDepartment of Healthcare Information Management, The University of Tokyo HospitalDepartment of Healthcare Information Management, The University of Tokyo HospitalDepartment of Healthcare Information Management, The University of Tokyo HospitalDepartment of Healthcare Information Management, The University of Tokyo HospitalAbstract Assessing the risk of postoperative cardiovascular events before performing non-cardiac surgery is clinically important. The current risk score systems for preoperative evaluation may not adequately represent a small subset of high-risk populations. Accordingly, this study aimed at applying iterative random forest to analyze combinations of factors that could potentially be clinically valuable in identifying these high-risk populations. To this end, we used the Japan Medical Data Center database, which includes claims data from Japan between January 2005 and April 2021, and employed iterative random forests to extract factor combinations that influence outcomes. The analysis demonstrated that a combination of a prior history of stroke and extremely low LDL-C levels was associated with a high non-cardiac postoperative risk. The incidence of major adverse cardiovascular events in the population characterized by the incidence of previous stroke and extremely low LDL-C levels was 15.43 events per 100 person-30 days [95% confidence interval, 6.66–30.41] in the test data. At this stage, the results only show correlation rather than causation; however, these findings may offer valuable insights for preoperative risk assessment in non-cardiac surgery.https://doi.org/10.1038/s41598-024-78482-4Perioperative riskMachine learningIterative random forestsNon-cardiac surgery |
| spellingShingle | Tomohisa Seki Toru Takiguchi Yu Akagi Hiromasa Ito Kazumi Kubota Kana Miyake Masafumi Okada Yoshimasa Kawazoe Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery Scientific Reports Perioperative risk Machine learning Iterative random forests Non-cardiac surgery |
| title | Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery |
| title_full | Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery |
| title_fullStr | Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery |
| title_full_unstemmed | Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery |
| title_short | Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery |
| title_sort | iterative random forest based identification of a novel population with high risk of complications post non cardiac surgery |
| topic | Perioperative risk Machine learning Iterative random forests Non-cardiac surgery |
| url | https://doi.org/10.1038/s41598-024-78482-4 |
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