Development and validation of an ensemble learning risk model for sepsis after abdominal surgery
Introduction Although their importance has gained attention, the clinical applications of methods for screening patients at high risk of sepsis after abdominal surgery have been restricted. Therefore, we aimed to develop and validate models for screening patients at high risk of sepsis after abdomin...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Termedia Publishing House
2024-06-01
|
| Series: | Archives of Medical Science |
| Subjects: | |
| Online Access: | https://www.archivesofmedicalscience.com/Development-and-validation-of-an-ensemble-learning-risk-model-for-sepsis-after-abdominal,189505,0,2.html |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850134952152137728 |
|---|---|
| author | Xin Shu Yujie Li Yiziting Zhu Zhiyong Yang Xiang Liu Xiaoyan Hu Chunyong Yang Lei Zhao Tao Zhu Yuwen Chen Bin Yi |
| author_facet | Xin Shu Yujie Li Yiziting Zhu Zhiyong Yang Xiang Liu Xiaoyan Hu Chunyong Yang Lei Zhao Tao Zhu Yuwen Chen Bin Yi |
| author_sort | Xin Shu |
| collection | DOAJ |
| description | Introduction
Although their importance has gained attention, the clinical applications of methods for screening patients at high risk of sepsis after abdominal surgery have been restricted. Therefore, we aimed to develop and validate models for screening patients at high risk of sepsis after abdominal surgery based on machine learning with routine variables.
Material and methods
The whole dataset was composed of three representative academic hospitals in China and the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Routine clinical variables were implemented for model development. The Boruta algorithm was applied for feature selection. Afterwards, ensemble learning and eight other conventional algorithms were used for model fitting and validation based on all features and selected features. The area under the receiver operating characteristic curves (ROC AUC), sensitivity, specificity, F1 score, accuracy, net reclassification index (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and calibration curves were used for model evaluation.
Results
A total of 955 patients undergoing abdominal surgery were finally analyzed (sepsis: 285, non-sepsis: 670). After feature selection, the ensemble learning model constructed by integrating k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) yielded the ROC AUC of 0.892 (0.841–0.944) and accuracy of 85.0% on the test data, and the ROC AUC of 0.782 (0.727–0.838) and accuracy of 68.1% on the validation data, which performed best. Albumin, ASA score, neutrophil-lymphocyte ratio, age, and glucose were the top features associated with postoperative sepsis by KNN and SVM.
Conclusions
We developed a new and potential generalizable model to preoperatively screen patients at high risk of sepsis after abdominal surgery, with the advantages of a representative training cohort and routine variables. |
| format | Article |
| id | doaj-art-aafac5d6c8d549c486fb981245de6a11 |
| institution | OA Journals |
| issn | 1734-1922 1896-9151 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Termedia Publishing House |
| record_format | Article |
| series | Archives of Medical Science |
| spelling | doaj-art-aafac5d6c8d549c486fb981245de6a112025-08-20T02:31:34ZengTermedia Publishing HouseArchives of Medical Science1734-19221896-91512024-06-0121113815210.5114/aoms/189505189505Development and validation of an ensemble learning risk model for sepsis after abdominal surgeryXin Shu0Yujie Li1Yiziting Zhu2Zhiyong Yang3Xiang Liu4Xiaoyan Hu5Chunyong Yang6Lei Zhao7Tao Zhu8Yuwen Chen9Bin Yi10Department of Anesthesiology, Southwest Hospital, Third Military Medical University, ChinaDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, ChinaDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, ChinaDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, ChinaDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, ChinaDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, ChinaDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, ChinaDepartment of Anesthesiology, Xuan Wu Hospital, Capital Medical University, ChinaDepartment of Anesthesiology, West China Hospital of Sichuan University, ChinaChongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, ChinaDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, ChinaIntroduction Although their importance has gained attention, the clinical applications of methods for screening patients at high risk of sepsis after abdominal surgery have been restricted. Therefore, we aimed to develop and validate models for screening patients at high risk of sepsis after abdominal surgery based on machine learning with routine variables. Material and methods The whole dataset was composed of three representative academic hospitals in China and the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Routine clinical variables were implemented for model development. The Boruta algorithm was applied for feature selection. Afterwards, ensemble learning and eight other conventional algorithms were used for model fitting and validation based on all features and selected features. The area under the receiver operating characteristic curves (ROC AUC), sensitivity, specificity, F1 score, accuracy, net reclassification index (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and calibration curves were used for model evaluation. Results A total of 955 patients undergoing abdominal surgery were finally analyzed (sepsis: 285, non-sepsis: 670). After feature selection, the ensemble learning model constructed by integrating k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) yielded the ROC AUC of 0.892 (0.841–0.944) and accuracy of 85.0% on the test data, and the ROC AUC of 0.782 (0.727–0.838) and accuracy of 68.1% on the validation data, which performed best. Albumin, ASA score, neutrophil-lymphocyte ratio, age, and glucose were the top features associated with postoperative sepsis by KNN and SVM. Conclusions We developed a new and potential generalizable model to preoperatively screen patients at high risk of sepsis after abdominal surgery, with the advantages of a representative training cohort and routine variables.https://www.archivesofmedicalscience.com/Development-and-validation-of-an-ensemble-learning-risk-model-for-sepsis-after-abdominal,189505,0,2.htmlsepsismachine learningpostoperative complicationsperioperative periodrisk assessment |
| spellingShingle | Xin Shu Yujie Li Yiziting Zhu Zhiyong Yang Xiang Liu Xiaoyan Hu Chunyong Yang Lei Zhao Tao Zhu Yuwen Chen Bin Yi Development and validation of an ensemble learning risk model for sepsis after abdominal surgery Archives of Medical Science sepsis machine learning postoperative complications perioperative period risk assessment |
| title | Development and validation of an ensemble learning risk model for sepsis after abdominal surgery |
| title_full | Development and validation of an ensemble learning risk model for sepsis after abdominal surgery |
| title_fullStr | Development and validation of an ensemble learning risk model for sepsis after abdominal surgery |
| title_full_unstemmed | Development and validation of an ensemble learning risk model for sepsis after abdominal surgery |
| title_short | Development and validation of an ensemble learning risk model for sepsis after abdominal surgery |
| title_sort | development and validation of an ensemble learning risk model for sepsis after abdominal surgery |
| topic | sepsis machine learning postoperative complications perioperative period risk assessment |
| url | https://www.archivesofmedicalscience.com/Development-and-validation-of-an-ensemble-learning-risk-model-for-sepsis-after-abdominal,189505,0,2.html |
| work_keys_str_mv | AT xinshu developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery AT yujieli developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery AT yizitingzhu developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery AT zhiyongyang developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery AT xiangliu developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery AT xiaoyanhu developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery AT chunyongyang developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery AT leizhao developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery AT taozhu developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery AT yuwenchen developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery AT binyi developmentandvalidationofanensemblelearningriskmodelforsepsisafterabdominalsurgery |