[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124935-en":3,"doc-seo-124935-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124935,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury","Off-pump coronary artery bypass grafting (OPCABG) can lead to cardiac surgery-associated acute kidney injury (CSA-AKI), which occurs in up to one of three patients. A retrospective cohort of 1,041 OPCABG patients (2021-06-01 to 2023-04-30) was used to build an OPCABG-AKI prediction model using machine learning. Preoperative baseline and intraoperative time-series data were preprocessed and fused via transfer learning, yielding a best-performing GBDT model. Feature importance ranked multiple medication and clinical factors as key predictors, supporting early risk stratification and timely intervention.","Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury  \nYuezi Song1, Wenqian Zhai1,2, Songnan Ma3, Yubo Wu3, Min Ren4, Jef Van den Eynde5, Paolo Nardi6, Philip Y. K. Pang7, Jason M. Ali8, Jiange Han1,2\\#, Zhigang Guo2,9\\#  \n1Department of Anesthesiology, Chest Hospital, Tianjin University, Tianjin, China; 2Tianjin Key Laboratory of Cardiovascular Emergencies and Critical Care, Tianjin, China; 3Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; 4Tianjin Institute of Cardiovascular Disease, Tianjin, China; 5Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium; 6Department of Cardiac Surgery, Tor Vergata University Hospital of Rome, Rome, Italy; 7Department of Cardiothoracic Surgery, National Heart Centre Singapore, Singapore, Singapore; 8Department of Cardiothoracic Surgery, Royal Papworth Hospital, Cambridge, UK; 9Department of Cardiovascular Surgery, Chest Hospital, Tianjin University, Tianjin, China  \nContributions: (I) Conception and design: Y Song, J Han; (II) Administrative support: Z Guo; (III) Provision of study materials or patients: W Zhai, SMa; (IV) Collection and assembly of data: Y Wu, M Ren; (V) Data analysis and interpretation: Y Song, J Han; (VI) Manuscript writing: All authors;  \n(VII) Final approval of manuscript: All authors.  \n\\#These authors contributed equally to this work.  \nCorrespondence to: Jiange Han, BM. Department of Anesthesiology, Chest Hospital, Tianjin University, No. 261, Tai’erzhuang South Road, Jinnan District, Tianjin 300222, China; Tianjin Key Laboratory of Cardiovascular Emergencies and Critical Care, Tianjin, [China. Email: hanjiange@163.com](China. Email: hanjiange@163.com); Zhigang Guo, MM. Department of Cardiovascular Surgery, Chest Hospital, Tianjin University, No. 261, Tai’erzhuang South Road, Jinnan District, Tianjin 300222, China; Tianjin Key Laboratory of Cardiovascular Emergencies and Critical Care, Tianjin, China. Email: [zhigangguo@vip.163.com](zhigangguo@vip.163.com).  \nBackground: The cardiac surgery-associated acute kidney injury (CSA-AKI) occurs in up to 1 out of 3 patients. Off-pump coronary artery bypass grafting (OPCABG) is one of the major cardiac surgeries leading to CSA-AKI. Early identification and timely intervention are of clinical significance for CSA-AKI. In this study, we aimed to establish a prediction model of off-pump coronary artery bypass grafting-associated acute  \nkidney injury (OPCABG-AKI) after surgery based on machine learning methods.  \nMethods: The preoperative and intraoperative data of 1,041 patients who underwent OPCABG in Chest Hospital, Tianjin University from June 1, 2021 to April 30, 2023 were retrospectively collected. The definition of OPCABG-AKI was based on the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. The baseline data and intraoperative time series data were included in the dataset, which were preprocessed separately.  \nA total of eight machine learning models were constructed based on the baseline data: logistic regression (LR), gradient-boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT). The intraoperative time series data were extracted using a long short-term memory (LSTM) deep learning model. The baseline data and intraoperative features were then integrated through transfer learning and fused into each of the eight machine learning models for training. Based on the calculation of accuracy and area under the curve (AUC)  \nof the prediction model, the best model was selected to establish the final OPCABG-AKI risk prediction model.  \nThe importance of features was calculated and ranked by DT model, to identify the main risk factors.  \nResults: Among 701 patients included in the study, 73 patients (10.4%) developed OPCABG-AKI. The GBDT model was shown to have th","cbCaitKrfW4JeslP","https://ap.wps.com/l/cbCaitKrfW4JeslP","pdf",286455,1,9,"English","en",105,"# Background\n# Methods\n## Data source and cohort\n## Feature preprocessing and model training\n# Results\n## Model performance\n## Feature importance\n# Conclusions\n# Keywords","[{\"question\":\"What outcome and clinical context does the study predict?\",\"answer\":\"The study predicts off-pump coronary artery bypass grafting-associated acute kidney injury (OPCABG-AKI) occurring after OPCABG, within the broader context of cardiac surgery-associated AKI (CSA-AKI).\"},{\"question\":\"Which data types were used to build the prediction models?\",\"answer\":\"Preoperative baseline data and intraoperative time-series data were collected, preprocessed separately, and then fused through transfer learning to train prediction models.\"},{\"question\":\"Why was the GBDT model selected as the best predictor?\",\"answer\":\"GBDT achieved the highest predictive performance based on accuracy and AUC, both using baseline data only and using fused baseline plus intraoperative datasets.\"}]","Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury | 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