Show simple item record

Machine learning-based model development for predicting risk factors of prolonged intra-aortic balloon pump therapy in patients with coronary artery bypass grafting

dc.contributor.authorChangqing Yang
dc.contributor.authorPeng Zheng
dc.contributor.authorLuo Li
dc.contributor.authorQian Zhang
dc.contributor.authorZhouyu Luo
dc.contributor.authorZhan Shi
dc.contributor.authorSheng Zhao
dc.contributor.authorQuanye Li
dc.contributor.otherDepartment of Emergency, The Sixth Affiliated Hospital of Nantong University
dc.contributor.otherDepartment of Cardiology, The First Affiliated Hospital of Nanjing Medical University
dc.contributor.otherDepartment of Cardiovascular Surgery of the First Affiliated Hospital & Institute for Cardiovascular Science, Suzhou Medical College, Soochow University, Soochow University
dc.contributor.otherDepartment of Cardiology, The First Affiliated Hospital of Nanjing Medical University
dc.contributor.otherDepartment of Emergency, The Sixth Affiliated Hospital of Nantong University
dc.contributor.otherDepartment of Cardiovascular Surgery, The Sixth Affiliated Hospital of Nantong University
dc.contributor.otherDepartment of Cardiovascular Surgery, The First Affiliated Hospital of Nanjing Medical University
dc.contributor.otherDepartment of Emergency, The Sixth Affiliated Hospital of Nantong University
dc.date.accessioned2024-06-30T11:32:30Z
dc.date.accessioned2025-10-08T08:40:02Z
dc.date.available2025-10-08T08:40:02Z
dc.date.issued01-06-2024
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/36644
dc.description.abstractAbstract Machine learning algorithms are frequently used to clinical risk prediction. Our study was designed to predict risk factors of prolonged intra-aortic balloon pump (IABP) use in patients with coronary artery bypass grafting (CABG) through developing machine learning-based models. Patients who received perioperative IABP therapy were divided into two groups based on their length of IABP implantation longer than the 75th percentile for the whole cohort: normal (≤ 10 days) and prolonged (> 10 days) groups. Seven machine learning-based models were created and evaluated, and then the Shapley Additive exPlanations (SHAP) method was employed to further illustrate the influence of the features on model. In our study, a total of 143 patients were included, comprising 56 cases (38.16%) in the prolonged group. The logistic regression model was considered the final prediction model according to its most excellent performance. Furthermore, feature important analysis identified left ventricular end-systolic or diastolic diameter, preoperative IABP use, diabetes, and cardiac troponin T as the top five risk variables for prolonged IABP implantation in patients. The SHAP analysis further explained the features attributed to the model. Machine learning models were successfully developed and used to predict risk variables of prolonged IABP implantation in patients with CABG. This may help early identification for prolonged IABP use and initiate clinical interventions.
dc.language.isoEN
dc.publisherBMC
dc.subject.lccSurgery
dc.titleMachine learning-based model development for predicting risk factors of prolonged intra-aortic balloon pump therapy in patients with coronary artery bypass grafting
dc.typeArticle
dc.description.keywordsMachine learning
dc.description.keywordsProlonged IABP
dc.description.keywordsRisk factors
dc.description.keywordsPrediction
dc.description.pages1-12
dc.description.doi10.1186/s13019-024-02830-8
dc.title.journalJournal of Cardiothoracic Surgery
dc.identifier.e-issn1749-8090
dc.identifier.oaioai:doaj.org/journal:614c3911a184435d945eebc1223edbc5
dc.journal.infoVolume 19, Issue 1


This item appears in the following Collection(s)

Show simple item record