{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhu X"],"funding":["Science Fund for Distinguished Young Scholars of Anhui Province","the Major Project of Natural Science Research of Colleges and Universities in Anhui Province"],"pagination":["224"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11948733"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["Background
Systemic embolic events due to exfoliation of intracardiac thrombus (ICT) are one of the catastrophic complications of dilated cardiomyopathy (DCM). This study intended to develop a prediction model to predict the risk of ICT in patients with DCM.Methods
Data from 632 patients with DCM from a hospital was collected. ICT was identified based on the results of transthoracic echocardiography. Basic information, vital signs, comorbidities, and biochemical data were measured and collected from each patient. The least absolute shrinkage and selection operator (LASSO) regression was used for the final model variable screening. Four classifiers including Logistic Regression, support vector machine (SVM), Random Forest, and eXtreme Gradient Boosting (XGBoost) were used fo"],"journal":["BMC cardiovascular disorders"],"pubmed_title":["Construction and validation of a predictive model for intracardiac thrombus risk in patients with dilated cardiomyopathy: a retrospective study."],"pmcid":["PMC11948733"],"funding_grant_id":["KJ2019ZD65","2208085MH200"],"pubmed_authors":["Jiang Y","Li J","Hu Z","Wang T","Zhu X"],"additional_accession":[]},"is_claimable":false,"name":"Construction and validation of a predictive model for intracardiac thrombus risk in patients with dilated cardiomyopathy: a retrospective study.","description":"Background
Systemic embolic events due to exfoliation of intracardiac thrombus (ICT) are one of the catastrophic complications of dilated cardiomyopathy (DCM). This study intended to develop a prediction model to predict the risk of ICT in patients with DCM.Methods
Data from 632 patients with DCM from a hospital was collected. ICT was identified based on the results of transthoracic echocardiography. Basic information, vital signs, comorbidities, and biochemical data were measured and collected from each patient. The least absolute shrinkage and selection operator (LASSO) regression was used for the final model variable screening. Four classifiers including Logistic Regression, support vector machine (SVM), Random Forest, and eXtreme Gradient Boosting (XGBoost) were used fo","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-06-25T03:04:34.848Z","creation":"2025-06-25T03:04:34.848Z"},"accession":"S-EPMC11948733","cross_references":{"pubmed":["40148766"],"doi":["10.1186/s12872-025-04581-3"]}}