{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["55(1)"],"submitter":["Hao M"],"pubmed_abstract":["<h4>Objective</h4>Diabetes mellitus complicated with heart failure has high mortality and morbidity, but no reliable diagnoses and treatments are available. This study aimed to develop and verify a new model nomogram based on clinical parameters to predict diastolic cardiac dysfunction in patients with Type 2 diabetes mellitus (T2DM).<h4>Methods</h4>3030 patients with T2DM underwent Doppler echocardiography at the First Affiliated Hospital of Shenzhen University between January 2014 and December 2021. The patients were divided into the training dataset (<i>n</i> = 1701) and the verification dataset (<i>n</i> = 1329). In this study, a predictive diastolic cardiac dysfunction nomogram is developed using multivariable logical regression analysis, which contains the candidates selected in a mi"],"journal":["Annals of medicine"],"pagination":["766-777"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10798288"],"repository":["biostudies-literature"],"pubmed_title":["Novel model predicts diastolic cardiac dysfunction in type 2 diabetes."],"pmcid":["PMC10798288"],"pubmed_authors":["Liu X","Fang X","Li H","Zhou L","Lv L","Hao M","Huang X","Yan D","Guo T"],"additional_accession":[]},"is_claimable":false,"name":"Novel model predicts diastolic cardiac dysfunction in type 2 diabetes.","description":"<h4>Objective</h4>Diabetes mellitus complicated with heart failure has high mortality and morbidity, but no reliable diagnoses and treatments are available. This study aimed to develop and verify a new model nomogram based on clinical parameters to predict diastolic cardiac dysfunction in patients with Type 2 diabetes mellitus (T2DM).<h4>Methods</h4>3030 patients with T2DM underwent Doppler echocardiography at the First Affiliated Hospital of Shenzhen University between January 2014 and December 2021. The patients were divided into the training dataset (<i>n</i> = 1701) and the verification dataset (<i>n</i> = 1329). In this study, a predictive diastolic cardiac dysfunction nomogram is developed using multivariable logical regression analysis, which contains the candidates selected in a mi","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Dec","modification":"2025-04-04T14:16:36.877Z","creation":"2025-04-04T14:16:36.877Z"},"accession":"S-EPMC10798288","cross_references":{"pubmed":["36908240"],"doi":["10.1080/07853890.2023.2180154"]}}