{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["He C"],"funding":["Zhejiang Medical Health Science and Technology Program"],"pagination":["77"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11299393"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(1)"],"pubmed_abstract":["<h4>Background</h4>Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes.<h4>Methods</h4>From January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Lo"],"journal":["Biomedical engineering online"],"pubmed_title":["Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics."],"pmcid":["PMC11299393"],"funding_grant_id":["2022KY1317"],"pubmed_authors":["He C","Kong L","Lu Z","Qi Y","Fu L","Xu H","Wu F"],"additional_accession":[]},"is_claimable":false,"name":"Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.","description":"<h4>Background</h4>Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes.<h4>Methods</h4>From January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Lo","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Aug","modification":"2025-04-05T15:03:16.743Z","creation":"2025-02-19T04:40:26.177Z"},"accession":"S-EPMC11299393","cross_references":{"pubmed":["39098936"],"doi":["10.1186/s12938-024-01273-5"]}}