{"doi":"10.1016/j.jacadv.2024.101188","title":"Artificial Intelligence Prediction of Cardiovascular Events Using Opportunistic Epicardial Adipose Tissue Assessments From Computed Tomography Calcium Score","abstract":"Recent studies have used basic epicardial adipose tissue (EAT) assessments (eg, volume and mean Hounsfield unit [HU]) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACEs). The purpose of this study was to create novel, hand-crafted EAT features, “fat-omics,” to capture the pathophysiology of EAT and improve MACE prediction. We studied a cohort of 400 patients with low-dose cardiac computed tomography calcium score examinations. We purposefully used a MACE-enriched cohort (56% event rate) for feature engineering purposes. We divided the cohort into training/testing sets (80%/20%). We segmented EAT using a previously validated, deep-learning method with optional manual correction. We extracted 148 initial EAT features (eg, morphologic, spatial, and HU), dubbed fat-omics, and used Cox elastic-net for feature reduction and prediction of MACE. Bootstrap validation gave CIs. Traditional EAT features gave marginal prediction (EAT-volume/EAT-mean-HU/BMI gave C-indices 0.53/0.55/0.57, respectively). Significant improvement was obtained with the 15-feature fat-omics model (C-index = 0.69, test set). High-risk features included the volume-of-voxels-having-elevated-HU-[-50,-30-HU] and HU-negative-skewness, both of which assess high HU values in EAT, a property implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, with the high-risk group having an HR 2.4 times that of the low-risk group ( P < 0.001). Preliminary findings indicate an opportunity to use finely tuned, explainable assessments on EAT for improved cardiovascular risk prediction.","journal":"JACC Advances","year":2024,"id":421415,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":30,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9516,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1214190,"name":"Joshua Freeze","orcid":null,"position":1,"is_corresponding":false},{"id":929837,"name":"Prerna Singh","orcid":"0000-0002-7823-323X","position":2,"is_corresponding":false},{"id":323658,"name":"Justin N. Kim","orcid":"0000-0003-4713-8552","position":3,"is_corresponding":false},{"id":762293,"name":"Yingnan Song","orcid":"0009-0003-2929-0638","position":4,"is_corresponding":false},{"id":21505,"name":"Hao Wu","orcid":"0000-0003-1269-7354","position":5,"is_corresponding":false},{"id":1213684,"name":"Juhwan Lee","orcid":"0000-0003-0200-4884","position":6,"is_corresponding":false},{"id":295888,"name":"Sadeer Al‐Kindi","orcid":"0000-0002-1122-7695","position":7,"is_corresponding":false},{"id":103416,"name":"Sanjay Rajagopalan","orcid":"0000-0001-6669-8163","position":8,"is_corresponding":false},{"id":311212,"name":"David L. Wilson","orcid":"0000-0001-9763-1463","position":9,"is_corresponding":false},{"id":829571,"name":"Ammar Hoori","orcid":"0000-0002-8458-1214","position":10,"is_corresponding":false},{"id":829572,"name":"Tao Hu","orcid":"0009-0002-6661-2723","position":0,"is_corresponding":true}],"reference_count":36,"raw_metadata":null,"created_at":"2026-07-19T01:57:36.298171Z","pmid":"39372475","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}