{"doi":"10.1016/j.jcct.2024.04.006","title":"AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CACTM) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis","abstract":"INTRODUCTION: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF). METHODS: We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years. RESULTS: Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​< ​0.0001), and comparable to clinical risk factors (0.85, p ​= ​0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p ​< ​0.0001). CONCLUSION: AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.","journal":"Journal of cardiovascular computed tomography","year":2024,"id":423726,"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":23,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9611,"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":443588,"name":"Anthony P. Reeves","orcid":"0000-0002-1451-3080","position":1,"is_corresponding":false},{"id":24934,"name":"Matthew J. Budoff","orcid":"0000-0002-9616-1946","position":2,"is_corresponding":false},{"id":381244,"name":"Dong Li","orcid":"0000-0002-4347-7015","position":3,"is_corresponding":false},{"id":1219497,"name":"Kyle Atlas","orcid":"0000-0003-3061-3855","position":4,"is_corresponding":false},{"id":1211957,"name":"Chenyu Zhang","orcid":"0000-0002-9799-8772","position":5,"is_corresponding":false},{"id":1212652,"name":"Thomas Atlas","orcid":null,"position":6,"is_corresponding":false},{"id":444501,"name":"Sion Roy","orcid":null,"position":7,"is_corresponding":false},{"id":371559,"name":"Claudia I. Henschke","orcid":"0000-0002-6085-5305","position":8,"is_corresponding":false},{"id":259512,"name":"Nathan D. Wong","orcid":"0000-0003-1102-7324","position":9,"is_corresponding":false},{"id":278980,"name":"Christopher R. deFilippi","orcid":"0000-0002-0660-4943","position":10,"is_corresponding":false},{"id":38114,"name":"Daniel Levy","orcid":"0000-0003-1843-8724","position":11,"is_corresponding":false},{"id":371560,"name":"David F. Yankelevitz","orcid":"0000-0001-7364-4294","position":12,"is_corresponding":false},{"id":1211956,"name":"Morteza Naghavi","orcid":"0000-0001-8112-175X","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-19T01:58:01.889748Z","pmid":"38664073","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":[]}