{"doi":"10.1093/ehjci/jead229","title":"Distal-vessel fractional flow reserve by computed tomography to monitor epicardial coronary artery disease","abstract":"AIMS: Coronary computed tomography angiography (CTA) and fractional flow reserve by computed tomography (FFR-CT) are increasingly utilized to characterize coronary artery disease (CAD). We evaluated the feasibility of distal-vessel FFR-CT as an integrated measure of epicardial CAD that can be followed serially, assessed the CTA parameters that correlate with distal-vessel FFR-CT, and determined the combination of clinical and CTA parameters that best predict distal-vessel FFR-CT and distal-vessel FFR-CT changes. METHODS AND RESULTS: Patients (n = 71) who underwent serial CTA scans at ≥2 years interval (median = 5.2 years) over a 14-year period were included in this retrospective study. Coronary arteries were analysed blindly using artificial intelligence-enabled quantitative coronary CTA. Two investigators jointly determined the anatomic location and corresponding distal-vessel FFR-CT values at CT1 and CT2. A total of 45.3% had no significant change, 27.8% an improvement, and 26.9% a worsening in distal-vessel FFR-CT at CT2. Stepwise multiple logistic regression analysis identified a four-parameter model consisting of stenosis diameter ratio, lumen volume, low density plaque volume, and age, that best predicted distal-vessel FFR-CT ≤ 0.80 with an area under the curve (AUC) = 0.820 at CT1 and AUC = 0.799 at CT2. Improvement of distal-vessel FFR-CT was captured by a decrease in high-risk plaque and increases in lumen volume and remodelling index (AUC = 0.865), whereas increases in stenosis diameter ratio, medium density calcified plaque volume, and total cholesterol presaged worsening of distal-vessel FFR-CT (AUC = 0.707). CONCLUSION: Distal-vessel FFR-CT permits the integrative assessment of epicardial atherosclerotic plaque burden in a vessel-specific manner and can be followed serially to determine changes in global CAD.","journal":"European Heart Journal - Cardiovascular Imaging","year":2023,"id":344969,"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":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.961,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1083901,"name":"S Almeida","orcid":"0000-0002-0679-3190","position":1,"is_corresponding":false},{"id":451207,"name":"James Sayre","orcid":"0000-0001-6090-8713","position":2,"is_corresponding":false},{"id":1020802,"name":"Ronald P. Karlsberg","orcid":"0000-0003-4695-9092","position":3,"is_corresponding":false},{"id":584469,"name":"René R. Sevag Packard","orcid":"0000-0002-8520-5843","position":4,"is_corresponding":false},{"id":375857,"name":"Michael Chen","orcid":"0000-0001-7897-7733","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T01:11:35.133818Z","pmid":"37708371","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":[]}