{"doi":"10.1038/s41598-021-95026-2","title":"Patient-specific computational simulation of coronary artery bifurcation stenting","abstract":"Patient-specific and lesion-specific computational simulation of bifurcation stenting is an attractive approach to achieve individualized pre-procedural planning that could improve outcomes. The objectives of this work were to describe and validate a novel platform for fully computational patient-specific coronary bifurcation stenting. Our computational stent simulation platform was trained using n = 4 patient-specific bench bifurcation models (n = 17 simulations), and n = 5 clinical bifurcation cases (training group, n = 23 simulations). The platform was blindly tested in n = 5 clinical bifurcation cases (testing group, n = 29 simulations). A variety of stent platforms and stent techniques with 1- or 2-stents was used. Post-stenting imaging with micro-computed tomography (μCT) for bench group and optical coherence tomography (OCT) for clinical groups were used as reference for the training and testing of computational coronary bifurcation stenting. There was a very high agreement for mean lumen diameter (MLD) between stent simulations and post-stenting μCT in bench cases yielding an overall bias of 0.03 (- 0.28 to 0.34) mm. Similarly, there was a high agreement for MLD between stent simulation and OCT in clinical training group [bias 0.08 (- 0.24 to 0.41) mm], and clinical testing group [bias 0.08 (- 0.29 to 0.46) mm]. Quantitatively and qualitatively stent size and shape in computational stenting was in high agreement with clinical cases, yielding an overall bias of < 0.15 mm. Patient-specific computational stenting of coronary bifurcations is a feasible and accurate approach. Future clinical studies are warranted to investigate the ability of computational stenting simulations to guide decision-making in the cardiac catheterization laboratory and improve clinical outcomes.","journal":"Scientific Reports","year":2021,"id":158168,"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":47,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8916,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":472753,"name":"Wei Wu","orcid":"0000-0003-0734-1636","position":1,"is_corresponding":false},{"id":474028,"name":"Saurabhi Samant","orcid":null,"position":2,"is_corresponding":false},{"id":474031,"name":"Behram Khan","orcid":null,"position":3,"is_corresponding":false},{"id":338803,"name":"Ghassan S. 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Brilakis","orcid":"0000-0001-9416-9701","position":16,"is_corresponding":false},{"id":3774,"name":"Deepak L. Bhatt","orcid":"0000-0002-1278-6245","position":17,"is_corresponding":false},{"id":246555,"name":"George Dangas","orcid":"0000-0001-7502-8049","position":18,"is_corresponding":false},{"id":317152,"name":"Claudio Chiastra","orcid":"0000-0003-2070-6142","position":19,"is_corresponding":false},{"id":472760,"name":"Goran Stanković","orcid":"0000-0002-9414-0885","position":20,"is_corresponding":false},{"id":474032,"name":"Yves Louvard","orcid":null,"position":21,"is_corresponding":false},{"id":472761,"name":"Yiannis S. 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