Journal of Cardiovascular Computed Tomography最新文献

{"title":"Has pretest probability assessment prior to coronary artery disease testing become obsolete?","authors":"Armin Arbab-Zadeh","doi":"10.1016/j.jcct.2025.04.004","DOIUrl":"10.1016/j.jcct.2025.04.004","url":null,"abstract":"","PeriodicalId":49039,"journal":{"name":"Journal of Cardiovascular Computed Tomography","volume":"19 2","pages":"Pages 262-263"},"PeriodicalIF":5.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiac hydatid cyst causing coronary arterial compression","authors":"Nataraju Komallama Girish , Damandeep Singh , Niraj Nirmal Pandey , Aprateem Mukherjee , Rakesh Yadav , Priya Jagia","doi":"10.1016/j.jcct.2024.09.006","DOIUrl":"10.1016/j.jcct.2024.09.006","url":null,"abstract":"","PeriodicalId":49039,"journal":{"name":"Journal of Cardiovascular Computed Tomography","volume":"19 2","pages":"Pages 273-274"},"PeriodicalIF":5.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new business paradigm to make coronary CT angiography (CCTA) accessible to all","authors":"Ed Nicol ,&nbsp;Mark Ibrahim ,&nbsp;Benjamin J. Cohen ,&nbsp;Jonathan R. Weir McCall ,&nbsp;Ron Blankstein ,&nbsp;Leslee J. Shaw","doi":"10.1016/j.jcct.2024.09.008","DOIUrl":"10.1016/j.jcct.2024.09.008","url":null,"abstract":"<div><div>Recently, the Centers for Medicare and Medicaid proposed a classification change that, if enacted, could double reimbursement for coronary CT angiography (CCTA) in the U.S. [1]. With this comes the potential to realistically build an economically viable and sustainable model to deliver cardiac CT outside of major urban (hospital and private practice) and academic centers. The value of CCTA in reducing cardiovascular morbidity and mortality has been demonstrated in large, randomized control trials and real-world studies, but access to CCTA in rural, socially deprived, and low-resource settings (including poorer urban areas with a lack of specialist equipment and specialty-based services) remains a significant challenge. This paper discusses the end-to-end business aspects required to deliver a sustainable cardiac CT service in these areas, exploring technologist-delivered services, with remote support from physicians, and the potential to leverage developing artificial intelligence (AI) decision aid tools and mobile scanners.</div></div>","PeriodicalId":49039,"journal":{"name":"Journal of Cardiovascular Computed Tomography","volume":"19 2","pages":"Pages 256-261"},"PeriodicalIF":5.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142335240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAVE THE DATE_4C_FP","authors":"","doi":"10.1016/S1934-5925(25)00077-2","DOIUrl":"10.1016/S1934-5925(25)00077-2","url":null,"abstract":"","PeriodicalId":49039,"journal":{"name":"Journal of Cardiovascular Computed Tomography","volume":"19 2","pages":"Page OBC"},"PeriodicalIF":5.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Membranous septum area and the risk of conduction abnormalities following transcatheter aortic valve implantation","authors":"Christopher Pavitt ,&nbsp;Timothy Bagnall ,&nbsp;James Smethurst ,&nbsp;George Mcinerney-Baker ,&nbsp;Sandeep Arunothayaraj ,&nbsp;Christopher Broyd ,&nbsp;Michael Michail ,&nbsp;James Cockburn ,&nbsp;David Hildick-Smith","doi":"10.1016/j.jcct.2025.03.003","DOIUrl":"10.1016/j.jcct.2025.03.003","url":null,"abstract":"<div><h3>Background</h3><div>Conduction abnormalities (CA) after TAVI remain problematic. Membranous septum (MS) depth correlates inversely with new CA though within-patient variability exists.</div></div><div><h3>Objectives</h3><div>To determine the association of CT-derived MS area with new CA after TAVI.</div></div><div><h3>Methods</h3><div>MS depth was measured along its width (20 ​% intervals) to calculate MS area in 140 patients without CA. The primary outcome was PPI or new persistent LBBB at discharge.</div></div><div><h3>Results</h3><div>New CA occurred in 49 (35 ​%) patients of whom 10 (7.1 ​%) required PPI and 39 (27.9 ​%) developed persisting LBBB. MS area was significantly smaller in those with new CA (20.1 [8.6] vs. 41.2 [18.0] mm2; p ​&lt; ​0.01). By multivariable regression, a model including MS area and TAVI contact (MS width∗implant depth): MS area ratio showed better discrimination for new CA compared with a model including MS depth and MS depth – implant depth (AUC 0.89 [95 ​% CI 0.83–0.94] vs. 0.84 [95 ​% CI 0.76–0.90]; p ​= ​0.05, respectively). Optimal cut off point for correct classification of new CA for MS depth was 3.9 ​mm (sensitivity 73 ​%, specificity 76 ​%, PPV 58 ​% and NPV 84 ​%), 28.0 ​mm² for MS area (sensitivity 88 ​%, specificity 78 ​%, PPV 68 ​% and NPV 92 ​%) and 1.88 (sensitivity 63 ​%, specificity 81, PPV 77 ​% and NPV 68 ​%) for TAVI contact: MS area ratio. To minimize new CA, maximal valve implant depth should ≤ (1.88 ∗ MS area)/MS width.</div></div><div><h3>Conclusions</h3><div>Pre-procedural assessment of the MS area offers additional predictive value for development of new conduction abnormalities after TAVI when compared with MS depth and can guide implant depth.</div></div>","PeriodicalId":49039,"journal":{"name":"Journal of Cardiovascular Computed Tomography","volume":"19 2","pages":"Pages 247-255"},"PeriodicalIF":5.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning and computational fluid dynamics derived FFRCT demonstrate comparable diagnostic performance in patients with coronary artery disease; A Systematic Review and Meta-Analysis","authors":"Roozbeh Narimani-Javid ,&nbsp;Mehdi Moradi ,&nbsp;Mehrdad Mahalleh ,&nbsp;Roya Najafi-vosough ,&nbsp;Alireza Arzhangzadeh ,&nbsp;Omar Khalique ,&nbsp;Hamid Mojibian ,&nbsp;Toshiki Kuno ,&nbsp;Amr Mohsen ,&nbsp;Mahboob Alam ,&nbsp;Sasan Shafiei ,&nbsp;Nakisa Khansari ,&nbsp;Zahra Shaghaghi ,&nbsp;Salma Nozhat ,&nbsp;Kaveh Hosseini ,&nbsp;Seyed Kianoosh Hosseini","doi":"10.1016/j.jcct.2025.02.004","DOIUrl":"10.1016/j.jcct.2025.02.004","url":null,"abstract":"<div><h3>Background</h3><div>As a new noninvasive diagnostic technique, computed tomography-derived fraction flow reserve (FFRCT) has been used to identify hemodynamically significant coronary artery stenosis. FFRCT can be calculated using computational fluid dynamics (CFD) or machine learning (ML) approaches. It was hypothesized that ML-based FFRCT (FFRCT_ML) has comparable diagnostic performance with CFD-based FFRCT (FFRCT_CFD). We used invasive FFR as the reference test to evaluate the diagnostic performance of FFRCT_ML vs. FFRCT_CFD.</div></div><div><h3>Methods</h3><div>We searched PubMed, Cochrane Library, EMBASE, WOS, and Scopus for articles published until March 2024. We analyzed the synthesized sensitivity, specificity, and diagnostic odds ratio (DOR) of FFRCT_ML vs FFRCT_CFD at both the patient and vessel levels. We generated summary receiver operating characteristic curves (SROC) and then calculated the area under the curve (AUC).</div></div><div><h3>Results</h3><div>This meta-analysis included 23 studies reporting FFRCT_CFD diagnostic performance and 18 studies reporting FFRCT_ML diagnostic performance. In the FFRCT_CFD group, 2501 patients and 3764 vessels or lesions were analyzed. In the FFRCT_ML group, 1323 patients and 4194 vessels or lesions were analyzed. Our results showed that at the per-patient level, FFRCT_CFD and FFRCT_ML had comparable pooled specificity (Z ​= ​−0.59, P ​= ​0.55) and AUC (P ​= ​0.5). At the per-vessel level, FFRCTCFD and FFRCTML also showed comparable specificity (Z ​= ​0.94, P ​= ​0.34), DOR (Z ​= ​0.7, P ​= ​0.48), and AUC (P ​= ​0.74). However, the sensitivity of FFRCT_ML was significantly lower compared to FFRCT_CFD at both patient (Z ​= ​−3.85, P ​= ​0.0001) and vessel (Z ​= ​−2.05, P ​= ​0.04) levels.</div></div><div><h3>Conclusion</h3><div>The FFRCT_ML technique was comparable to standard CFD approaches in terms of AUC and specificity. However, it did not achieve the same level of sensitivity as FFRCT_CFD.</div></div>","PeriodicalId":49039,"journal":{"name":"Journal of Cardiovascular Computed Tomography","volume":"19 2","pages":"Pages 232-246"},"PeriodicalIF":5.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic performance of CCTA and CTP imaging for clinically suspected in-stent restenosis: A meta-analysis","authors":"Jorge Dahdal ,&nbsp;Ruurt A. Jukema ,&nbsp;Sharon Remmelzwaal ,&nbsp;Pieter G. Raijmakers ,&nbsp;Pim van der Harst ,&nbsp;Marco Guglielmo ,&nbsp;Maarten J. Cramer ,&nbsp;Steven A.J. Chamuleau ,&nbsp;Pepijn A. van Diemen ,&nbsp;Paul Knaapen ,&nbsp;Ibrahim Danad","doi":"10.1016/j.jcct.2024.10.014","DOIUrl":"10.1016/j.jcct.2024.10.014","url":null,"abstract":"<div><h3>Aims</h3><div>The objective of this study is to conduct a meta-analysis to assess the diagnostic performance of Coronary Computed Tomography Angiography (CCTA) and a hybrid approach that incorporates Computed Tomography Perfusion (CTP) in addition to CCTA (CCTA ​+ ​CTP) for the detection of in-stent restenosis (ISR), as defined by angiography.</div></div><div><h3>Methods</h3><div>A comprehensive search of articles identified 18,513 studies. After removing duplicates, title/abstract screening, and full-text review, 17 CCTA and 3 CCTA ​+ ​CTP studies were included. Only studies using ≥64-slices multidetector computed tomography (CT) were considered eligible.</div></div><div><h3>Results</h3><div>The per-patient ISR prevalence was 43 ​%, with 92 ​% of stents fully interpretable with CCTA. Meta-analysis exhibited a per-stent CCTA (n ​= ​2674) sensitivity of 90 ​% (95 ​% CI; 84–94 ​%), specificity of 89 ​% (95 ​% CI; 86–92 ​%), positive likelihood ratio of 7.17 (95 ​% CI; 5.24–9.61), negative likelihood ratio of 0.17 (95 ​% CI; 0.10–0.25), and diagnostic odds ratio of 45.7 (95 ​% CI; 22.71–82.43). Additional sensitivity analyses revealed no influence of stent diameter or strut thickness on the diagnostic yield of CCTA. The per-stent diagnostic performance of CCTA ​+ ​CTP (n ​= ​752) did not show differences compared to CCTA.</div></div><div><h3>Conclusions</h3><div>With currently utilized scanners, CCTA and CCTA ​+ ​CTP demonstrated high diagnostic performance for in-stent restenosis evaluation. Consequently, a history of previous stent implantation should not be an argument to preclude using these methods in clinically suspected patients.</div></div>","PeriodicalId":49039,"journal":{"name":"Journal of Cardiovascular Computed Tomography","volume":"19 2","pages":"Pages 183-190"},"PeriodicalIF":5.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization and scaling of coronary CT angiography workflows in a quaternary health system","authors":"Sadia Sultana,&nbsp;Mangun Randhawa,&nbsp;Dhrubajyoti Bandyopadhyay,&nbsp;Vinit Baliyan,&nbsp;Borek Foldyna,&nbsp;Nandini M. Meyersohn,&nbsp;Albree Tower-Rader,&nbsp;Michael Lu,&nbsp;Anushri Parakh,&nbsp;Sandeep Hedgire,&nbsp;Brian B. Ghoshhajra","doi":"10.1016/j.jcct.2025.02.007","DOIUrl":"10.1016/j.jcct.2025.02.007","url":null,"abstract":"","PeriodicalId":49039,"journal":{"name":"Journal of Cardiovascular Computed Tomography","volume":"19 2","pages":"Pages 271-272"},"PeriodicalIF":5.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Masthead-4C","authors":"","doi":"10.1016/S1934-5925(25)00067-X","DOIUrl":"10.1016/S1934-5925(25)00067-X","url":null,"abstract":"","PeriodicalId":49039,"journal":{"name":"Journal of Cardiovascular Computed Tomography","volume":"19 2","pages":"Pages A1-A2"},"PeriodicalIF":5.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of obstructive coronary artery disease using coronary calcification and epicardial adipose tissue assessments from CT calcium scoring scans","authors":"Juhwan Lee ,&nbsp;Tao Hu ,&nbsp;Michelle C. Williams ,&nbsp;Ammar Hoori ,&nbsp;Hao Wu ,&nbsp;Justin N. Kim ,&nbsp;David E. Newby ,&nbsp;Robert Gilkeson ,&nbsp;Sanjay Rajagopalan ,&nbsp;David L. Wilson","doi":"10.1016/j.jcct.2025.01.007","DOIUrl":"10.1016/j.jcct.2025.01.007","url":null,"abstract":"<div><h3>Background</h3><div>Low-cost/no-cost non-contrast CT calcium scoring (CTCS) exams can provide direct evidence of coronary atherosclerosis. In this study, using features from CTCS images, we developed a novel machine learning model to predict obstructive coronary artery disease (CAD), as defined by the coronary artery disease-reporting and data system (CAD-RADS).</div></div><div><h3>Methods</h3><div>This study analyzed 1324 patients from the SCOT-HEART trial who underwent both CTCS and CT angiography. Obstructive CAD was defined as CAD-RADS 4A-5, while CAD-RADS 0–3 were considered non-obstructive CAD. We analyzed clinical, Agatston-score-derived, and epicardial fat-omics features to predict obstructive CAD. The most predictive features were selected using elastic net logistic regression and used to train a CatBoost model. Model performance was evaluated using 1000 repeated five-fold cross-validation and survival analyses to predict major adverse cardiovascular event (MACE) and revascularization. Generalizability was assessed using an external validation set of 2316 patients for survival predictions.</div></div><div><h3>Results</h3><div>Among the 1324 patients, obstructive CAD was identified in 334 patients (25.2 ​%). Elastic net regression identified the top 14 features (5 clinical, 2 Agatston-score-derived, and 7 fat-omics). The proposed method achieved excellent performance for classifying obstructive CAD, with an AUC of 90.1 ​± ​0.9 ​% and sensitivity/specificity/accuracy of 83.5 ​± ​5.5 ​%/93.7 ​± ​1.9 ​%/82.4 ​± ​2.0 ​%. The inclusion of Agatston-score-derived and fat-omics features significantly improved classification performance. Survival analyses showed that both actual and predicted obstructive CAD significantly differentiated patients who experienced MACE and revascularization.</div></div><div><h3>Conclusions</h3><div>We developed a novel machine learning model to predict obstructive CAD from non-contrast CTCS scans. Our findings highlight the potential clinical benefits of CTCS imaging in identifying patients likely to benefit from advanced imaging.</div></div>","PeriodicalId":49039,"journal":{"name":"Journal of Cardiovascular Computed Tomography","volume":"19 2","pages":"Pages 224-231"},"PeriodicalIF":5.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}