Journal of cardiovascular computed tomography最新文献

{"title":"Guideline-endorsed but devalued: Fee schedule-wide and code-specific declines in Medicare payment for cardiac computed tomography, 2010-2026.","authors":"Muhammad Naeem, Robert J French, Carlos A Rojas, Matthew T Stib, Michael B Gotway, Clinton E Jokerst, Michael F Morris, Kristopher W Cummings","doi":"10.1016/j.jcct.2026.09.010","DOIUrl":"https://doi.org/10.1016/j.jcct.2026.09.010","url":null,"abstract":"<p><strong>Background: </strong>Coronary CT angiography (CCTA) received a Class 1 recommendation in the 2021 AHA/ACC chest pain guideline, yet the trajectory of Medicare payment for cardiac CT over the Category I era has not been systematically quantified.</p><p><strong>Methods: </strong>January-effective Physician Fee Schedule Relative Value Files were compiled for 2010 through 2026 for CPT 75571-75574, with FFR-CT (75580) and AI-based plaque analysis (75577) from their Category I dates, and CT angiography of the chest (71275) as a non-cardiac comparator. National unadjusted non-facility payment (all geographic indices 1.000) was calculated for global, professional, and technical components as total relative value units (RVUs) multiplied by the conversion factor, adjusted to 2026 dollars using the Consumer Price Index for All Urban Consumers (CPI-U), with the Medicare Economic Index (MEI) as a sensitivity analysis, and summarized as total real change and compound annual growth rate (CAGR). Claims volumes for 2013-2024 were obtained from CMS public use files, expressed per 1000 fee-for-service Part B enrollees, and the change in real allowed fee schedule spending was partitioned into unit revenue, volume, and interaction effects.</p><p><strong>Results: </strong>Inflation-adjusted global payment declined for all four core codes between 2010 and 2026: -68.3% for CCTA (75574; CAGR -6.93%/y), -53.6% for congenital cardiac CT (75573), -50.2% for structural cardiac CT (75572), and -27.4% for coronary calcium scoring (75571). Declines concentrated in the technical component (-74.8% for 75574; RVU reductions of up to 58.7%), whereas professional payment fell 35.9%-37.5% with work RVUs unchanged until the 2.5% efficiency adjustment of 2026, attributable to a 38.8% real decline in the conversion factor. Results were unchanged under the MEI (-68.6% for CCTA) and modestly attenuated under the MEI net of productivity (-65.0%). Chest CT angiography fell 57.9%, with a professional decline close to the cardiac codes (-40.7%) but a smaller technical decline (-62.5% vs -74.8%). The largest single-year reduction occurred in 2011 (-36.4% for 75574). In the five years after the 2021 guideline, real global payment fell a further 24.2%-34.2%. Over 2013-2024, CCTA volume rose 408% while mean real allowed charge per service fell 45.9%, a decline that partly reflects migration of billing to facility settings. Real allowed fee schedule spending rose $22.0 million, comprising a $51.4 million volume effect offset by unit revenue (-$5.8 million) and interaction (-$23.6 million) effects; hospital technical payment is not captured.</p><p><strong>Conclusion: </strong>Medicare payment for cardiac CT has declined steeply in real terms since Category I code adoption, and the decline accelerated during the period of strongest guideline endorsement. It exceeded the decline of a non-cardiac comparator and was insensitive to the choice of deflator. This discordance has implications for","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adult survival of unrepaired truncus arteriosus with interrupted aortic arch (Van Praagh type A4): A cardiac CT illustration.","authors":"Jia-Ke Li, Zhi-Gang Yang, Wei-Feng Yan","doi":"10.1016/j.jcct.2026.08.006","DOIUrl":"https://doi.org/10.1016/j.jcct.2026.08.006","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149077178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic performance of artificial intelligence-based coronary artery analysis in the emergency department: A multicenter study.","authors":"Ji Won Lee, Kyunghwa Han, Jin Young Kim, Kye Ho Lee, Dong Jin Im, Na Young Kim, Byoung Wook Choi, Max Schöbinger, Seongyong Pak, Jin Hur","doi":"10.1016/j.jcct.2026.09.005","DOIUrl":"https://doi.org/10.1016/j.jcct.2026.09.005","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>On-premise artificial intelligence (AI) software may enable rapid interpretation of coronary CT angiography (CCTA) in the emergency department (ED), although its diagnostic performance relative to invasive coronary angiography (ICA) remains uncertain. We aimed to evaluate the diagnostic performance of an AI-based coronary artery analysis software for detecting obstructive coronary artery disease (CAD) (CAD-RADS ≥3) in ED patients with acute chest pain, using expert consensus and ICA as separate reference standards.</p><p><strong>Materials and methods: </strong>In this retrospective multicenter study, 1193 patients with acute chest pain who underwent CCTA across four academic EDs (January 2019-August 2024) were analyzed using on-premise AI software. Diagnostic performance for detecting obstructive CAD was evaluated in two cohorts, using expert consensus (n ​= ​885) and ICA (n ​= ​308) as reference standards. Overall, 342 patients (28.7%) had obstructive CAD.</p><p><strong>Results: </strong>Using expert consensus, AI achieved a sensitivity of 79.5% and a negative predictive value (NPV) of 95.4%. With ICA as the reference, sensitivity and NPV were 94.6% and 89.2%, respectively. AI and expert radiologists showed comparable sensitivity (94.6% vs 95.2%; P ​= ​0.56), NPV (89.2% vs 91.4%; P ​= ​0.20), and area under the curve (0.87 vs 0.87; P ​= ​0.86), although AI demonstrated lower specificity (68.0% vs 78.7%) and positive predictive value (PPV) (81.8% vs 87.2%) (both P ​< ​0.001).</p><p><strong>Conclusion: </strong>On-premise AI provides high sensitivity and NPV for excluding obstructive CAD, comparable to expert radiologists, although specificity and PPV were significantly lower. These findings support its role as a second-reader and clinical decision-support tool in emergency settings.</p>","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149067007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond stenosis and plaque burden: Defining the biological phenotype of coronary atherosclerosis with CCTA.","authors":"Shaun Khanna, Aditya Bhat, Nitesh Nerlekar","doi":"10.1016/j.jcct.2026.09.004","DOIUrl":"https://doi.org/10.1016/j.jcct.2026.09.004","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photon-counting detector CT for prediction of valve dysfunction after transcatheter aortic valve implantation: Comparison with echocardiography.","authors":"Sardi Hyska, José Osoria-Velasquez, Muhammad Taha Hagar, Gavin W McCraw, Milán Vecsey-Nagy, Jens Ricke, Nicola Mueller, Tobias Bäuerle, Pál Maurovich-Horvat, Gerald S Laux, Michaela Hell, Tilman Emrich, Akos Varga-Szemes, Moritz C Halfmann","doi":"10.1016/j.jcct.2026.09.001","DOIUrl":"https://doi.org/10.1016/j.jcct.2026.09.001","url":null,"abstract":"<p><strong>Background: </strong>Noninvasive evaluation of suspected prosthetic valve dysfunction after transcatheter aortic valve implantation (TAVI) remains challenging because both anatomic and hemodynamic information are required for a conclusive diagnosis.</p><p><strong>Purpose: </strong>To evaluate the diagnostic accuracy of hemodynamic parameters derived from time-resolved photon-counting detector (PCD)-CT for functional TAVI-assessment.</p><p><strong>Methods: </strong>In this retrospective single-center study, consecutive post-TAVI PCD-CT examinations acquired between January 2023 and December 2025 were screened. After CT-based exclusion of cases with severe mitral regurgitation, systolic prosthetic valve orifice area was measured to calculate mean and maximum transvalvular pressure gradients (P_mean/P_max) from time-resolved left ventricular volumetry. Spearman correlation (ρ) and paired comparisons were performed with transthoracic echocardiography (TTE) as reference standard. Diagnostic performance of CT-derived P_mean for identifying potential valve dysfunction was assessed.</p><p><strong>Results: </strong>Seventy-one post-TAVI PCD-CT scans of 63 patients (median age, 77.0 [73.5-83.0] years; 37 women) were included. CT-derived parameters showed strong correlations and high agreement with their echocardiographic counterparts (orifice area: ρ = 0.73; P_mean: ρ = 0.85; P_max: ρ = 0.85; p < 0.001). There was no bias for orifice area or P_mean, whereas CT-derived P_max was higher than TTE-based P_max (median difference, 4.4 mmHg; p < 0.001). CT-derived P_mean showed high diagnostic performance for detection of potential valve dysfunction, with an area under the curve of 0.94, sensitivity/specificity of 88.9%/88.6%, accuracy of 88.7% and Youden index-derived optimal cutoff of 20.4 mmHg.</p><p><strong>Conclusion: </strong>Time-resolved cardiac PCD-CT for post-TAVI imaging enables robust derivation of mean transvalvular pressure gradients that correlate strongly with echocardiography, show excellent diagnostic accuracy for screening of valve dysfunction and may expand the diagnostic value of cardiac CT toward functional assessment.</p>","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blooming is more than partial volume averaging.","authors":"Michael A Winkler, Izabella Barreto, Ryan Masotti, Criz Santos, Neal L Weintraub","doi":"10.1016/j.jcct.2026.09.003","DOIUrl":"https://doi.org/10.1016/j.jcct.2026.09.003","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148979254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving coronary CT angiography image quality using video-coached breathing: A prospective study.","authors":"Zaheen Hossain, Yong Hao Yeo, Parvathy Sankar, Tai Metzger, Michael Gallagher, Richard Bloomingdale","doi":"10.1016/j.jcct.2026.08.015","DOIUrl":"https://doi.org/10.1016/j.jcct.2026.08.015","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glycemic control impacts plaque progression in premature coronary artery disease: A longitudinal CCTA analysis.","authors":"Qiyu Zhang, Jinying Zhou, Jingpu Wang, Fang Zhang, Rende Xu, Wei Gao, Yan Xia, Weifeng Guo, Yuanji Ma, Juying Qian, Chenguang Li, Junbo Ge","doi":"10.1016/j.jcct.2026.08.013","DOIUrl":"https://doi.org/10.1016/j.jcct.2026.08.013","url":null,"abstract":"<p><strong>Background: </strong>Premature coronary artery disease (PCAD) is an escalating public health issue. While diabetes mellitus (DM) is a known risk factor, the specific impact of glycemic control (GC) on those younger patients remains poorly defined.</p><p><strong>Aims: </strong>To investigate the impact of GC on plaque progression and major adverse cardiovascular events (MACE) in PCAD using serial coronary computed tomography angiography (CCTA).</p><p><strong>Methods: </strong>This cohort study enrolled PCAD patients with serial CCTA between 2019 and 2024. Poor GC was defined as HbA1c ≥ 7%. Plaque progression was defined as annualized change in total percent atheroma volume (ΔPAV) >0%. MACE included cardiac death, myocardial infarction, and unplanned revascularization. Causal mediation analysis was performed.</p><p><strong>Results: </strong>We retrospectively enrolled 225 PCAD patients (age 40.5 ± 3.6 years) with serial CCTA (mean interval: 2.0 years). After multivariable adjustment, poor GC independently predicted plaque progression (OR 2.81, 95%CI 1.21-6.55, p = 0.017) and 3-year MACE (HR 3.08, 95%CI 1.11-8.50, p = 0.030). Notably, patients with good GC showed comparable annualized ΔPAV to non-DM patients (p = 0.434), while poor GC was associated with global acceleration across all plaque subtypes. Mediation analysis revealed that plaque progression mediated 19.6% of the effect of poor GC on MACE (p = 0.044).</p><p><strong>Conclusions: </strong>In PCAD patients, poor GC was linked to accelerated plaque progression and a higher 3-year risk of MACE, with 19.6% of this association mediated by plaque progression. These findings underscore the necessity of strict glycemic management and the value of serial CCTA-based monitoring for personalized risk stratification in this population.</p>","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149019295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human intelligent use of artificial intelligence.","authors":"Ronen Rubinshtein","doi":"10.1016/j.jcct.2026.08.016","DOIUrl":"https://doi.org/10.1016/j.jcct.2026.08.016","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretation and reporting of coronary computed tomographic angiography (2026 update): An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT).","authors":"Prabhakar Shantha Rajiah, Hatem Alkadhi, Daniele Andreini, Renée P Bullock-Palmer, Kavitha M Chinnaiyan, Benjamin J W Chow, Ricardo C Cury, Sandeep Hedgire, Lynne M Hurwitz Koweek, Koen Nieman, Dominika Suchá, Michelle C Williams, Pamela K Woodard, Quynh A Truong","doi":"10.1016/j.jcct.2026.08.014","DOIUrl":"https://doi.org/10.1016/j.jcct.2026.08.014","url":null,"abstract":"","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149168558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}