1
|
Hou C, Lu Y, Ma Y, Li Q, Liu C, Lu M, Cao C, Liu J. Investigation of the predictive value of a novel algorithm based on coronary CT angiography regarding fractional flow reserve and revascularization in patients with stable coronary artery disease. Heart Vessels 2024; 39:195-205. [PMID: 37897523 DOI: 10.1007/s00380-023-02324-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 09/28/2023] [Indexed: 10/30/2023]
Abstract
Fractional flow reserve (FFR) has been established as a gold standard for functional coronary ischemia. At present, the FFR can be calculated from coronary computed tomography angiography (CCTA) images (CT-FFR). Previous studies have suggested that CT-FFR outperforms CCTA and invasive coronary angiography (ICA) in determining hemodynamic significance of stenoses. Recently, a novel automatical algorithm of CT-FFR called RuiXin-FFR has been developed. The present study is designed to investigate the predictive value of this algorithm and its value in therapeutic decision making. The present study retrospectively included 166 patients with stable coronary artery disease (CAD) who underwent CCTA screening and diagnostic ICA examination at Peking University People's Hospital, in 73 of whom wire-derived FFR was also measured. CT-FFR analyses were performed with a dedicated software. All patients were followed up for at least 1 year. We validated the accuracy of RuiXin-FFR with invasive FFR as the standard of reference, and investigated the role of RuiXin-FFR in predicting treatment strategy and long-term prognosis. The mean age of the patients was 63.3 years with 63.9% male. The CT-FFR showed a moderate correlation with wire-derived FFR (r = 0.542, p < 0.0001) and diagnostic accuracy of 87.6% to predict myocardial ischemia (AUC: 0.839, 95% CI 0.728-0.950), which was significantly higher than CCTA and ICA. In the multivariate logistic regression analysis, CT-FFR ≤ 0.80 was an independent predictor of undergoing coronary revascularization (OR: 45.54, 95% CI 12.03-172.38, p < 0.0001), whereas CT-FFR > 0.80 was an independent predictor of non-obstructive CAD (OR: 14.67, 95% CI 5.42-39.72, p < 0.0001). Reserving ICA and revascularization for vessels with positive CT-FFR could have reduced the rate of ICA by 29.6%, lowered the rate of ICA in vessels without stenosis > 50% by 11.7%, and increased the rate of revascularization in patients receiving ICA by 21.2%. The average follow-up was 23.7 months, and major adverse cardiovascular events (MACE) occurred in 11 patients. The rate of MACE was significantly lower in patients with CT-FFR > 0.80. The new algorithm of CT-FFR can be used to predict the invasive FFR. The RuiXin-FFR can also provide useful information for the screening of patients in whom further ICA is indeed needed and prognosis evaluation.
Collapse
Affiliation(s)
- Chang Hou
- Department of Cardiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, China
- Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Peking University People's Hospital, Beijing, China
| | - Yahui Lu
- Department of Cardiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, China
- Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Peking University People's Hospital, Beijing, China
| | - Yuliang Ma
- Department of Cardiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, China
- Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Peking University People's Hospital, Beijing, China
| | - Qi Li
- Department of Cardiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, China
- Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Peking University People's Hospital, Beijing, China
| | - Chuanfen Liu
- Department of Cardiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, China
- Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Peking University People's Hospital, Beijing, China
| | - Mingyu Lu
- Department of Cardiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, China
- Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Peking University People's Hospital, Beijing, China
| | - Chengfu Cao
- Department of Cardiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, China
- Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Peking University People's Hospital, Beijing, China
| | - Jian Liu
- Department of Cardiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, China.
- Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Peking University People's Hospital, Beijing, China.
| |
Collapse
|
2
|
Chen C, Chen M, Tao Q, Hu S, Hu C. Non-contrast CT-based radiomics nomogram of pericoronary adipose tissue for predicting haemodynamically significant coronary stenosis in patients with type 2 diabetes. BMC Med Imaging 2023; 23:99. [PMID: 37507716 PMCID: PMC10386261 DOI: 10.1186/s12880-023-01051-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) patients have a higher incidence of coronary artery disease than the general population. The aim of this study was to develop a radiomics nomogram of pericoronary adipose tissue (PCAT) based on non-contrast CT to predict haemodynamically significant coronary stenosis in T2DM patients. METHODS The study enrolled 215 T2DM patients who underwent non-contrast CT and coronary computed tomography angiography (CCTA). CCTA derived fractional flow reserve (FFRCT) ≤ 0.80 was defined as hemodynamically significant stenosis.1691 radiomics features were extracted from PCAT on non-contrast CT. Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were used to select useful radiomics features to construct Radscore. Logistic regression was applied to select significant factors among Radscore, fat attenuation index (FAI) and coronary artery calcium score (CACS) to construct radiomics nomogram. RESULTS Radscore [odds ratio (OR) = 2.84; P < 0.001] and CACS (OR = 1.00; P = 0.023) were identified as independent predictors to construct the radiomics nomogram. The radiomics nomogram showed excellent performance [training cohort: area under the curve (AUC) = 0.81; 95% CI: 0.76-0.86; validation cohort: AUC = 0.83; 95%CI: 0.76-0.90] to predict haemodynamically significant coronary stenosis in patients with T2DM. Decision curve analysis demonstrated high clinical value of the radiomics nomogram. CONCLUSION The non-contrast CT-based radiomics nomogram of PCAT could effectively predict haemodynamically significant coronary stenosis in patients with T2DM, which might be a potential noninvasive tool for screening of high-risk patients.
Collapse
Affiliation(s)
- Can Chen
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, China
| | - Meng Chen
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, China
| | - Qing Tao
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, China
| | - Su Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, China.
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, China.
| |
Collapse
|
3
|
Counseller Q, Aboelkassem Y. Recent technologies in cardiac imaging. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 4:984492. [PMID: 36704232 PMCID: PMC9872125 DOI: 10.3389/fmedt.2022.984492] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 11/30/2022] [Indexed: 01/11/2023] Open
Abstract
Cardiac imaging allows physicians to view the structure and function of the heart to detect various heart abnormalities, ranging from inefficiencies in contraction, regulation of volumetric input and output of blood, deficits in valve function and structure, accumulation of plaque in arteries, and more. Commonly used cardiovascular imaging techniques include x-ray, computed tomography (CT), magnetic resonance imaging (MRI), echocardiogram, and positron emission tomography (PET)/single-photon emission computed tomography (SPECT). More recently, even more tools are at our disposal for investigating the heart's physiology, performance, structure, and function due to technological advancements. This review study summarizes cardiac imaging techniques with a particular interest in MRI and CT, noting each tool's origin, benefits, downfalls, clinical application, and advancement of cardiac imaging in the near future.
Collapse
Affiliation(s)
- Quinn Counseller
- College of Health Sciences, University of Michigan, Flint, MI, United States
| | - Yasser Aboelkassem
- College of Innovation and Technology, University of Michigan, Flint, MI, United States
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
4
|
Muscogiuri G, Volpato V, Cau R, Chiesa M, Saba L, Guglielmo M, Senatieri A, Chierchia G, Pontone G, Dell’Aversana S, Schoepf UJ, Andrews MG, Basile P, Guaricci AI, Marra P, Muraru D, Badano LP, Sironi S. Application of AI in cardiovascular multimodality imaging. Heliyon 2022; 8:e10872. [PMID: 36267381 PMCID: PMC9576885 DOI: 10.1016/j.heliyon.2022.e10872] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/23/2022] [Accepted: 09/27/2022] [Indexed: 12/16/2022] Open
Abstract
Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.
Collapse
Affiliation(s)
- Giuseppe Muscogiuri
- Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, Italy,School of Medicine, University of Milano-Bicocca, Milan, Italy,Corresponding author.
| | - Valentina Volpato
- Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy,IRCCS Ospedale Galeazzi - Sant'Ambrogio, University Cardiology Department, Milan, Italy
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | | | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | - Marco Guglielmo
- Department of Cardiology, Division of Heart and Lungs, Utrecht University, Utrecht University Medical Center, Utrecht, the Netherlands
| | | | | | | | - Serena Dell’Aversana
- Department of Radiology, Ospedale S. Maria Delle Grazie - ASL Napoli 2 Nord, Pozzuoli, Italy
| | - U. Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Mason G. Andrews
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Paolo Basile
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Paolo Marra
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Denisa Muraru
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Luigi P. Badano
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Sandro Sironi
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| |
Collapse
|
5
|
Feng Y, Xu Z, Zhang L, Zhang Y, Xu H, Zhuang X, Zhang H, Xie X. Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia. Front Physiol 2022; 13:980996. [PMID: 36225300 PMCID: PMC9550214 DOI: 10.3389/fphys.2022.980996] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives: To determine the association between radiomics signature (Rad-signature) of pericoronary tissue (PCT) in coronary computed tomography angiography (CCTA) and CT-derived fractional flow reserve (CT-FFR), and explore the influential factors of functional ischemia.Methods: We retrospectively included 350 patients who underwent CCTA from 2 centers, consisting of the training (n = 134), validation (n = 66), and testing (with CCTA and invasive coronary angiography, n = 150) groups. After evaluating coronary stenosis level in CCTA (anatomical CT), pericoronary fat attenuation index (FAI), and CT-FFR, we extracted 1,691 radiomic features from PCT. By accumulating and weighting the most contributive features to functional ischemia (CT-FFR ≤ 0.8) the Rad-signature was established using Boruta integrating with a random forest algorithm. Another 45 patients who underwent CCTA and invasive FFR were included to assure the performance of Rad-signature.Results: A total of 1046 vessels in 350 patients were analyzed, and functional ischemia was identified in 241/1046 (23.0%) vessels and 179/350 (51.1%) patients. From the 47 features highly relevant to functional ischemia, the top-8 contributive features were selected to establish Rad-signature. At the vessel level, the area under the curve (AUC) of Rad-signature to discriminate functional ischemia was 0.83, 0.82, and 0.82 in the training, validation, and testing groups, higher than 0.55, 0.55, and 0.52 of FAI (p < 0.001), respectively, and was higher than 0.72 of anatomical CT in the testing group (p = 0.017). The AUC of the combined model (Rad-signature + anatomical CT) was 0.86, 0.85, and 0.83, respectively, significantly higher than that of anatomical CT and FAI (p < 0.05). In the CCTA-invasive FFR group, using invasive FFR as the standard, the mean AUC of Rad-signature was 0.83 ± 0.02. At the patient level, multivariate logistic regression analysis showed that Rad-signature of left anterior descending (LAD) [odds ratio (OR) = 1.72; p = 0.012] and anatomical CT (OR = 3.53; p < 0.001) were independent influential factors of functional ischemia (p < 0.05). In the subgroup of nonobstructive (stenosis <50% in invasive coronary angiography) and obstructive (≥50%) cases of the testing group, the independent factor of functional ischemia was FAI of LAD (OR = 1.10; p = 0.041) and Rad-signature of LAD (OR = 2.45; p = 0.042), respectively.Conclusion: The machine-learning-derived Rad-signature of PCT in CCTA demonstrates significant association with functional ischemia.
Collapse
Affiliation(s)
- Yan Feng
- Department of Radiology, Shanghai General Hospital of Nanjing Medical University, Shanghai, China
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhihan Xu
- DI CT Collaboration, Siemens Healthineers Ltd, Shanghai, China
| | - Lin Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yaping Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Xu
- Department of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaozhong Zhuang
- Department of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Zhang
- Department of Radiology, Shanghai General Hospital of Nanjing Medical University, Shanghai, China
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Xueqian Xie, ; Hao Zhang,
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital of Nanjing Medical University, Shanghai, China
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Xueqian Xie, ; Hao Zhang,
| |
Collapse
|
6
|
Shi K, Yang FF, Si N, Zhu CT, Li N, Dong XL, Guo Y, Zhang T. Effect of 320-row CT reconstruction technology on fractional flow reserve derived from coronary CT angiography based on machine learning: single- versus multiple-cardiac periodic images. Quant Imaging Med Surg 2022; 12:3092-3103. [PMID: 35655842 PMCID: PMC9131332 DOI: 10.21037/qims-21-659] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 03/02/2022] [Indexed: 10/13/2023]
Abstract
BACKGROUND Fractional flow reserve derived from computed tomography (CT-FFR) can be used to noninvasively evaluate the functions of coronary arteries and has been widely welcomed in the field of cardiovascular research. However, whether different image reconstruction schemes have an effect on CT-FFR analysis through single- and multiple-cardiac periodic images in the same patient has not been investigated. METHODS This study retrospectively enrolled 122 patients who underwent 320-row computed tomography (CT) examination with both single- and multiple-cardiac periodic reconstruction schemes; a total of 366 coronary arteries were analyzed. The lowest CT-FFR values of each vessel and the poststenosis CT-FFR values of the lesion-specific coronary artery were measured using the two reconstruction techniques. The Wilcoxon signed-rank test was used to compare differences in CT-FFR values between the two reconstruction techniques. Spearman correlation analysis was performed to determine the relationship between CT-FFR values derived using the two methods. Bland-Altman and intraclass correlation coefficient (ICC) analyses were performed to evaluate the consistency of CT-FFR values. RESULTS In all blood vessels, the lowest CT-FFR values showed no significant differences between the two reconstruction techniques in the left anterior descending artery (LAD; P=0.65), left circumflex artery (LCx; P=0.46), or right coronary artery (RCA; P=0.22). In blood vessels with atherosclerotic plaques, the poststenosis CT-FFR values (2 cm distal to the maximum stenosis) exhibited no significant differences between the two reconstruction techniques in the LAD (P=0.78), LCx (P=1.00), or RCA (P=1.00). The mean CT-FFR values of single- and multiple-cardiac periodic images showed excellent correlation and minimal bias in all groups. CONCLUSIONS CT-FFR analysis based on an artificial intelligence deep learning neural network is stable and not affected by the type of 320-row CT reconstruction technology.
Collapse
Affiliation(s)
- Ke Shi
- Department of Radiology, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Feng-Feng Yang
- Department of Radiology, The Second Hospital, Tianjin Medical University, Tianjin, China
| | - Nuo Si
- Department of Radiology, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Chen-Tao Zhu
- Department of Radiology, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Na Li
- Department of Radiology, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Xiao-Lin Dong
- Department of Radiology, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Tong Zhang
- Department of Radiology, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| |
Collapse
|
7
|
Muscogiuri G, Guglielmo M, Serra A, Gatti M, Volpato V, Schoepf UJ, Saba L, Cau R, Faletti R, McGill LJ, De Cecco CN, Pontone G, Dell’Aversana S, Sironi S. Multimodality Imaging in Ischemic Chronic Cardiomyopathy. J Imaging 2022; 8:jimaging8020035. [PMID: 35200737 PMCID: PMC8877428 DOI: 10.3390/jimaging8020035] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/23/2022] [Accepted: 01/27/2022] [Indexed: 02/01/2023] Open
Abstract
Ischemic chronic cardiomyopathy (ICC) is still one of the most common cardiac diseases leading to the development of myocardial ischemia, infarction, or heart failure. The application of several imaging modalities can provide information regarding coronary anatomy, coronary artery disease, myocardial ischemia and tissue characterization. In particular, coronary computed tomography angiography (CCTA) can provide information regarding coronary plaque stenosis, its composition, and the possible evaluation of myocardial ischemia using fractional flow reserve CT or CT perfusion. Cardiac magnetic resonance (CMR) can be used to evaluate cardiac function as well as the presence of ischemia. In addition, CMR can be used to characterize the myocardial tissue of hibernated or infarcted myocardium. Echocardiography is the most widely used technique to achieve information regarding function and myocardial wall motion abnormalities during myocardial ischemia. Nuclear medicine can be used to evaluate perfusion in both qualitative and quantitative assessment. In this review we aim to provide an overview regarding the different noninvasive imaging techniques for the evaluation of ICC, providing information ranging from the anatomical assessment of coronary artery arteries to the assessment of ischemic myocardium and myocardial infarction. In particular this review is going to show the different noninvasive approaches based on the specific clinical history of patients with ICC.
Collapse
Affiliation(s)
- Giuseppe Muscogiuri
- Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, University Milano Bicocca, 20149 Milan, Italy
- Correspondence: ; Tel.: +39-329-404-9840
| | - Marco Guglielmo
- Department of Cardiology, Division of Heart and Lungs, Utrecht University, Utrecht University Medical Center, 3584 Utrecht, The Netherlands;
| | - Alessandra Serra
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, 09042 Cagliari, Italy; (A.S.); (L.S.); (R.C.)
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy; (M.G.); (R.F.)
| | - Valentina Volpato
- Department of Cardiac, Neurological and Metabolic Sciences, Istituto Auxologico Italiano IRCCS, San Luca Hospital, University Milano Bicocca, 20149 Milan, Italy;
| | - Uwe Joseph Schoepf
- Department of Radiology and Radiological Science, MUSC Ashley River Tower, Medical University of South Carolina, 25 Courtenay Dr, Charleston, SC 29425, USA; (U.J.S.); (L.J.M.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, 09042 Cagliari, Italy; (A.S.); (L.S.); (R.C.)
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, 09042 Cagliari, Italy; (A.S.); (L.S.); (R.C.)
| | - Riccardo Faletti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy; (M.G.); (R.F.)
| | - Liam J. McGill
- Department of Radiology and Radiological Science, MUSC Ashley River Tower, Medical University of South Carolina, 25 Courtenay Dr, Charleston, SC 29425, USA; (U.J.S.); (L.J.M.)
| | - Carlo Nicola De Cecco
- Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA;
| | | | - Serena Dell’Aversana
- Department of Radiology, Ospedale S. Maria Delle Grazie—ASL Napoli 2 Nord, 80078 Pozzuoli, Italy;
| | - Sandro Sironi
- School of Medicine and Post Graduate School of Diagnostic Radiology, University of Milano-Bicocca, 20126 Milan, Italy;
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| |
Collapse
|
8
|
Peper J, Becker LM, van Kuijk JP, Leiner T, Swaans MJ. Fractional Flow Reserve: Patient Selection and Perspectives. Vasc Health Risk Manag 2021; 17:817-831. [PMID: 34934324 PMCID: PMC8684425 DOI: 10.2147/vhrm.s286916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 11/30/2021] [Indexed: 01/10/2023] Open
Abstract
The aim of this review was to discuss the current practice and patient selection for invasive FFR, new techniques to estimate invasive FFR and future of coronary physiology tests. We elaborate on the indication and application of FFR and on the contraindications and concerns in certain patient populations.
Collapse
Affiliation(s)
- Joyce Peper
- Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Leonie M Becker
- Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jan-Peter van Kuijk
- Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martin J Swaans
- Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands
| |
Collapse
|
9
|
Gabara L, Hinton J, Gilpin TR, Curzen N. Fractional flow reserve derived from coronary computed tomography: where are we now and where are we heading? Future Cardiol 2020; 17:723-741. [PMID: 32951466 DOI: 10.2217/fca-2020-0058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Computed tomography coronary angiography is emerging as the preferred diagnostic tool for patients with chest pain. Additional knowledge of the extent and distribution of myocardial ischemia enables tailored patient management. Computed tomography-derived fractional flow reserve (FFRCT) employs computed tomography coronary angiography raw data processed via complex computational fluid dynamics and produces a surrogate of the invasive fractional flow reserve (FFR) thus delivering anatomical and physiological assessment in a single test. FFRCT has been extensively validated against invasive FFR and observational clinical studies have consistently demonstrated its utility as gatekeeper to invasive angiography while also reducing downstream clinical events and costs. Novel workstation-based models of estimating FFR are now being tested. Ongoing and future research results will define their role in clinical practice.
Collapse
Affiliation(s)
- Lavinia Gabara
- Coronary Research Group, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK.,Faculty of Medicine, University of Southampton, 12 University Road, Southampton, SO17 1BJ, UK
| | - Jonathan Hinton
- Coronary Research Group, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK.,Faculty of Medicine, University of Southampton, 12 University Road, Southampton, SO17 1BJ, UK
| | - Thomas Russell Gilpin
- Coronary Research Group, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK.,Faculty of Medicine, University of Southampton, 12 University Road, Southampton, SO17 1BJ, UK
| | - Nick Curzen
- Coronary Research Group, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK.,Faculty of Medicine, University of Southampton, 12 University Road, Southampton, SO17 1BJ, UK
| |
Collapse
|
10
|
Updates on Fractional Flow Reserve Derived by CT (FFRCT). CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2020. [DOI: 10.1007/s11936-020-00816-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|