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CAE can affect atherosclerotic plaques through hemodynamic changes. However, no study has evaluated the characteristics of CAE with atherosclerotic plaques. Therefore, we aimed to disclose the characteristics of atherosclerotic plaques in patients with CAE using optical coherence tomography (OCT). We evaluated patients with CAE, confirmed by coronary angiography, who underwent pre-intervention OCT between April 2015 and April 2021. Each millimeter of the OCT images was analyzed to assess the characteristics of CAEs, plaque phenotypes, and plaque vulnerability. A total of 286 patients (344 coronary vessels) met our criteria, 82.87% of whom were men. Right coronary artery lesions were the most common, comprising 44.48% (n = 153) of the total. We found 329 CAE vessels with plaques, accounting for 95.64% of the coronary vessels. After grouping CAEs and plaques by their relative positions, we found that the length of plaques within CAE lesions was longer than that of plaques in other sites (P < 0.001). Plaques within CAE lesions had greater maximum lipid angles and lipid indexes (P = 0.007, P = 0.004, respectively) than those on other sites. This study revealed the most common vascular and morphological characteristics of CAE. While the accompanying plaques were not affected by the location or morphology of the CAE vessels, they were affected by their position relative to the CAE lesion.\u003C\u002Fjats:p>",{"EN":57},"Characteristics of coronary artery ectasia and accompanying plaques: an optical coherence tomography study",{"VOID":59},"37099062",{"VOID":61},"10.1007\u002Fs10554-023-02835-9","PUBLICATION","VERIFIED","Auto 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al (1983) Aneurysmal coronary artery disease. Circulation 67(1):134–138",{"doi":235},"10.1161\u002F01.CIR.67.1.134",{"id":18,"text":237,"url":18,"identifiers":238},"Markis JE, Joffe CD, Cohn PF, Feen DJ, Herman MV, Gorlin R (1976) Clinical significance of coronary arterial ectasia. Am J Cardiol 37(2):217–222",{"doi":239},"10.1016\u002F0002-9149(76)90315-5",{"id":18,"text":241,"url":18,"identifiers":242},"Roberts WC (2011) Natural history, clinical consequences, and morphologic features of coronary arterial aneurysms in adults. Am J Cardiol 108:814–821. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.amjcard.2011.05.009",{"doi":243},"10.1016\u002Fj.amjcard.2011.05.009",{"id":18,"text":245,"url":18,"identifiers":246},"Altinbas A, Nazli C, Kinay O et al (2004) Predictors of exercise induced myocardial ischemia in patients with isolated coronary artery ectasia. Int J Cardiovasc Imaging 20:3–17. https:\u002F\u002Fdoi.org\u002F10.1023\u002Fb:caim.0000013158.15961.43",{"doi":247},"10.1023\u002Fb:caim.0000013158.15961.43",{"id":18,"text":249,"url":18,"identifiers":250},"Siasos G, Tsigkou V, Zaromytidou M et al (2018) Role of local coronary blood flow patterns and shear stress on the development of microvascular and epicardial endothelial dysfunction and coronary plaque. Curr Opin Cardiol 33:638–644. https:\u002F\u002Fdoi.org\u002F10.1097\u002FHCO.0000000000000571",{"doi":251},"10.1097\u002FHCO.0000000000000571",{"id":18,"text":253,"url":18,"identifiers":254},"Doi T, Kataoka Y, Noguchi T et al (2017) Coronary artery ectasia predicts future cardiac events in patients with acute myocardial infarction. Arterioscler Thromb Vasc Biol 37:2350–2355. https:\u002F\u002Fdoi.org\u002F10.1161\u002FATVBAHA.117.309683",{"doi":255},"10.1161\u002FATVBAHA.117.309683",{"id":18,"text":257,"url":18,"identifiers":258},"Syed M, Lesch M (1997) Coronary artery aneurysm: a review. Prog Cardiovasc Dis 40:77–84. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0033-0620(97)80024-2",{"doi":259},"10.1016\u002Fs0033-0620(97)80024-2",{"id":18,"text":261,"url":18,"identifiers":262},"Alfonso F, Pérez-Vizcayno MJ, Ruiz M et al (2009) Coronary aneurysms after drug-eluting stent implantation: clinical, angiographic, and intravascular ultrasound findings. J Am Coll Cardiol 53:2053–2060. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2009.01.069",{"doi":263},"10.1016\u002Fj.jacc.2009.01.069",{"id":18,"text":265,"url":18,"identifiers":266},"Newburger JW, Takahashi M, Burns JC (2016) Kawasaki disease. J Am Coll Cardiol 67:1738–1749. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2015.12.073",{"doi":267},"10.1016\u002Fj.jacc.2015.12.073",{"id":18,"text":269,"url":18,"identifiers":270},"McCrindle BW, Rowley AH, Newburger JW et al (2017) Diagnosis, treatment, and long-term management of kawasaki disease: a scientific statement for health professionals from the american heart association. Circulation 135:e927-927e999. https:\u002F\u002Fdoi.org\u002F10.1161\u002FCIR.0000000000000484",{"doi":271},"10.1161\u002FCIR.0000000000000484",{"id":18,"text":273,"url":18,"identifiers":274},"Robinson JG, Heistad DD, Fox KA (2015) Atherosclerosis stabilization with PCSK-9 inhibition: an evolving concept for cardiovascular prevention. Atherosclerosis 243(2):593–597. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atherosclerosis.2015.10.023",{"doi":275},"10.1016\u002Fj.atherosclerosis.2015.10.023",{"id":18,"text":277,"url":18,"identifiers":278},"Yetkin E, Acikgoz N, Sivri N et al (2007) Increased plasma levels of cystatin C and transforming growth factor-beta1 in patients with coronary artery ectasia: can there be a potential interaction between cystatin C and transforming growth factor-beta1. Coron Artery Dis 18:211–214. https:\u002F\u002Fdoi.org\u002F10.1097\u002FMCA.0b013e328087bd98",{"doi":279},"10.1097\u002FMCA.0b013e328087bd98",{"id":18,"text":281,"url":18,"identifiers":282},"Reji R, Nguyen M (2018) Medically managed coronary artery aneurysm without concomitant stenosis. BMJ Case Rep. https:\u002F\u002Fdoi.org\u002F10.1136\u002Fbcr-2018-224244",{"doi":283},"10.1136\u002Fbcr-2018-224244",{"id":18,"text":285,"url":18,"identifiers":286},"Fan T, Zhou Z, Fang W, Wang W, Xu L, Huo Y (2019) Morphometry and hemodynamics of coronary artery aneurysms caused by atherosclerosis. Atherosclerosis 284:187–193. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atherosclerosis.2019.03.001",{"doi":287},"10.1016\u002Fj.atherosclerosis.2019.03.001",{"id":18,"text":289,"url":18,"identifiers":290},"Gulati M, Levy PD, Mukherjee D et al (2021) 2021 AHA\u002FACC\u002FASE\u002FCHEST\u002FSAEM\u002FSCCT\u002FSCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology\u002FAmerican Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol 78:e187-187e285. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2021.07.053",{"doi":291},"10.1016\u002Fj.jacc.2021.07.053",{"id":18,"text":293,"url":18,"identifiers":294},"Liu R, Gao X, Liang S, Zhao H (2022) Five-years’ prognostic analysis for coronary artery ectasia patients with coronary atherosclerosis: a retrospective cohort study. Front Cardiovasc Med 9:950291. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffcvm.2022.950291",{"doi":295},"10.3389\u002Ffcvm.2022.950291",{"id":18,"text":297,"url":18,"identifiers":298},"Ren J, Shishkov M, Villiger ML, Otsuka K, Nadkarni SK, Bouma BE (2021) Single-catheter dual-modality intravascular imaging combining IVUS and OFDI: a holistic structural visualisation of coronary arteries. EuroIntervention 17:e919-919e922. https:\u002F\u002Fdoi.org\u002F10.4244\u002FEIJ-D-20-00990",{"doi":299},"10.4244\u002FEIJ-D-20-00990",{"id":18,"text":301,"url":18,"identifiers":302},"Doradla P, Otsuka K, Nadkarni A et al (2020) Biomechanical stress profiling of coronary atherosclerosis: identifying a multifactorial metric to evaluate plaque rupture risk. JACC Cardiovasc Imaging 13:804–816. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jcmg.2019.01.033",{"doi":303},"10.1016\u002Fj.jcmg.2019.01.033",false,{"id":306,"createTime":307,"updateTime":308,"relativeEntities":309,"slug":310,"properties":311,"entityType":62,"verifyStatus":63,"verifyTime":308,"verifyNote":64,"syncStatus":17,"languages":323,"translateLanguages":18,"viewCount":19,"primaryUrl":324,"fullTextUrl":18,"authors":325,"publicationType":213,"publisherRelationship":526,"citationCount":19,"citationInfo":548,"publishDate":18,"publishYear":18,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":550,"isForceReanalyzing":304},"e6f1302d-84e5-4263-8f75-d5507f1873dd","2024-04-19T08:52:23.985+00:00","2024-12-25T23:44:29.024+00:00",[],"Three-dimensional-echocardiography-for-the-evaluation-of-hypertrophic-cardiomyopathy-patients-relation-to-symptoms-and-exercise-capacity",{"keywords":312,"openalex":313,"abstract":315,"title":317,"pm":319,"doi":321},{},{"VOID":314},"W4387541226",{"EN":316},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>Patients with hypertrophic cardiomyopathy may exhibit impaired functional capacity, associated with increased morbidity and mortality. Systolic function is one of the determinants of functional capacity. Early identification of systolic disfunction may identify patients at risk for adverse outcomes. Myocardial deformation parameters, derived from three-dimensional (3D) speckle-tracking echocardiography (3DSTE) are useful tools to assess left ventricular systolic function, and are often abnormal before a decline in ejection fraction is seen. The aim of this study was to evaluate the correlation between myocardial deformation parameters obtained by 3DSTE and functional capacity in patients with hypertrophic cardiomyopathy. Seventy-four hypertrophic cardiomyopathy adult patients were prospectively evaluated. All patients underwent a dedicated 2D and 3D echocardiographic examination and cardiopulmonary exercise testing (CPET). Values of 3D global radial (GRS), longitudinal (3DGLS) and circumferential strain (GCS) were overall reduced in our population: 99% (n = 73) of the patients had reduced GLS, 82% (n = 61) had reduced GRS and all patients had reduced GCS obtain by 3DSTE. Average peak VO\u003Cjats:sub>2\u003C\u002Fjats:sub> was 21.01 (6.08) ml\u002FKg\u002Fmin; 58% (n = 39) of the patients showed reduced exercise tolerance (predicted peak VO\u003Cjats:sub>2\u003C\u002Fjats:sub> < 80%). The average VE\u002FVCO\u003Cjats:sub>2\u003C\u002Fjats:sub> slope was 29.0 (5.3) and 16% (n = 11) of the patients had impaired ventilatory efficiency (VE\u002FVCO\u003Cjats:sub>2\u003C\u002Fjats:sub> > 34). In multivariable analysis, 3D GLS (β\u003Cjats:sub>1\u003C\u002Fjats:sub> = 0.10, 95%CI: 0.03;0.23, p = 0.014), age (β\u003Cjats:sub>1\u003C\u002Fjats:sub> = -0.15, 95%CI: -0.23; -0.05, p = 0.002) and female gender (β\u003Cjats:sub>1\u003C\u002Fjats:sub> = -5.10, 95%CI: -7.7; -2.6, p < 0.01) were independently associated with peak VO\u003Cjats:sub>2\u003C\u002Fjats:sub>. No association was found between left ventricle ejection fraction obtain and peak VO\u003Cjats:sub>2\u003C\u002Fjats:sub> (r = 0.161, p = 0.5). Impaired myocardial deformation parameters evaluated by 3DSTE were associated with worse functional capacity assessed by peak VO\u003Cjats:sub>2\u003C\u002Fjats:sub>.\u003C\u002Fjats:p>",{"EN":318},"Three-dimensional echocardiography for the evaluation of hypertrophic cardiomyopathy patients: relation to symptoms and exercise 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N Engl J Med 364(23):2187–2198. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa1103510\nLeon MB et al (2016) Transcatheter or surgical aortic-valve replacement in intermediate-risk patients. N Engl J Med 374(17):1609–1620. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa1514616\nReardon MJ et al (2017) “Surgical or transcatheter aortic-valve replacement in intermediate-risk patients,” (in eng). N Engl J Med 376(14):1321–1331. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa1700456\nMack MJ et al (2019) Transcatheter aortic-valve replacement with a balloon-expandable valve in low-risk patients. N Engl J Med 380(18):1695–1705. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa1814052\nGénéreux P et al (2013) “Paravalvular leak after transcatheter aortic valve replacement: the new achilles’ heel? A comprehensive review of the literature,” (in eng). J Am Coll Cardiol 61(11):1125–1136. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2012.08.1039\nGénéreux P, Reiss GR, Kodali SK, Williams MR, Hahn RT (2012) “Periaortic hematoma after transcatheter aortic valve replacement: description of a new complication,” (in eng). Catheter Cardiovasc Interv 79(5):766–776. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fccd.23242\nAthappan G et al (2013) “Incidence, predictors, and outcomes of aortic regurgitation after transcatheter aortic valve replacement: meta-analysis and systematic review of literature,” (in eng). J Am Coll Cardiol 61(15):1585–1595. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2013.01.047\nKenny C, Monaghan M (2015) “How to assess aortic annular size before transcatheter aortic valve implantation (TAVI): the role of echocardiography compared with other imaging modalities,” (in eng). Heart 101(9):727–736. https:\u002F\u002Fdoi.org\u002F10.1136\u002Fheartjnl-2013-304689\nKasel AM et al (2013) “Standardized imaging for aortic annular sizing: implications for transcatheter valve selection,” (in eng). JACC Cardiovasc Imaging 6(2):249–262. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jcmg.2012.12.005\nWillson AB et al (2012) “Computed tomography-based sizing recommendations for transcatheter aortic valve replacement with balloon-expandable valves: comparison with transesophageal echocardiography and rationale for implementation in a prospective trial,” (in eng). J Cardiovasc Comput Tomogr 6(6):406–414. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jcct.2012.10.002\nVaquerizo B et al (2016) “Three-dimensional echocardiography vs. computed tomography for transcatheter aortic valve replacement sizing,” (in eng). Eur Heart J Cardiovasc Imaging 17(1):15–23. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fehjci\u002Fjev238\nStella S et al (2019) “Accuracy and reproducibility of aortic annular measurements obtained from echocardiographic 3D manual and semi-automated software analyses in patients referred for transcatheter aortic valve implantation: implication for prosthesis size selection,: (in eng). Eur Heart J Cardiovasc Imaging 20(1):45–55. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fehjci\u002Fjey013\nQueirós S et al (2018) “Validation of a novel software tool for automatic aortic annular sizing in three-dimensional transesophageal echocardiographic images,” (in eng). J Am Soc Echocardiogr 31(4):515-525.e5. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.echo.2018.01.007\nPrihadi EA, van Rosendael PJ, Vollema EM, Bax JJ, Delgado V, Ajmone Marsan N (2018) “Feasibility, accuracy, and reproducibility of aortic annular and root sizing for transcatheter aortic valve replacement using novel automated three-dimensional echocardiographic software: comparison with multi-detector row computed tomography,” (in eng). J Am Soc Echocardiogr 31(4):505–514. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.echo.2017.10.003\nJilaihawi H et al (2012) “Cross-sectional computed tomographic assessment improves accuracy of aortic annular sizing for transcatheter aortic valve replacement and reduces the incidence of paravalvular aortic regurgitation,” (in eng). J Am Coll Cardiol 59(14):1275–1286. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2011.11.045\nBax J et al (2014) Open issues in transcatheter aortic valve implantation. Part 1: patient selection and treatment strategy for transcatheter aortic valve implantation. Europ Heart J. https:\u002F\u002Fdoi.org\u002F10.1093\u002Feurheartj\u002Fehu256\nD’Ancona G, Dißmann M, Heinze H, Zohlnhöfer-Momm D, Ince H, Kische S (2018) Transcatheter aortic valve replacement with the 34 mm medtronic evolut valve. Neth Heart J 26(7):401–408. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12471-018-1122-4\nKappetein AP et al (2013) Updated standardized endpoint definitions for transcatheter aortic valve implantation: the valve academic research consortium-2 consensus document∗. J Thorac Cardiovasc Surg 145(1):6–23. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jtcvs.2012.09.002\nMitchell C et al (2019) “Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the american society of echocardiography,” (in eng). J Am Soc Echocardiogr 32(1):1–64. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.echo.2018.06.004\nSteyerberg EW (2018) “Validation in prediction research: the waste by data splitting,” (in eng). J Clin Epidemiol 103:131–133. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jclinepi.2018.07.010\nRong LQ et al (2019) “Three-dimensional echocardiography for transcatheter aortic valve replacement sizing: a systematic review and meta-analysis,” (in eng). J Am Heart Assoc 8(19):e013463. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fjaha.119.013463\nKhalique OK et al (2014) “Aortic annular sizing using a novel 3-dimensional echocardiographic method: use and comparison with cardiac computed tomography,” (in eng). Circ Cardiovasc Imaging 7(1):155–163. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcircimaging.113.001153\nSchultz CJ et al (2010) “Three dimensional evaluation of the aortic annulus using multislice computer tomography: are manufacturer’s guidelines for sizing for percutaneous aortic valve replacement helpful?,” (in eng). Eur Heart J 31(7):849–856. https:\u002F\u002Fdoi.org\u002F10.1093\u002Feurheartj\u002Fehp534\nJilaihawi H et al (2013) “Aortic annular sizing for transcatheter aortic valve replacement using cross-sectional 3-dimensional transesophageal echocardiography,” (in eng). J Am Coll Cardiol 61(9):908–916. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2012.11.055\nKhalique OK et al (2017) “Impact of methodologic differences in three-dimensional echocardiographic measurements of the aortic annulus compared with computed tomographic angiography before transcatheter aortic valve replacement,” (in eng). J Am Soc Echocardiogr 30(4):414–421. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.echo.2016.10.012\nGarcía-Martín A et al (2016) “Accuracy and reproducibility of novel echocardiographic three-dimensional automated software for the assessment of the aortic root in candidates for thanscatheter aortic valve replacement,” (in eng). Eur Heart J Cardiovasc Imaging 17(7):772–778. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fehjci\u002Fjev204\nClaessen BE, Tang GHL, Kini AS, Sharma SK (2021) considerations for optimal device selection in transcatheter aortic valve replacement: a review. JAMA Cardiology 6(1):102–112. https:\u002F\u002Fdoi.org\u002F10.1001\u002Fjamacardio.2020.3682",{"EN":629},"3D-transesophageal echocardiography (3D-TEE) is an alternative to multidetector row computed tomography (MDCT) for aortic annulus (AoA) sizing in preparation for Transcatheter aortic valve implantation (TAVI). We aim to evaluate how the fully automated (auto) and semi-automated (SA) TEE methods perform compared to conventional manual TEE method and the gold standard MDCT for annulus sizing both in expert and novice operators. In this prospective cohort study, eighty-nine patients with severe aortic stenosis underwent multimodality imaging with 3D-TEE and MDCT. Annular measurements were collected by expert echocardiographers using 3D auto, SA and manual methods and compared to MDCT. A novice in the field of echocardiography retrospectively measured the AoA for all patients using the same methods. TEE measurements, independently of the method used, had good to very good agreement to MDCT. They significantly underestimated aortic annular area and circumference vs. MDCT with the auto method underestimating it the most and the manual method the least (6.5% and 1.3% respectively for area and circumference). For experts, the manual TEE method offered the least systematic bias while the SA method had narrower limits of agreement (LOA). For the novice operator, SA method provided the least bias and narrower LOA vs. MDCT. There is good agreement between novice and experts for all 3 TEE methods but better agreement with auto and SA methods as opposed to manual one. Our study supports the use of 3D-TEE as a complementary method to MDCT for aortic annular sizing. The newer auto and SA software, that requires minimal operator intervention, is an easy to use, reliable and reproducible tool for aortic annulus sizing for experienced operators, and especially less experienced ones.",{"EN":631},"Automated and semi-automated 3D echocardiographic software for aortic annulus sizing in transcatheter aortic valve implantation helps bridge the gap between expert and novice 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noninvasive estimation of right atrial pressure (RAP) by inferior vena cava (IVC) measurement during echocardiography may have significant inter-rater variability due to different levels of observers’ experience. Therefore, there is a need to develop new approaches to decrease the variability of IVC analysis and RAP estimation. This study aims to develop a fully automated artificial intelligence (AI)-based system for automated IVC analysis and RAP estimation. We presented a multi-stage AI system to identify the IVC view, select good quality images, delineate the IVC region and quantify its thickness, enabling temporal tracking of its diameter and collapsibility changes. The automated system was trained and tested on expert manual IVC and RAP reference measurements obtained from 255 patients during routine clinical workflow. The performance was evaluated using Pearson correlation and Bland-Altman analysis for IVC values, as well as macro accuracy and chi-square test for RAP values. Our results show an excellent agreement (r=0.96) between automatically computed versus manually measured IVC values, and Bland-Altman analysis showed a small bias of \u003Cjats:inline-formula>\u003Cjats:alternatives>\u003Cjats:tex-math>$$-$$\u003C\u002Fjats:tex-math>\u003Cmml:math xmlns:mml=\"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML\">\n                  \u003Cmml:mo>-\u003C\u002Fmml:mo>\n                \u003C\u002Fmml:math>\u003C\u002Fjats:alternatives>\u003C\u002Fjats:inline-formula>0.33 mm. Further, there is an excellent agreement (\u003Cjats:inline-formula>\u003Cjats:alternatives>\u003Cjats:tex-math>$$(p<0.01$$\u003C\u002Fjats:tex-math>\u003Cmml:math xmlns:mml=\"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML\">\n                  \u003Cmml:mrow>\n                    \u003Cmml:mo>(\u003C\u002Fmml:mo>\n                    \u003Cmml:mi>p\u003C\u002Fmml:mi>\n                    \u003Cmml:mo><\u003C\u002Fmml:mo>\n                    \u003Cmml:mn>0.01\u003C\u002Fmml:mn>\n                  \u003C\u002Fmml:mrow>\n                \u003C\u002Fmml:math>\u003C\u002Fjats:alternatives>\u003C\u002Fjats:inline-formula>) between automatically estimated versus manually derived RAP values with a macro accuracy of 0.85. The proposed AI-based system accurately quantified IVC diameter, collapsibility index, both are used for RAP estimation. This automated system could serve as a paradigm to perform IVC analysis in routine echocardiography and support various cardiac diagnostic applications.\u003C\u002Fjats:p>",{"EN":770},"Evaluation of an artificial intelligence-based system for echocardiographic estimation of right atrial pressure",{"VOID":772},"37682418",{"VOID":774},"10.1007\u002Fs10554-023-02941-8",[66],"https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10554-023-02941-8",[778,797,813,834,855,871],{"id":779,"sortIndex":131,"researcher":18,"roles":780,"affiliations":781,"properties":790},"40cf01f0-8d49-44d7-8e67-4bd88b8df199",[],[782],{"id":18,"sortIndex":19,"affiliation":783,"properties":18},{"id":784,"createTime":785,"updateTime":785,"relativeEntities":786,"slug":18,"properties":787,"entityType":82,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"1381f86a-94b8-404b-8c0b-1f4f367e4ec9","2023-12-14T03:39:03.721+00:00",[],{"title":788},{"VI":789},"National Library of Medicine, National Institutes of Health, Bethesda, USA",{"openalex":791,"orcid":793,"title":795},{"VOID":792},"A5073995883",{"VOID":794},"https:\u002F\u002Forcid.org\u002F0000-0002-0040-1387",{"EN":796},"Sameer 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N, Trueick R, Bhat S, Sengupta PP, Dwivedi G (2020) Current challenges and recent updates in artificial intelligence and echocardiography. Current Cardiovasc Imaging Rep 13:1–12",{"doi":908},"10.1007\u002Fs12410-020-9529-x",{"id":18,"text":910,"url":18,"identifiers":911},"Zamzmi G, Hsu LY, Li W, Sachdev V, Antani S (2020) Harnessing machine intelligence in automatic echocardiogram analysis: Current status, limitations, and future directions. IEEE Rev Biomed Eng 14:181–203",{"doi":912},"10.1109\u002FRBME.2020.2988295",{"id":18,"text":914,"url":18,"identifiers":915},"Caplan M, Durand A, Bortolotti P, Colling D, Goutay J, Duburcq T, Drumez E, Rouze A, Nseir S, Howsam M et al (2020) Measurement site of inferior vena cava diameter affects the accuracy with which fluid responsiveness can be predicted in spontaneously breathing patients: a post hoc analysis of two prospective cohorts. Ann Intensive Care 10:1–10",{"doi":916},"10.1186\u002Fs13613-020-00786-1",{"id":18,"text":918,"url":18,"identifiers":919},"Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T et al (2015) Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the american society of echocardiography and the european association of cardiovascular imaging. Eur Heart J Cardiovasc Imaging 16(3):233–271",{"doi":920},"10.1093\u002Fehjci\u002Fjev014",{"id":18,"text":922,"url":18,"identifiers":923},"Porter TR, Shillcutt SK, Adams MS, Desjardins G, Glas KE, Olson JJ, Troughton RW (2015) Guidelines for the use of echocardiography as a monitor for therapeutic intervention in adults: a report from the american society of echocardiography. J Am Soc Echocardiogr 28(1):40–56",{"doi":924},"10.1016\u002Fj.echo.2014.09.009",{"id":18,"text":926,"url":18,"identifiers":927},"Magnino C, Omede P, Avenatti E, Presutti D, Iannaccone A, Chiarlo M, Moretti C, Gaita F, Veglio F, Milan A et al (2017) Inaccuracy of right atrial pressure estimates through inferior vena cava indices. Am J Cardiol 120(9):1667–1673",{"doi":928},"10.1016\u002Fj.amjcard.2017.07.069",{"id":18,"text":930,"url":18,"identifiers":931},"Istrail L, Kiernan J, Stepanova M (2023) A novel method for estimating right atrial pressure with point-of-care ultrasound. J Am Soc Echocardiogr 36(3):278–283",{"doi":932},"10.1016\u002Fj.echo.2022.12.008",{"id":18,"text":934,"url":18,"identifiers":935},"Albani S, Pinamonti B, Giovinazzo T, de Scordilli M, Fabris E, Stolfo D, Perkan A, Gregorio C, Barbati G, Geri P et al (2020) Accuracy of right atrial pressure estimation using a multi-parameter approach derived from inferior vena cava semi-automated edge-tracking echocardiography: A pilot study in patients with cardiovascular disorders. Int J Cardiovasc Imaging 36:1213–1225",{"doi":936},"10.1007\u002Fs10554-020-01814-8",{"id":18,"text":938,"url":18,"identifiers":939},"Cannesson M, Tanabe M, Suffoletto MS, McNamara DM, Madan S, Lacomis JM, Gorcsan J (2007) A novel two-dimensional echocardiographic image analysis system using artificial intelligence-learned pattern recognition for rapid automated ejection fraction. J Am Coll Cardiol 49(2):217–226",{"doi":940},"10.1016\u002Fj.jacc.2006.08.045",{"id":18,"text":942,"url":18,"identifiers":943},"Yao X, Rushlow DR, Inselman JW, McCoy RG, Thacher TD, Behnken EM, Bernard ME, Rosas SL, Akfaly A, Misra A et al (2021) Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med 27(5):815–819",{"doi":944},"10.1038\u002Fs41591-021-01335-4",{"id":18,"text":946,"url":18,"identifiers":947},"Jian Z, Wang X, Zhang J, Wang X, Deng Y (2020) Diagnosis of left ventricular hypertrophy using convolutional neural network. BMC Med Inform Decis Mak 20:1–12",{"doi":948},"10.1186\u002Fs12911-020-01255-2",{"id":18,"text":950,"url":18,"identifiers":951},"Howard JP, Stowell CC, Cole GD, Ananthan K, Demetrescu CD, Pearce K, Rajani R, Sehmi J, Vimalesvaran K, Kanaganayagam GS et al (2021) Automated left ventricular dimension assessment using artificial intelligence developed and validated by a uk-wide collaborative. Circ Cardiovasc Imaging 14(5):e011951",{"doi":952},"10.1161\u002FCIRCIMAGING.120.011951",{"id":18,"text":954,"url":18,"identifiers":955},"Salte IM, Østvik A, Smistad E, Melichova D, Nguyen TM, Karlsen S, Brunvand H, Haugaa KH, Edvardsen T, Lovstakken L et al (2021) Artificial intelligence for automatic measurement of left ventricular strain in echocardiography. Cardiovasc Imaging 14(10):1918–1928",{},{"id":18,"text":957,"url":18,"identifiers":958},"Akkus Z, Aly YH, Attia IZ, Lopez-Jimenez F, Arruda-Olson AM, Pellikka PA, Pislaru SV, Kane GC, Friedman PA, Oh JK (2021) Artificial intelligence (ai)-empowered echocardiography interpretation: a state-of-the-art review. J Clin Med 10(7):1391",{"doi":959},"10.3390\u002Fjcm10071391",{"id":18,"text":961,"url":18,"identifiers":962},"Blaivas M, Adhikari S, Savitsky EA, Blaivas LN, Liu YT (2020) Artificial intelligence versus expert: a comparison of rapid visual inferior vena cava collapsibility assessment between pocus experts and a deep learning algorithm. J Am College Emergency Phys Open 1(5):857–864",{"doi":963},"10.1002\u002Femp2.12206",{"id":18,"text":965,"url":18,"identifiers":966},"Blaivas M, Blaivas L, Philips G, Merchant R, Levy M, Abbasi A, Eickhoff C, Shapiro N, Corl K (2021) Development of a deep learning network to classify inferior vena cava collapse to predict fluid responsiveness. J Ultrasound Med 40(8):1495–1504",{"doi":967},"10.1002\u002Fjum.15527",{"id":18,"text":969,"url":18,"identifiers":970},"Mesin L, Policastro P, Albani S, Petersen C, Sciarrone P, Taddei C, Giannoni A (2022) Non-invasive estimation of right atrial pressure using a semi-automated echocardiographic tool for inferior vena cava edge-tracking. J Clin Med 11(12):3257",{"doi":971},"10.3390\u002Fjcm11123257",{"id":18,"text":973,"url":18,"identifiers":974},"Albani S, Mesin L, Roatta S, De Luca A, Giannoni A, Stolfo D, Biava L, Bonino C, Contu L, Pelloni E et al (2022) Inferior vena cava edge tracking echocardiography: A promising tool with applications in multiple clinical settings. Diagnostics 12(2):427",{"doi":975},"10.3390\u002Fdiagnostics12020427",{"id":18,"text":977,"url":18,"identifiers":978},"Sanfilippo F, La Via L, Dezio V, Santonocito C, Amelio P, Genoese G, Astuto M, Noto A (2023) Assessment of the inferior vena cava collapsibility from subcostal and trans-hepatic imaging using both m-mode or artificial intelligence: a prospective study on healthy volunteers. Intensive Care Med Exp 11(1):1–12",{"doi":979},"10.1186\u002Fs40635-022-00487-y",{"id":18,"text":981,"url":18,"identifiers":982},"Bendale A (2016) T.E. Boult, In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1563–1572",{},{"id":18,"text":984,"url":18,"identifiers":985},"Zamzmi G, Oguguo T, Rajaraman S, Antani S (2022) Open-world active learning for echocardiography view classification. Med Imaging 2022: Computer-Aided Diagn 12033:124–134",{},{"id":18,"text":987,"url":18,"identifiers":988},"Zamzmi G, Rajaraman S, Sachdev V, Antani S (2021) Trilateral attention network for real-time cardiac region segmentation. IEEE Access 9:118205–118214",{"doi":989},"10.1109\u002FACCESS.2021.3107303",{"id":18,"text":991,"url":18,"identifiers":992},"Reddy PR, Amarnadh V, Bhaskar M (2012) Evaluation of stopping criterion in contour tracing algorithms. Int J Comput Sci Inf Technol 3(3):3888–3894",{},{"id":18,"text":994,"url":18,"identifiers":995},"Schafer RW (2011) What is a savitzky-golay filter?[lecture notes]. IEEE Signal Process Mag 28(4):111–117",{"doi":996},"10.1109\u002FMSP.2011.941097",{"id":18,"text":998,"url":18,"identifiers":999},"Benesty J, Chen J, Huang Y, Cohen I Pearson correlation coefficientnoise reduction in speech processing. 2. 1st",{"doi":1000},"10.1007\u002F978-3-642-00296-0_5",{"id":18,"text":1002,"url":18,"identifiers":1003},"Giavarina D (2015) Understanding bland altman analysis. Biochemia Medica 25(2):141–151",{"doi":1004},"10.11613\u002FBM.2015.015",{"id":18,"text":1006,"url":18,"identifiers":1007},"Madani A, Arnaout R, Mofrad M, Arnaout R (2018) Fast and accurate view classification of echocardiograms using deep learning. NPJ Digital Med 1(1):6",{"doi":1008},"10.1038\u002Fs41746-017-0013-1",{"id":18,"text":1010,"url":18,"identifiers":1011},"Labs RB, Zolgharni M, Loo JP (2021) In Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021, Proceedings 25 Springer, pp. 488–502",{"doi":1012},"10.1007\u002F978-3-030-80432-9_36",{"id":18,"text":1014,"url":18,"identifiers":1015},"Asgari Taghanaki S, Abhishek K, Cohen JP, Cohen-Adad J, Hamarneh G (2021) Deep semantic segmentation of natural and medical images: a review. Artif Intell Rev 54:137–178",{"doi":1016},"10.1007\u002Fs10462-020-09854-1",{"id":18,"text":1018,"url":18,"identifiers":1019},"Knackstedt C, Bekkers SC, Schummers G, Schreckenberg M, Muraru D, Badano LP, Franke A, Bavishi C, Omar AMS, Sengupta PP (2015) Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: the fast-efs multicenter study. J Am College Cardiol 66(13):1456–1466",{"doi":1020},"10.1016\u002Fj.jacc.2015.07.052",{"id":18,"text":1022,"url":18,"identifiers":1023},"Nolan MT, Thavendiranathan P (2019) Automated quantification in echocardiography. Cardiovasc Imaging 12(6):1073–1092",{},{"id":18,"text":1025,"url":18,"identifiers":1026},"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4510–4520",{},{"id":18,"text":1028,"url":18,"identifiers":1029},"Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine learning for medical imaging. Radiographics 37(2):505–515",{"doi":1030},"10.1148\u002Frg.2017160130",{"id":18,"text":1032,"url":18,"identifiers":1033},"Brink H, Richards J, Fetherolf M (2016) Real-world machine learning. Simon and Schuster",{},{"id":18,"text":1035,"url":18,"identifiers":1036},"Shu L, Xu H, Liu B (2017) Doc: Deep open classification of text documents. arXiv preprint arXiv:1709.08716",{"doi":1037},"10.18653\u002Fv1\u002FD17-1314",{"id":18,"text":1039,"url":18,"identifiers":1040},"Shu L, Xu H, Liu B (2018) Unseen class discovery in open-world classification. arXiv preprint arXiv:1801.05609",{},{"id":18,"text":1042,"url":18,"identifiers":1043},"Jaderberg M, Simonyan K, Zisserman A, et al (2015) Spatial transformer networks. Advances in neural information processing systems 28",{},{"id":1045,"createTime":1046,"updateTime":1047,"relativeEntities":1048,"slug":1049,"properties":1050,"entityType":62,"verifyStatus":63,"verifyTime":1059,"verifyNote":64,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1060,"fullTextUrl":18,"authors":1061,"publicationType":213,"publisherRelationship":1233,"citationCount":18,"citationInfo":18,"publishDate":1253,"publishYear":1254,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":304},"2fbbf869-d797-4fdf-aa5e-4b8d1b17872d","2024-02-14T10:52:40.147+00:00","2025-02-08T23:28:39.965+00:00",[],"Quantitative-flow-ratio-to-predict-long-term-coronary-artery-bypass-graft-patency-in-patients-with-left-main-coronary-artery-disease",{"references":1051,"abstract":1053,"title":1055,"doi":1057},{"VOID":1052},"Fournier S, Toth GG, De Bruyne B, Johnson NP, Ciccarelli G, Xaplanteris P et al (2018) Six-year follow-up of fractional flow reserve-guided versus angiography-guided coronary artery bypass graft surgery. Circ Cardiovasc Interv 11(6):e006368. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcircinterventions.117.006368\nGlineur D, Grau JB, Etienne PY, Benedetto U, Fortier JH, Papadatos S et al (2019) Impact of preoperative fractional flow reserve on arterial bypass graft anastomotic function: the IMPAG trial. Eur Heart J 40(29):2421–2428. https:\u002F\u002Fdoi.org\u002F10.1093\u002Feurheartj\u002Fehz329\nWestra J, Tu S (2020) Overview of quantitative flow ratio and optical flow ratio in the assessment of intermediate coronary lesions. US Cardiol Rev 14:e09\nZhang R, Song C, Guan C, Liu Q, Wang C, Xie L et al (2020) Prognostic value of quantitative flow ratio based functional SYNTAX score in patients with left main or multivessel coronary artery disease. Circ Cardiovasc Interv 13(10):e009155. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcircinterventions.120.009155\nMehta OH, Hay M, Lim RY, Ihdayhid AR, Michail M, Zhang JM et al (2020) Comparison of diagnostic performance between quantitative flow ratio, non-hyperemic pressure indices and fractional flow reserve. Cardiovasc Diagn Therapy 10(3):442–452\nNashef SA, Roques F, Sharples LD, Nilsson J, Smith C, Goldstone AR, et al. (2012) EuroSCORE II. Eur J Cardio-Thorac Surg 41(4):734–744; discussion 44–45. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fejcts\u002Fezs043\nTonino PA, De Bruyne B, Pijls NH, Siebert U, Ikeno F, van’t Veer M et al (2009) Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med 360(3):213–224. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa0807611\nDe Bruyne B, Pijls NH, Kalesan B, Barbato E, Tonino PA, Piroth Z et al (2012) Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease. N Engl J Med 367(11):991–1001. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa1205361\nXaplanteris P, Fournier S, Pijls NHJ, Fearon WF, Barbato E, Tonino PAL et al (2018) Five-year outcomes with PCI guided by fractional flow reserve. N Engl J Med 379(3):250–259. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa1803538\nBotman CJ, Schonberger J, Koolen S, Penn O, Botman H, Dib N et al (2007) Does stenosis severity of native vessels influence bypass graft patency? A prospective fractional flow reserve-guided study. Ann Thorac Surg 83(6):2093–2097. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.athoracsur.2007.01.027\nThuesen AL, Riber LP, Veien KT, Christiansen EH, Jensen SE, Modrau I et al (2018) Fractional flow reserve versus angiographically-guided coronary artery bypass grafting. J Am Coll Cardiol 72(22):2732–2743. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2018.09.043\nToth GG, De Bruyne B, Kala P, Ribichini FL, Casselman F, Ramos R et al (2019) Graft patency after FFR-guided versus angiography-guided coronary artery bypass grafting: the GRAFFITI trial. EuroIntervention 15(11):e999–e1005. https:\u002F\u002Fdoi.org\u002F10.4244\u002Feij-d-19-00463\nToth GG, Collet C, Langhoff Thuesen A, Mizukami T, Casselman F, Riber LP et al (2021) Influence of fractional flow reserve on grafts patency: systematic review and patient-level meta-analysis. Catheter Cardiovasc Interv. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fccd.29864\nThuesen AL, Riber LP, Veien KT, Christiansen EH, Jensen SE, Modrau I et al (2021) Health-related quality of life and angina in fractional flow reserve-versus angiography-guided coronary artery bypass grafting: FARGO Trial (fractional flow reserve versus angiography randomization for graft optimization). Circ Cardiovasc Qual Outcomes 14(6):e007302. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcircoutcomes.120.007302\nToth G, De Bruyne B, Casselman F, De Vroey F, Pyxaras S, Di Serafino L et al (2013) Fractional flow reserve-guided versus angiography-guided coronary artery bypass graft surgery. Circulation 128(13):1405–1411. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcirculationaha.113.002740\nYong AS, Daniels D, De Bruyne B, Kim HS, Ikeno F, Lyons J et al (2013) Fractional flow reserve assessment of left main stenosis in the presence of downstream coronary stenoses. Circ Cardiovasc Interv 6(2):161–165. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcircinterventions.112.000104\nHamilos M, Muller O, Cuisset T, Ntalianis A, Chlouverakis G, Sarno G et al (2009) Long-term clinical outcome after fractional flow reserve-guided treatment in patients with angiographically equivocal left main coronary artery stenosis. Circulation 120(15):1505–1512. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcirculationaha.109.850073\nFassa AA, Wagatsuma K, Higano ST, Mathew V, Barsness GW, Lennon RJ et al (2005) Intravascular ultrasound-guided treatment for angiographically indeterminate left main coronary artery disease: a long-term follow-up study. J Am Coll Cardiol 45(2):204–211. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2004.09.066\nde la Torre Hernandez JM, Hernández Hernandez F, Alfonso F, Rumoroso JR, Lopez-Palop R, Sadaba M et al (2011) Prospective application of pre-defined intravascular ultrasound criteria for assessment of intermediate left main coronary artery lesions results from the multicenter LITRO study. J Am Coll Cardiol 58(4):351–358. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2011.02.064\nPark SJ, Ahn JM, Kang SJ, Yoon SH, Koo BK, Lee JY et al (2014) Intravascular ultrasound-derived minimal lumen area criteria for functionally significant left main coronary artery stenosis. JACC Cardiovasc Interv 7(8):868–874. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jcin.2014.02.015\nThuijs D, Kappetein AP, Serruys PW, Mohr FW, Morice MC, Mack MJ et al (2019) Percutaneous coronary intervention versus coronary artery bypass grafting in patients with three-vessel or left main coronary artery disease: 10-year follow-up of the multicentre randomised controlled SYNTAX trial. Lancet (Lond, Engl) 394(10206):1325–1334. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0140-6736(19)31997-x\nEscaned J, Collet C, Ryan N, De Maria GL, Walsh S, Sabate M et al (2017) Clinical outcomes of state-of-the-art percutaneous coronary revascularization in patients with de novo three vessel disease: 1-year results of the SYNTAX II study. Eur Heart J 38(42):3124–3134. https:\u002F\u002Fdoi.org\u002F10.1093\u002Feurheartj\u002Fehx512\nAsano T, Katagiri Y, Chang CC, Kogame N, Chichareon P, Takahashi K et al (2019) Angiography-derived fractional flow reserve in the SYNTAX II trial: feasibility, diagnostic performance of quantitative flow ratio, and clinical prognostic value of functional SYNTAX score derived from quantitative flow ratio in patients with 3-vessel disease. JACC Cardiovasc Interv 12(3):259–270. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jcin.2018.09.023\nFearon WF, Zimmermann FM, De Bruyne B, Piroth Z, van Straten AHM, Szekely L et al (2021) Fractional flow reserve-guided PCI as compared with coronary bypass surgery. N Engl J Med. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa2112299\nGaudino M, Benedetto U, Fremes S, Biondi-Zoccai G, Sedrakyan A, Puskas JD et al (2018) Radial-artery or saphenous-vein grafts in coronary-artery bypass surgery. N Engl J Med 378(22):2069–2077. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa1716026\nWestra J, Tu S, Campo G, Qiao S, Matsuo H, Qu X et al (2019) Diagnostic performance of quantitative flow ratio in prospectively enrolled patients: an individual patient-data meta-analysis. Catheteriz Cardiovasc Intervent 94(5):693–701. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fccd.28283\nMasdjedi K, Tanaka N, Van Belle E, Porouchani S, Linke A, Woitek FJ et al (2021) Vessel fractional flow reserve (vFFR) for the assessment of stenosis severity: the FAST II study. EuroIntervention. https:\u002F\u002Fdoi.org\u002F10.4244\u002Feij-d-21-00471\nGigante C, Mizukami T, Sonck J, Nagumo S, Tanzilli A, Bartunek J et al (2020) Graft patency and progression of coronary artery disease after CABG assessed by angiography-derived fractional flow reserve. Int J Cardiol 316:19–25. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ijcard.2020.04.083\nXu B, Tu S, Song L, Jin Z, Yu B, Fu G et al (2021) Angiographic quantitative flow ratio-guided coronary intervention (FAVOR III China): a multicentre, randomised, sham-controlled trial. Lancet (Lond, Engl) 398(10317):2149–2159. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0140-6736(21)02248-0\nChan M, Ridley L, Dunn DJ, Tian DH, Liou K, Ozdirik J et al (2016) A systematic review and meta-analysis of multidetector computed tomography in the assessment of coronary artery bypass grafts. Int J Cardiol 221:898–905. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ijcard.2016.06.264\nGaudino M, Antoniades C, Benedetto U, Deb S, Di Franco A, Di Giammarco G et al (2017) Mechanisms, consequences, and prevention of coronary graft failure. Circulation 136(18):1749–1764. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcirculationaha.117.027597",{"EN":1054},"Fractional flow reserve (FFR) has been demonstrated in some studies to predict long-term coronary artery bypass graft (CABG) patency. Quantitative flow ratio (QFR) is an emerging technology which may predict FFR. In this study, we hypothesised that QFR would predict long-term CABG patency and that QFR would offer superior diagnostic performance to quantitative coronary angiography (QCA) and intravascular ultrasound (IVUS). A prospective study was performed on patients with left main coronary artery disease who were undergoing CABG. QFR, QCA and IVUS assessment was performed. Follow-up computed tomography coronary angiography and invasive coronary angiography was undertaken to assess graft patency. A total of 22 patients, comprising of 65 vessels were included in the analysis. At a median follow-up of 3.6 years post CABG (interquartile range, 2.3 to 4.8 years), 12 grafts (18.4%) were occluded. QFR was not statistically significantly higher in occluded grafts (0.81 ± 0.19 vs. 0.69 ± 0.21; P = 0.08). QFR demonstrated a discriminatory power to predict graft occlusion (area under the receiver operating characteristic curve, 0.70; 95% confidence interval [CI], 0.52 to 0.88; P = 0.03). At long-term follow-up, the risk of graft occlusion was higher in vessels with a QFR > 0.80 (58.6% vs. 17.0%; hazard ratio, 3.89; 95% CI, 1.05 to 14.42; P = 0.03 by log-rank test). QCA (minimum lumen diameter, lesion length, diameter stenosis) and IVUS (minimum lumen area, minimum lumen diameter, diameter stenosis) parameters were not predictive of long-term graft patency. QFR may predict long-term graft patency in patients undergoing CABG.",{"EN":1056},"Quantitative flow ratio to predict long-term coronary artery bypass graft patency in patients with left main coronary artery disease",{"VOID":1058},"10.1007\u002Fs10554-022-02699-5","2025-02-08T23:28:39.964+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10554-022-02699-5",[1062,1097,1121,1146,1158,1170,1182,1197,1209,1221],{"id":1063,"sortIndex":19,"researcher":18,"roles":1064,"affiliations":1065,"properties":1094},"8e7c1c89-5b1c-4acb-8bda-dd902245f48a",[639],[1066,1074,1084],{"id":18,"sortIndex":19,"affiliation":1067,"properties":18},{"id":1068,"createTime":1069,"updateTime":1069,"relativeEntities":1070,"slug":18,"properties":1071,"entityType":82,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"8d3a5826-ceea-45ca-8c44-260e0aead1a9","2024-01-05T02:41:54.125+00:00",[],{"title":1072},{"VI":1073},"MonashHeart, Monash Health and Monash Cardiovascular Research Centre, Monash University, Melbourne, Australia",{"id":1075,"sortIndex":71,"affiliation":1076,"properties":1083},"d379d5ba-33d7-46d5-9b91-0ea76a97eb3b",{"id":1077,"createTime":1078,"updateTime":1078,"relativeEntities":1079,"slug":18,"properties":1080,"entityType":82,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"75580ca8-dc7a-483f-b1df-76293f2e03a3","2023-12-06T23:48:52.744+00:00",[],{"title":1081},{"VI":1082},"Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, USA",{},{"id":1085,"sortIndex":198,"affiliation":1086,"properties":1093},"224f0786-7a2f-428d-a853-02a436832f68",{"id":1087,"createTime":1088,"updateTime":1088,"relativeEntities":1089,"slug":18,"properties":1090,"entityType":82,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"9926f171-82b8-4b58-a6ec-e1ebf4a0f8f5","2024-02-14T10:52:40.177+00:00",[],{"title":1091},{"VI":1092},"MonashHeart, Clayton, Australia",{},{"title":1095},{"VI":1096},"Cameron Dowling",{"id":1098,"sortIndex":71,"researcher":18,"roles":1099,"affiliations":1100,"properties":1118},"7b0e87bd-f40f-49ae-ab26-683bac90e7a8",[639],[1101,1113],{"id":1102,"sortIndex":71,"affiliation":1103,"properties":1112},"45619494-ba19-4448-8791-e5991d45eb3c",{"id":1104,"createTime":1105,"updateTime":1106,"relativeEntities":1107,"slug":1108,"properties":1109,"entityType":82,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"0a502994-cae5-4f1a-9d88-c1dee203c8ab","2024-04-11T05:14:36.258+00:00","2025-06-11T23:13:38.503+00:00",[],"Duke-Clinical-Research-Institute-Duke-University-Durham-USA",{"title":1110},{"EN":1111},"Duke Clinical Research Institute, Duke University, Durham, USA",{},{"id":18,"sortIndex":19,"affiliation":1114,"properties":18},{"id":1068,"createTime":1069,"updateTime":1069,"relativeEntities":1115,"slug":18,"properties":1116,"entityType":82,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1117},{"VI":1073},{"title":1119},{"VI":1120},"Adam J. 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This retrospective, IRB-approved study included 70 patients (30 women, 40 men, mean age 78 ± 9 years) who underwent ECG-gated cardiac non-contrast CT with PCD-CT (gantry rotation time 0.25 s) prior to transcatheter aortic valve replacement. Each scan was reconstructed at a temporal resolution of 66 ms using the dual-source information and at 125 ms using the single-source information. Average heart rate and heart rate variability were calculated from the recorded ECG. CAC, AVC, and MAC were quantified according to the Agatston method on images with both temporal resolutions. Two readers assessed blur artifacts using a 4-point visual grading scale. The influence of average heart rate and heart rate variability on calcium quantification and blur artifacts of the respective structures were analyzed by linear regression analysis. Mean heart rate and heart rate variability during data acquisition were 76 ± 17 beats per minute (bpm) and 4 ± 6 bpm, respectively. CAC scores were smaller on 66 ms (median, 511; interquartile range, 220–978) than on 125 ms reconstructions (538; 203–1050, \u003Cjats:italic>p\u003C\u002Fjats:italic> < 0.001). Median AVC scores [2809 (2009–3952) versus 3177 (2158–4273)] and median MAC scores [226 (0-1284) versus 251 (0-1574)] were also significantly smaller on 66ms than on 125ms reconstructions (\u003Cjats:italic>p\u003C\u002Fjats:italic> < 0.001). Reclassification of CAC and AVC risk categories occurred in 4% and 11% of cases, respectively, whereby the risk category was always overestimated on 125ms reconstructions. Image blur artifacts were significantly less on 66ms as opposed to 125 ms reconstructions (\u003Cjats:italic>p\u003C\u002Fjats:italic> < 0.001). Intra-individual analyses indicate that temporal resolution significantly impacts on calcium scoring with cardiac CT, with CAC, MAC, and AVC being overestimated at lower temporal resolution because of increased motion artifacts eventually leading to an overestimation of patient risk.\u003C\u002Fjats:p>",{"EN":1268},"Effect of temporal resolution on calcium scoring: insights from photon-counting detector 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Radiology 301:105–112. https:\u002F\u002Fdoi.org\u002F10.1148\u002Fradiol.2021204623",{"doi":1426},"10.1148\u002Fradiol.2021204623",{"id":18,"text":1428,"url":18,"identifiers":1429},"Blaha MJ, Whelton SP, Al Rifai M et al (2021) Comparing risk scores in the prediction of Coronary and Cardiovascular deaths. JACC: Cardiovasc Imaging 14:411–421. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jcmg.2019.12.010",{"doi":1430},"10.1016\u002Fj.jcmg.2019.12.010",{"id":18,"text":1432,"url":18,"identifiers":1433},"Elias-Smale SE, Proença RV, Koller MT et al (2010) Coronary calcium score improves classification of Coronary Heart Disease Risk in the Elderly. J Am Coll Cardiol 56:1407–1414. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2010.06.029",{"doi":1434},"10.1016\u002Fj.jacc.2010.06.029",{"id":18,"text":1436,"url":18,"identifiers":1437},"Christensen JL, Tan S, Chung HE et al (2020) Aortic valve calcification predicts all-cause mortality independent of coronary calcification and severe stenosis. Atherosclerosis 307:16–20. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atherosclerosis.2020.06.019",{"doi":1438},"10.1016\u002Fj.atherosclerosis.2020.06.019",{"id":18,"text":1440,"url":18,"identifiers":1441},"Pawade T, Clavel M-A, Tribouilloy C et al (2018) Computed tomography aortic valve calcium scoring in patients with aortic stenosis. Circ: Cardiovasc Imaging 11:e007146. https:\u002F\u002Fdoi.org\u002F10.1161\u002FCIRCIMAGING.117.007146",{"doi":1442},"10.1161\u002FCIRCIMAGING.117.007146",{"id":18,"text":1444,"url":18,"identifiers":1445},"Brodov Y, Konen E, Di Segni M et al (2019) Mitral Annulus Calcium score: an independent predictor of New Conduction System abnormalities in patients after transcatheter aortic valve implantation. Circ: Cardiovasc Imaging 12:e007508. https:\u002F\u002Fdoi.org\u002F10.1161\u002FCIRCIMAGING.117.007508",{"doi":1446},"10.1161\u002FCIRCIMAGING.117.007508",{"id":18,"text":1448,"url":18,"identifiers":1449},"Okuno T, Brugger N, Asami M et al (2021) Clinical impact of mitral calcium volume in patients undergoing transcatheter aortic valve implantation. J Cardiovasc Comput Tomogr 15:356–365. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jcct.2020.10.003",{"doi":1450},"10.1016\u002Fj.jcct.2020.10.003",{"id":18,"text":1452,"url":18,"identifiers":1453},"Willemink MJ, Vliegenthart R, Takx RAP et al (2014) Coronary artery calcification scoring with state-of-the-art CT scanners from different vendors has substantial effect on risk classification. Radiology 273:695–702. https:\u002F\u002Fdoi.org\u002F10.1148\u002Fradiol.14140066",{"doi":1454},"10.1148\u002Fradiol.14140066",{"id":18,"text":1456,"url":18,"identifiers":1457},"Van Der Werf NR, Dobrolinska MM, Greuter MJW et al (2023) Vendor Independent Coronary Calcium Scoring Improves Individual Risk Assessment. JACC: Cardiovascular Imaging S1936878X23002243. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jcmg.2023.05.005",{"doi":1458},"10.1016\u002Fj.jcmg.2023.05.005",{"id":18,"text":1460,"url":18,"identifiers":1461},"Van Praagh GD, Wang J, Van Der Werf NR et al (2022) Coronary artery calcium scoring: toward a New Standard. Invest Radiol 57:13–22. https:\u002F\u002Fdoi.org\u002F10.1097\u002FRLI.0000000000000808",{"doi":1462},"10.1097\u002FRLI.0000000000000808",{"id":18,"text":1464,"url":18,"identifiers":1465},"Messerli M, Rengier F, Desbiolles L et al (2016) Impact of Advanced Modeled Iterative Reconstruction on Coronary Artery Calcium quantification. Acad Radiol 23:1506–1512. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.acra.2016.08.008",{"doi":1466},"10.1016\u002Fj.acra.2016.08.008",{"id":18,"text":1468,"url":18,"identifiers":1469},"Rossi A, Gennari AG, Etter D et al (2022) Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification. Eur Radiol 33:3832–3838. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00330-022-09287-0",{"doi":1470},"10.1007\u002Fs00330-022-09287-0",{"id":18,"text":1472,"url":18,"identifiers":1473},"Hinzpeter R, Weber L, Euler A et al (2020) Aortic valve calcification scoring with computed tomography: impact of iterative reconstruction techniques. Int J Cardiovasc Imaging 36:1575–1581. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10554-020-01862-0",{"doi":1474},"10.1007\u002Fs10554-020-01862-0",{"id":18,"text":1476,"url":18,"identifiers":1477},"McCollough CH, Ulzheimer S, Halliburton SS et al (2007) Coronary artery calcium: a multi-institutional, Multimanufacturer International Standard for Quantification at Cardiac CT. Radiology 243:527–538. https:\u002F\u002Fdoi.org\u002F10.1148\u002Fradiol.2432050808",{"doi":1478},"10.1148\u002Fradiol.2432050808",{"id":18,"text":1480,"url":18,"identifiers":1481},"Van Der Werf NR, Booij R, Greuter MJW et al (2022) Reproducibility of coronary artery calcium quantification on dual-source CT and dual-source photon-counting CT: a dynamic phantom study. Int J Cardiovasc Imaging 38:1613–1619. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10554-022-02540-z",{"doi":1482},"10.1007\u002Fs10554-022-02540-z",{"id":18,"text":1484,"url":18,"identifiers":1485},"Groen JM, Greuter MJ, Schmidt B et al (2007) The influence of heart rate, slice thickness, and calcification density on calcium scores using 64-Slice Multidetector computed tomography: a systematic Phantom Study. Invest Radiol 42:848–855. https:\u002F\u002Fdoi.org\u002F10.1097\u002FRLI.0b013e318154c549",{"doi":1486},"10.1097\u002FRLI.0b013e318154c549",{"id":18,"text":1488,"url":18,"identifiers":1489},"Groen JM, Greuter MJW, Vliegenthart R et al (2008) Calcium scoring using 64-slice MDCT, dual source CT and EBT: a comparative phantom study. Int J Cardiovasc Imaging 24:547–556. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10554-007-9282-0",{"doi":1490},"10.1007\u002Fs10554-007-9282-0",{"id":18,"text":1492,"url":18,"identifiers":1493},"Husmann L, Leschka S, Desbiolles L et al (2007) Coronary artery motion and Cardiac Phases: dependency on Heart Rate—implications for CT Image Reconstruction. Radiology 245:567–576. https:\u002F\u002Fdoi.org\u002F10.1148\u002Fradiol.2451061791",{"doi":1494},"10.1148\u002Fradiol.2451061791",{"id":18,"text":1496,"url":18,"identifiers":1497},"Van Der Werf NR, Willemink MJ, Willems TP et al (2018) Influence of heart rate on coronary calcium scores: a multi-manufacturer phantom study. Int J Cardiovasc Imaging 34:959–966. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10554-017-1293-x",{"doi":1498},"10.1007\u002Fs10554-017-1293-x",{"id":18,"text":1500,"url":18,"identifiers":1501},"Mergen V, Sartoretti T, Cundari G et al (2023) The importance of temporal resolution for Ultra-high-resolution Coronary Angiography: evidence from photon-counting detector CT. https:\u002F\u002Fdoi.org\u002F10.1097\u002FRLI.0000000000000987. Invest Radiol Publish Ahead of Print",{"doi":1502},"10.1097\u002FRLI.0000000000000987",{"id":18,"text":1504,"url":18,"identifiers":1505},"Rajendran K, Petersilka M, Henning A et al (2021) First clinical photon-counting detector CT system: technical evaluation. https:\u002F\u002Fdoi.org\u002F10.1148\u002Fradiol.212579. Radiology 212579",{"doi":1506},"10.1148\u002Fradiol.212579",{"id":18,"text":1508,"url":18,"identifiers":1509},"Greffier J, Villani N, Defez D et al (2023) Spectral CT imaging: technical principles of dual-energy CT and multi-energy photon-counting CT. Diagn Interv Imaging 104:167–177. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.diii.2022.11.003",{"doi":1510},"10.1016\u002Fj.diii.2022.11.003",{"id":18,"text":1512,"url":18,"identifiers":1513},"Van Der Werf NR, Greuter MJW, Booij R et al (2022) Coronary calcium scores on dual-source photon-counting computed tomography: an adapted Agatston methodology aimed at radiation dose reduction. Eur Radiol 32:5201–5209. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00330-022-08642-5",{"doi":1514},"10.1007\u002Fs00330-022-08642-5",{"id":18,"text":1516,"url":18,"identifiers":1517},"Eberhard M, Mergen V, Higashigaito K et al (2021) Coronary calcium scoring with First Generation Dual-Source Photon-counting CT—First evidence from Phantom and In-Vivo scans. Diagnostics 11:1708. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fdiagnostics11091708",{"doi":1518},"10.3390\u002Fdiagnostics11091708",{"id":18,"text":1520,"url":18,"identifiers":1521},"Hecht HS, Blaha MJ, Kazerooni EA et al (2018) An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT). J Cardiovasc Comput Tomogr 12:185–191. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jcct.2018.03.008. CAC-DRS: Coronary Artery Calcium Data and Reporting System",{"doi":1522},"10.1016\u002Fj.jcct.2018.03.008",{"id":18,"text":1524,"url":18,"identifiers":1525},"Baumgartner H, Falk V, Bax JJ et al (2017) 2017 ESC\u002FEACTS guidelines for the management of valvular heart disease. Eur Heart J 38:2739–2791. https:\u002F\u002Fdoi.org\u002F10.1093\u002Feurheartj\u002Fehx391",{"doi":1526},"10.1093\u002Feurheartj\u002Fehx391",{"id":18,"text":1528,"url":18,"identifiers":1529},"Gaine SP, Blumenthal RS, Sharma G (2023) Coronary artery calcium score as a graded decision Tool. JACC: Adv 2:100664. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacadv.2023.100664",{"doi":1530},"10.1016\u002Fj.jacadv.2023.100664",{"id":18,"text":1532,"url":18,"identifiers":1533},"Dobrolinska MM, Van Praagh GD, Oostveen LJ et al (2022) Systematic assessment of coronary calcium detectability and quantification on four generations of CT reconstruction techniques: a patient and phantom study. Int J Cardiovasc Imaging 39:221–231. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10554-022-02703-y",{"doi":1534},"10.1007\u002Fs10554-022-02703-y",{"id":1536,"createTime":1537,"updateTime":1538,"relativeEntities":1539,"slug":1540,"properties":1541,"entityType":62,"verifyStatus":63,"verifyTime":1538,"verifyNote":64,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1550,"fullTextUrl":18,"authors":1551,"publicationType":213,"publisherRelationship":1664,"citationCount":18,"citationInfo":18,"publishDate":1683,"publishYear":756,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":304},"27bf4c7d-dfb5-4d71-8673-b2dc20b66af1","2024-01-05T21:35:56.137+00:00","2024-12-10T23:12:22.757+00:00",[],"Associations-of-cardiovascular-and-diabetes-related-risk-factors-with-myocardial-perfusion-reserve-assessed-by-201Tl-99mTc-tetrofosmin-single-photon-emission-computed-tomography-in-patients-with-diabetes-mellitus-and-stable-coronary-artery-disease",{"references":1542,"abstract":1544,"title":1546,"doi":1548},{"VOID":1543},"Rawshani A, Rawshani A, Franzen S et al (2017) Mortality and Cardiovascular Disease in Type 1 and type 2 diabetes. N Engl J Med 376:1407–1418. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa1608664\nMosenzon O, Alguwaihes A, Leon JLA et al (2021) CAPTURE: a multinational, cross-sectional study of cardiovascular disease prevalence in adults with type 2 diabetes across 13 countries. Cardiovasc Diabetol 20:154. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12933-021-01344-0\nGoraya TY, Leibson CL, Palumbo PJ, Weston SA, Killian JM, Pfeifer EA, Jacobsen SJ, Frye RL, Roger VL (2002) Coronary atherosclerosis in diabetes mellitus: a population-based autopsy study. J Am Coll Cardiol 40:946–953. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0735-1097(02)02065-x\nPaneni F, Beckman JA, Creager MA, Cosentino F (2013) Diabetes and vascular disease: pathophysiology, clinical consequences, and medical therapy: part I. Eur Heart J 34:2436–2443. https:\u002F\u002Fdoi.org\u002F10.1093\u002Feurheartj\u002Feht149\nTaylor KS, Heneghan CJ, Farmer AJ, Fuller AM, Adler AI, Aronson JK, Stevens RJ (2013) All-cause and cardiovascular mortality in middle-aged people with type 2 diabetes compared with people without diabetes in a large U.K. primary care database. Diabetes Care 36:2366–2371. https:\u002F\u002Fdoi.org\u002F10.2337\u002Fdc12-1513\nBeckman JA, Paneni F, Cosentino F, Creager MA (2013) Diabetes and vascular disease: pathophysiology, clinical consequences, and medical therapy: part II. Eur Heart J 34:2444–2452. https:\u002F\u002Fdoi.org\u002F10.1093\u002Feurheartj\u002Feht142\nMurthy VL, Bateman TM, Beanlands RS et al (2018) Clinical quantification of myocardial blood Flow using PET: joint position paper of the SNMMI Cardiovascular Council and the ASNC. J Nucl Cardiol 25:269–297. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12350-017-1110-x\nShaw LJ, Iskandrian AE (2004) Prognostic value of gated myocardial perfusion SPECT. J Nucl Cardiol 11:171–185. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nuclcard.2003.12.004\nMurthy VL, Naya M, Foster CR, Gaber M, Hainer J, Klein J, Dorbala S, Blankstein R, Di Carli MF (2012) Association between coronary vascular dysfunction and cardiac mortality in patients with and without diabetes mellitus. Circulation 126:1858–1868. https:\u002F\u002Fdoi.org\u002F10.1161\u002FCIRCULATIONAHA.112.120402\nGulati M, Levy PD, Mukherjee D et al (2021) 2021 AHA\u002FACC\u002FASE\u002FCHEST\u002FSAEM\u002FSCCT\u002FSCMR Guideline for the evaluation and diagnosis of chest Pain: executive summary: a report of the American College of Cardiology\u002FAmerican Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 144:e368–e454. https:\u002F\u002Fdoi.org\u002F10.1161\u002FCIR.0000000000001030\nDi Carli MF, Janisse J, Grunberger G, Ager J (2003) Role of chronic hyperglycemia in the pathogenesis of coronary microvascular dysfunction in diabetes. J Am Coll Cardiol 41:1387–1393. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0735-1097(03)00166-9\nOh M, Choi JH, Kim SO, Lee PH, Ahn JM, Lee SW, Moon DH, Lee CW (2021) Comparison of empagliflozin and sitagliptin therapy on myocardial perfusion reserve in diabetic patients with coronary artery disease. Nucl Med Commun 42:972–978. https:\u002F\u002Fdoi.org\u002F10.1097\u002FMNM.0000000000001429\nHan S, Kim YH, Ahn JM et al (2018) Feasibility of dynamic stress (201)Tl\u002Frest (99m)Tc-tetrofosmin single photon emission computed tomography for quantification of myocardial perfusion reserve in patients with stable coronary artery disease. Eur J Nucl Med Mol Imaging 45:2173–2180. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00259-018-4057-5\nBairey Merz CN, Pepine CJ, Walsh MN, Fleg JL (2017) Ischemia and no obstructive coronary artery Disease (INOCA): developing evidence-based therapies and Research Agenda for the Next Decade. Circulation 135:1075–1092. https:\u002F\u002Fdoi.org\u002F10.1161\u002FCIRCULATIONAHA.116.024534\nRaghavan S, Vassy JL, Ho YL, Song RJ, Gagnon DR, Cho K, Wilson PWF, Phillips LS (2019) Diabetes Mellitus-Related all-cause and Cardiovascular Mortality in a national cohort of adults. J Am Heart Assoc 8:e011295. https:\u002F\u002Fdoi.org\u002F10.1161\u002FJAHA.118.011295\nDi Carli MF, Charytan D, McMahon GT, Ganz P, Dorbala S, Schelbert HR (2011) Coronary circulatory function in patients with the metabolic syndrome. J Nucl Med 52:1369–1377. https:\u002F\u002Fdoi.org\u002F10.2967\u002Fjnumed.110.082883\nCzernin J, Waldherr C (2003) Cigarette smoking and coronary blood flow. Prog Cardiovasc Dis 45:395–404. https:\u002F\u002Fdoi.org\u002F10.1053\u002Fpcad.2003.00104\nBerenson GS, Srinivasan SR, Bao W, Newman WP 3rd, Tracy RE, Wattigney WA (1998) Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. The Bogalusa Heart Study. N Engl J Med 338:1650–1656. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJM199806043382302\nHoward G, Wagenknecht LE, Burke GL, Diez-Roux A, Evans GW, McGovern P, Nieto FJ, Tell GS (1998) Cigarette smoking and progression of atherosclerosis: the atherosclerosis risk in Communities (ARIC) Study. JAMA 279:119–124. https:\u002F\u002Fdoi.org\u002F10.1001\u002Fjama.279.2.119\nHamasaki S, Al Suwaidi J, Higano ST, Miyauchi K, Holmes DR Jr, Lerman A (2000) Attenuated coronary flow reserve and vascular remodeling in patients with hypertension and left ventricular hypertrophy. J Am Coll Cardiol 35:1654–1660. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0735-1097(00)00594-5\nAl-Mashhadi RH, Al-Mashhadi AL, Nasr ZP et al (2021) Local pressure drives low-density lipoprotein Accumulation and Coronary Atherosclerosis in Hypertensive Minipigs. J Am Coll Cardiol 77:575–589. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2020.11.059\nWu KY, Timmerman NP, McPhedran R, Hossain A, Beanlands RSB, Chong AY, deKemp RA (2020) Differential association of diabetes mellitus and female sex with impaired myocardial flow reserve across the spectrum of epicardial coronary disease. Eur Heart J Cardiovasc Imaging 21:576–584. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fehjci\u002Fjez163\nMurthy VL, Naya M, Taqueti VR et al (2014) Effects of sex on coronary microvascular dysfunction and cardiac outcomes. Circulation 129:2518–2527. https:\u002F\u002Fdoi.org\u002F10.1161\u002FCIRCULATIONAHA.113.008507\nKim YH, Ahn JM, Park DW et al (2012) Impact of ischemia-guided revascularization with myocardial perfusion imaging for patients with multivessel coronary disease. J Am Coll Cardiol 60:181–190. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2012.02.061\nDe Bruyne B, Oldroyd KG, Pijls NHJ (2016) Microvascular (dys)function and clinical outcome in stable coronary disease. J Am Coll Cardiol 67:1170–1172. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2015.11.066\nHerzog BA, Husmann L, Valenta I, Gaemperli O, Siegrist PT, Tay FM, Burkhard N, Wyss CA, Kaufmann PA (2009) Long-term prognostic value of 13 N-ammonia myocardial perfusion positron emission tomography added value of coronary flow reserve. J Am Coll Cardiol 54:150–156. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2009.02.069\nFarhad H, Dunet V, Bachelard K, Allenbach G, Kaufmann PA, Prior JO (2013) Added prognostic value of myocardial blood flow quantitation in rubidium-82 positron emission tomography imaging. Eur Heart J Cardiovasc Imaging 14:1203–1210. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fehjci\u002Fjet068\nNkoulou R, Fuchs TA, Pazhenkottil AP et al (2016) Absolute myocardial blood Flow and Flow Reserve assessed by gated SPECT with cadmium-Zinc-Telluride detectors using 99mTc-Tetrofosmin: head-to-Head comparison with 13 N-Ammonia PET. J Nucl Med 57:1887–1892. https:\u002F\u002Fdoi.org\u002F10.2967\u002Fjnumed.115.165498\nHsu B, Hu LH, Yang BH, Chen LC, Chen YK, Ting CH, Hung GU, Huang WS, Wu TC (2017) SPECT myocardial blood flow quantitation toward clinical use: a comparative study with (13)N-Ammonia PET myocardial blood flow quantitation. Eur J Nucl Med Mol Imaging 44:117–128. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00259-016-3491-5\nAgostini D, Roule V, Nganoa C, Roth N, Baavour R, Parienti JJ, Beygui F, Manrique A (2018) First validation of myocardial flow reserve assessed by dynamic (99m)Tc-sestamibi CZT-SPECT camera: head to head comparison with (15)O-water PET and fractional flow reserve in patients with suspected coronary artery disease. The WATERDAY study. Eur J Nucl Med Mol Imaging 45:1079–1090. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00259-018-3958-7\nValensi P, Lorgis L, Cottin Y (2011) Prevalence, incidence, predictive factors and prognosis of silent myocardial infarction: a review of the literature. Arch Cardiovasc Dis 104:178–188. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.acvd.2010.11.013",{"EN":1545},"We aimed to examine the associations of cardiovascular risk factors with myocardial perfusion reserve (MPR) in patients with type 2 diabetes and stable coronary artery disease. The study patients were retrospectively identified from a database of patients with diabetes and stable coronary artery disease at Asan Medical Center (Seoul, Republic of Korea), covering the period from 2017 to 2019. The primary outcome variable was MPR assessed by dynamic stress 201Tl\u002Frest 99mTc-tetrofosmin SPECT. Univariable and stepwise multivariable analyses were performed to assess the associations of cardiovascular risk factors with MPR. A total of 276 patients (236 men and 40 women) were included. The median global MPR was 2.4 (interquartile range 1.9–3.0). Seventy-five (27.2%) patients had an MPR \u003C 2.0. Multivariable linear regression showed that smoking (ß = − 0.44, 95% confidence interval − 0.68 to − 0.21, P \u003C 0.001), hypertension (ß = − 0.24, 95% confidence interval − 0.47 to − 0.02, P = 0.033), and summed difference score (ß = − 0.05, 95% confidence interval − 0.07 to − 0.03, P \u003C 0.001) were independently associated with MPR. Abnormal MPR (\u003C 2.0) was associated with a higher incidence of cardiac death or myocardial infarction (P = 0.034). MPR assessed by dynamic stress 201Tl\u002Frest 99mTc-tetrofosmin SPECT was impaired in a large cohort of patients with diabetes. After adjusting for risk variables, including standard myocardial perfusion imaging characteristics, smoking, and hypertension were associated with MPR. Our results may aid in identifying patients with impaired MPR and stratifying patients with type 2 diabetes.",{"EN":1547},"Associations of cardiovascular and diabetes-related risk factors with myocardial perfusion reserve assessed by 201Tl\u002F99mTc-tetrofosmin single-photon emission computed tomography in patients with diabetes mellitus and stable coronary artery disease",{"VOID":1549},"10.1007\u002Fs10554-023-02859-1","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10554-023-02859-1",[1552,1567,1579,1591,1606,1618,1634,1649],{"id":1553,"sortIndex":198,"researcher":18,"roles":1554,"affiliations":1555,"properties":1564},"8d841611-e526-45b3-823f-16cb1fd6f957",[639],[1556],{"id":18,"sortIndex":19,"affiliation":1557,"properties":18},{"id":1558,"createTime":1559,"updateTime":1559,"relativeEntities":1560,"slug":18,"properties":1561,"entityType":82,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"7e8f1dd0-a8d8-495e-8455-646d4a4bf97b","2023-12-18T07:06:25.644+00:00",[],{"title":1562},{"VI":1563},"Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea",{"title":1565},{"VI":1566},"Eonwoo Shin",{"id":1568,"sortIndex":92,"researcher":18,"roles":1569,"affiliations":1570,"properties":1576},"2cd926b8-91cc-40dd-b01d-f87d6227fe46",[639],[1571],{"id":18,"sortIndex":19,"affiliation":1572,"properties":18},{"id":1558,"createTime":1559,"updateTime":1559,"relativeEntities":1573,"slug":18,"properties":1574,"entityType":82,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1575},{"VI":1563},{"title":1577},{"VI":1578},"Minyoung 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RH, Alexander KM, Liao R et al (2016) AL (light-chain) cardiac amyloidosis: a review of diagnosis and therapy. J Am Coll Cardiol 68(12):1323–1341. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2016.06.053\nRuberg FL, Grogan M, Hanna M et al (2019) Transthyretin amyloid cardiomyopathy: JACC state-of-the-art review. J Am Coll Cardiol 73(22):2872–2891. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2019.04.003\nWechalekar AD, Gillmore JD, Hawkins PN (2016) Systemic amyloidosis. Lancet (London, England) 387(10038):2641–2654. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0140-6736(15)01274-x\nPhelan D, Collier P, Thavendiranathan P et al (2012) Relative apical sparing of longitudinal strain using two-dimensional speckle-tracking echocardiography is both sensitive and specific for the diagnosis of cardiac amyloidosis. Heart 98(19):1442–1448. https:\u002F\u002Fdoi.org\u002F10.1136\u002Fheartjnl-2012-302353\nMotwani M, Dey D, Berman DS et al (2017) Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J 38(7):500–507. https:\u002F\u002Fdoi.org\u002F10.1093\u002Feurheartj\u002Fehw188\nSanchez-Martinez S, Duchateau N, Erdei T et al (2018) Machine learning analysis of left ventricular function to characterize heart failure with preserved ejection fraction. Circ Cardiovasc Imaging 11(4):e007138. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcircimaging.117.007138\nNarula S, Shameer K, Salem Omar AM et al (2016) Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol 68(21):2287–2295. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacc.2016.08.062\nSengupta PP, Huang YM, Bansal M et al (2016) Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcircimaging.115.004330\nAwan SE, Bennamoun M, Sohel F et al (2019) Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics. ESC Heart Fail 6(2):428–435. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fehf2.12419\nAttia ZI, Noseworthy PA, Lopez-Jimenez F et al (2019) An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet (London, England) 394(10201):861–867. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0140-6736(19)31721-0\nRaj S, Ray KC (2018) A personalized arrhythmia monitoring platform. Sci Rep 8(1):11395. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-018-29690-2\nDorbala S, Ando Y, Bokhari S et al (2019) ASNC\u002FAHA\u002FASE\u002FEANM\u002FHFSA\u002FISA\u002FSCMR\u002FSNMMI expert consensus recommendations for multimodality imaging in cardiac amyloidosis: part 1 of 2-evidence base and standardized methods of imaging. J Card Fail 25(11):e1–e39. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cardfail.2019.08.001\nOmmen SR, Mital S, Burke MA et al (2020) 2020 AHA\u002FACC guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: executive summary: a report of the American College of Cardiology\u002FAmerican Heart Association Joint Committee on Clinical Practice guidelines. Circulation 142(25):e533–e557. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcir.0000000000000938\nChurpek MM, Yuen TC, Winslow C et al (2016) Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med 44(2):368–374. https:\u002F\u002Fdoi.org\u002F10.1097\u002Fccm.0000000000001571\nMortazavi BJ, Bucholz EM, Desai NR et al (2019) Comparison of machine learning methods with national cardiovascular data registry models for prediction of risk of bleeding after percutaneous coronary intervention. JAMA Netw Open 2(7):e196835. https:\u002F\u002Fdoi.org\u002F10.1001\u002Fjamanetworkopen.2019.6835\nAl’Aref SJ, Singh G, van Rosendael AR et al (2019) Determinants of in-hospital mortality after percutaneous coronary intervention: a machine learning approach. J Am Heart Assoc 8(5):e011160. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fjaha.118.011160\nSun JP, Stewart WJ, Yang XS et al (2009) Differentiation of hypertrophic cardiomyopathy and cardiac amyloidosis from other causes of ventricular wall thickening by two-dimensional strain imaging echocardiography. Am J Cardiol 103(3):411–415. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.amjcard.2008.09.102\nDi Bella G, Minutoli F, Pingitore A et al (2011) Endocardial and epicardial deformations in cardiac amyloidosis and hypertrophic cardiomyopathy. Circ J 75(5):1200–1208. https:\u002F\u002Fdoi.org\u002F10.1253\u002Fcircj.cj-10-0844\nBaccouche H, Maunz M, Beck T et al (2012) Differentiating cardiac amyloidosis and hypertrophic cardiomyopathy by use of three-dimensional speckle tracking echocardiography. Echocardiography 29(6):668–677. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1540-8175.2012.01680.x\nLiu D, Hu K, Niemann M et al (2013) Effect of combined systolic and diastolic functional parameter assessment for differentiation of cardiac amyloidosis from other causes of concentric left ventricular hypertrophy. Circ Cardiovasc Imaging 6(6):1066–1072. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcircimaging.113.000683\nPagourelias ED, Mirea O, Duchenne J et al (2017) Echo parameters for differential diagnosis in cardiac amyloidosis: a head-to-head comparison of deformation and nondeformation parameters. Circ Cardiovasc Imaging 10(3):e005588. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcircimaging.116.005588\nBoldrini M, Cappelli F, Chacko L et al (2020) Multiparametric echocardiography scores for the diagnosis of cardiac amyloidosis. JACC Cardiovasc Imaging 13(4):909–920. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jcmg.2019.10.011\nZhang J, Gajjala S, Agrawal P et al (2018) Fully automated echocardiogram interpretation in clinical practice. Circulation 138(16):1623–1635. https:\u002F\u002Fdoi.org\u002F10.1161\u002Fcirculationaha.118.034338\nShameer K, Johnson KW, Glicksberg BS et al (2018) Machine learning in cardiovascular medicine: are we there yet? Heart 104(14):1156–1164. https:\u002F\u002Fdoi.org\u002F10.1136\u002Fheartjnl-2017-311198",{"EN":1694},"Cardiac amyloidosis has a poor prognosis, and high mortality and is often misdiagnosed as hypertrophic cardiomyopathy, leading to delayed diagnosis. Machine learning combined with speckle tracking echocardiography was proposed to automate differentiating two conditions. A total of 74 patients with pathologically confirmed monoclonal immunoglobulin light chain cardiac amyloidosis and 64 patients with hypertrophic cardiomyopathy were enrolled from June 2015 to November 2018. Machine learning models utilizing traditional and advanced algorithms were established and determined the most significant predictors. The performance was evaluated by the receiver operating characteristic curve (ROC) and the area under the curve (AUC). With clinical and echocardiography data, all models showed great discriminative performance (AUC > 0.9). Compared with logistic regression (AUC 0.91), machine learning such as support vector machine (AUC 0.95, p = 0.477), random forest (AUC 0.97, p = 0.301) and gradient boosting machine (AUC 0.98, p = 0.230) demonstrated similar capability to distinguish cardiac amyloidosis and hypertrophic cardiomyopathy. With speckle tracking echocardiography, the predictive performance of the voting model was similar to that of LightGBM (AUC was 0.86 for both), while the AUC of XGBoost was slightly lower (AUC 0.84). In fivefold cross-validation, the voting model was more robust globally and superior to the single model in some test sets. Data-driven machine learning had shown admirable performance in differentiating two conditions and could automatically integrate abundant variables to identify the most discriminating predictors without making preassumptions. In the era of big data, automated machine learning will help to identify patients with cardiac amyloidosis and timely and effectively intervene, thus improving the outcome.",{"EN":1696},"Machine learning algorithms to automate differentiating cardiac amyloidosis from hypertrophic 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Lancet 381:242–255\nHypertropic cardiomyopathy | British Heart Foundation. https:\u002F\u002Fwww.bhf.org.uk\u002Finformationsupport\u002Fconditions\u002Fcardiomyopathy\u002Fhypertrophic-cardiomyopathy. Accessed 30 Nov 2021\nMaron BJ (2018) Clinical course and management of hypertrophic cardiomyopathy. N Engl J Med 379:655–668\nAuthors\u002FTask Force members, ElliottAnastasakis PMA et al (2014) 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy. Eur Heart J 35:2733–2779\nAutore C, Bernabò P, Barillà CS, Bruzzi P, Spirito P (2005) The prognostic importance of left ventricular outflow obstruction in hypertrophic cardiomyopathy varies in relation to the severity of symptoms. J Am Coll Cardiol 45:1076–1080\nNishimura RA, Holmes DR (2004) Hypertrophic obstructive cardiomyopathy. N Engl J Med 350:1320–1327\nDonati F, Myerson S, Bissell MM, Smith NP, Neubauer S, Monaghan MJ, Nordsletten DA, Lamata P (2017) Beyond Bernoulli: improving the accuracy and precision of noninvasive estimation of peak pressure drops. Circ Cardiovasc Imaging. https:\u002F\u002Fdoi.org\u002F10.1161\u002FCIRCIMAGING.116.005207\nGill H, Fernandes J, Chehab O, Prendergast B, Redwood S, Chiribiri A, Nordsletten D, Rajani R, Lamata P (2021) Evaluation of aortic stenosis: from Bernoulli and Doppler to Navier-stokes. Trends Cardiovasc Med. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.tcm.2021.12.003\nBois JP, Geske JB, Foley TA, Ommen SR, Pellikka PA (2017) Comparison of maximal wall thickness in hypertrophic cardiomyopathy differs between magnetic resonance imaging and transthoracic echocardiography. Am J Cardiol 119:643–650\nHindieh W, Weissler-Snir A, Hammer H, Adler A, Rakowski H, Chan RH (2017) Discrepant measurements of maximal left ventricular wall thickness between cardiac magnetic resonance imaging and echocardiography in patients with hypertrophic cardiomyopathy. Circ Cardiovasc Imaging. https:\u002F\u002Fdoi.org\u002F10.1161\u002FCIRCIMAGING.117.006309\nPhelan D, Sperry BW, Thavendiranathan P, Collier P, Popović ZB, Lever HM, Smedira NG, Desai MY (2017) Comparison of ventricular septal measurements in hypertrophic cardiomyopathy patients who underwent surgical myectomy using multimodality imaging and implications for diagnosis and management. Am J Cardiol 119:1656–1662\nRickers C, Wilke NM, Jerosch-Herold M, Casey SA, Panse P, Panse N, Weil J, Zenovich AG, Maron BJ (2005) Utility of cardiac magnetic resonance imaging in the diagnosis of hypertrophic cardiomyopathy. Circulation 112:855–861\nPosma JL, Blanksma PK, van der Wall EE, Hamer HP, Mooyaart EL, Lie KI (1996) Assessment of quantitative hypertrophy scores in hypertrophic cardiomyopathy: magnetic resonance imaging versus echocardiography. Am Heart J 132:1020–1027\nSchulz-Menger J, Abdel-Aty H, Busjahn A, Wassmuth R, Pilz B, Dietz R, Friedrich M (2006) Left ventricular outflow tract planimetry by cardiovascular magnetic resonance differentiates obstructive from non-obstructive hypertrophic cardiomyopathy. J Cardiovasc Magn Reson 8:741–746\nIbrahim M, Rao C, Ashrafian H, Chaudhry U, Darzi A, Athanasiou T (2012) Modern management of systolic anterior motion of the mitral valve. Eur J Cardiothorac Surg 41:1260–1270\nRaut M, Maheshwari A, Swain B (2018) Awareness of “systolic anterior motion” in different conditions. Clin Med Insights Cardiol 12:1179546817751921\nPatel P, Dhillon A, Popovic ZB, Smedira NG, Rizzo J, Thamilarasan M, Agler D, Lytle BW, Lever HM, Desai MY (2015) Left ventricular outflow tract obstruction in hypertrophic cardiomyopathy patients without severe septal hypertrophy: implications of mitral valve and papillary muscle abnormalities assessed using cardiac magnetic resonance and echocardiography. Circ Cardiovasc Imaging 8:e003132\nNara I, Iino T, Watanabe H, Sato W, Watanabe K, Shimbo M, Umeta Y, Ito H (2018) Morphological determinants of obstructive hypertrophic cardiomyopathy obtained using echocardiography. Int Heart J 59:339–346\nKramer CM, Appelbaum E, Desai MY et al (2015) Hypertrophic cardiomyopathy registry: the rationale and design of an international, observational study of hypertrophic cardiomyopathy. Am Heart J 170:223–230\nNeubauer S, Kolm P, Ho CY et al (2019) Distinct subgroups in hypertrophic cardiomyopathy in the NHLBI HCM registry. J Am Coll Cardiol 74:2333–2345\nHatle L, Brubakk A, Tromsdal A, Angelsen B (1978) Noninvasive assessment of pressure drop in mitral stenosis by Doppler ultrasound. Br Heart J 40:131–140\nYushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128\nCicchetti DV (1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 6:284–290\nMathWorks MATLAB—MathWorks, Natick, MA, USA. https:\u002F\u002Fuk.mathworks.com\u002Fproducts\u002Fmatlab.html. Accessed 13 Mar 2021\nSun X, Xu W (2014) Fast implementation of DeLong’s algorithm for comparing the areas under correlated receiver operating characteristic curves. IEEE Signal Process Lett 21:1389–1393\nYOUDEN WJ, (1950) Index for rating diagnostic tests. Cancer 3:32–35\nKoo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163\nMaron MS, Olivotto I, Harrigan C, Appelbaum E, Gibson CM, Lesser JR, Haas TS, Udelson JE, Manning WJ, Maron BJ (2011) Mitral valve abnormalities identified by cardiovascular magnetic resonance represent a primary phenotypic expression of hypertrophic cardiomyopathy. Circulation 124:40–47\nBrownlee J. (2021) Impact of dataset size on deep learning model skill and performance estimates. In: Machine learning mastery. https:\u002F\u002Fmachinelearningmastery.com\u002Fimpact-of-dataset-size-on-deep-learning-model-skill-and-performance-estimates\u002F. Accessed 10 Apr 2021\nLuo Y, Yang D, Liu H, Wan K, Sun J, Zhang T, Chen Y (2016) Mitral valve leaflet length as an important factor to differentiate hypertrophic cardiomyopathy from other causes of left ventricular hypertrophy. J Cardiovasc Magn Reson 18:P272\nHealio (2021) Venturi effect. In: Healio. https:\u002F\u002Fwww.healio.com\u002Fcardiology\u002Flearn-the-heart\u002Fcardiology-review\u002Ftopic-reviews\u002Fventuri-effect. Accessed 14 Apr 2021\nCritoph CH, Pantazis A, Tome Esteban MT, Salazar-Mendiguchía J, Pagourelias ED, Moon JC, Elliott PM (2014) The influence of aortoseptal angulation on provocable left ventricular outflow tract obstruction in hypertrophic cardiomyopathy. Open Heart 1:e000176\nDoddamani S, Bello R, Friedman MA et al (2007) Demonstration of left ventricular outflow tract eccentricity by real time 3D echocardiography: implications for the determination of aortic valve area. Echocardiography 24:860–866\nFerreira PF, Gatehouse PD, Mohiaddin RH, Firmin DN (2013) Cardiovascular magnetic resonance artefacts. J Cardiovasc Magn Reson 15:41\nMaron BJ, Desai MY, Nishimura RA, Spirito P, Rakowski H, Towbin JA, Rowin EJ, Maron MS, Sherrid MV (2022) Diagnosis and evaluation of hypertrophic cardiomyopathy: JACC state-of-the-art review. J Am Coll Cardiol 79:372–389",{"EN":1993},"Left ventricular outflow tract obstruction (LVOTO) is common in hypertrophic cardiomyopathy (HCM), but relationships between anatomical metrics and obstruction are poorly understood. We aimed to develop machine learning methods to evaluate LVOTO in HCM patients and quantify relationships between anatomical metrics and obstruction. This retrospective analysis of 1905 participants of the HCM Registry quantified 11 anatomical metrics derived from 14 landmarks automatically detected on the three-chamber long axis cine CMR images. Linear and logistic regression was used to quantify strengths of relationships with the presence of LVOTO (defined by resting Doppler pressure drop of > 30 mmHg), using the area under the receiver operating characteristic (AUC). Intraclass correlation coefficients between the network predictions and three independent observers showed similar agreement to that between observers. The distance from anterior mitral valve leaflet tip to basal septum (AML-BS) was most highly correlated with Doppler pressure drop (R2 = 0.19, p \u003C 10–5). Multivariate stepwise regression found the best predictive model included AML-BS, AML length to aortic valve diameter ratio, AML length to LV width ratio, and midventricular septal thickness metrics (AUC 0.84). Excluding AML-BS, metrics grouped according to septal hypertrophy, LV geometry, and AML anatomy each had similar associations with LVOTO (AUC 0.71, 0.71, 0.68 respectively, p = ns), significantly less than their combination (AUC 0.77, p \u003C 0.05 for each). Anatomical metrics derived from a standard three-chamber CMR cine acquisition can be used to highlight risk of LVOTO, and suggest further investigation if necessary. A combination of geometric factors is required to provide the best risk prediction.",{"EN":1995},"Machine learning evaluation of LV outflow obstruction in hypertrophic cardiomyopathy using three-chamber cardiovascular magnetic resonance",{"VOID":1997},"10.1007\u002Fs10554-022-02724-7","2025-01-21T23:03:59.317+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10554-022-02724-7",[2001,2016,2032,2045,2057,2069,2084,2096,2111,2126,2141,2153,2165,2182,2195],{"id":2002,"sortIndex":71,"researcher":18,"roles":2003,"affiliations":2004,"properties":2013},"6f16ca87-6b7a-476d-b911-270780547f86",[639],[2005],{"id":18,"sortIndex":19,"affiliation":2006,"properties":18},{"id":2007,"createTime":2008,"updateTime":2008,"relativeEntities":2009,"slug":18,"properties":2010,"entityType":82,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"73d8264a-762b-4e96-aaa3-87e8f50eb8d9","2023-12-28T06:13:56.870+00:00",[],{"title":2011},{"VI":2012},"Department of Biomedical Engineering, King’s College London, London, UK",{"title":2014},{"VI":2015},"Sepas Ryan Saraskani",{"id":2017,"sortIndex":2018,"researcher":18,"roles":2019,"affiliations":2020,"properties":2029},"8778f125-d962-486f-829d-8dcc5e15547b",12,[639],[2021],{"id":18,"sortIndex":19,"affiliation":2022,"properties":18},{"id":2023,"createTime":2024,"updateTime":2024,"relativeEntities":2025,"slug":18,"properties":2026,"entityType":82,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"f43f79de-1f44-4237-ae64-015ea4ee2b57","2023-12-28T06:13:56.906+00:00",[],{"title":2027},{"VI":2028},"Cardiovascular Division, University of Virginia Health, Charlottesville, USA",{"title":2030},{"VI":2031},"Christopher M. 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