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59 results on '"Kepka C"'

1. Probing $WW\gamma \gamma$ and $ZZ\gamma \gamma$ quartic anomalous couplings with 10 pb$^{-1}$ at the LHC

Author

Chapon, E. and Royon, O. Kepka. C.

Subjects
High Energy Physics - Phenomenology
Abstract

We report on a possible measurement at the LHC using the first data and a luminosity of 10 pb$^{-1}$ of $W$ and $Z$ pair production via two-photon exchange. This measurement allows to increase the present sensitivity on $WW\gamma \gamma$ and $ZZ\gamma \gamma$ quartic anomalous couplings from the LEP experiments by almost three orders of magnitude., Comment: 4 pages, 3 figures

Published
2009

2. Non-invasive characterization of pancoronary inflammation by computed tomography angiography in patients with recent spontaneous coronary dissection

Author

Wolny, R, primary, Kwiecinski, J, additional, Zalewska, J, additional, Michalowska, I, additional, Kruk, M, additional, Kepka, C, additional, Prejbisz, A, additional, Pregowski, J, additional, Skowronski, J, additional, Kobierska, A, additional, Ciesielski, R, additional, Januszewicz, A, additional, Witkowski, A, additional, Adlam, D, additional, and Kadziela, J, additional

Published
2022
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4. Pilot study of the multicentre DISCHARGE trial

Author

Rubeis, G. de, Napp, A.E., Schlattmann, P., Geleijns, J., Laule, M., Dreger, H., Kofoed, K., Sorgaard, M., Engstrom, T., Tilsted, H.H., Boi, A., Porcu, M., Cossa, S., Rodriguez-Palomares, J.F., Valente, F.X., Roque, A., Feuchtner, G., Plank, F., Stechovsky, C., Adla, T., Schroeder, S., Zelesny, T., Gutberlet, M., Woinke, M., Karolyi, M., Karady, J., Donnelly, P., Ball, P., Dodd, J.D., Hensey, M., Mancone, M., Ceccacci, A., Berzina, M., Zvaigzne, L., Sakalyte, G., Basevicius, A., Ilnicka-Suckiel, M., Kusmierz, D., Faria, R., Gama-Ribeiro, V., Benedek, I., Benedek, T., Adjic, F., Cankovic, M., Berry, C., Delles, C., Thwaite, E., Davis, G., Knuuti, J., Pietila, M., Kepka, C., Kruk, M., Vidakovic, R., Neskovic, A.N., Lecumberri, I., Gonzales, I.D., Ruzsics, B., Fisher, M., Dewey, M., Francone, M., and DISCHARGE Trial Grp

Abstract

The original version of this article, published on 16 December 2019, unfortunately contained two mistakes.

Published
2020

5. Correction to: Pilot study of the multicentre DISCHARGE trial: image quality and protocol adherence results of computed tomography and invasive coronary angiography

Author

De Rubeis, G., Napp, A. E., Schlattmann, P., Geleijns, J., Laule, M., Dreger, H., Kofoed, K., Sorgaard, M., Engstrom, T., Tilsted, H. H., Boi, A., Porcu, M., Cossa, S., Rodriguez-Palomares, J. F., Valente, F. X., Roque, A., Feuchtner, G., Plank, F., Stechovsky, C., Adla, T., Schroeder, S., Zelesny, T., Gutberlet, M., Woinke, M., Karolyi, M., Karady, J., Donnelly, P., Ball, P., Dodd, J. D., Hensey, M., Mancone, M., Ceccacci, A., Berzina, M., Zvaigzne, L., Sakalyte, G., Basevicius, A., Ilnicka-Suckiel, M., Kusmierz, D., Faria, R., Gama-Ribeiro, V., Benedek, I., Benedek, T., Adjic, F., Cankovic, M., Berry, C., Delles, C., Thwaite, E., Davis, G., Knuuti, J., Pietila, M., Kepka, C., Kruk, M., Vidakovic, R., Neskovic, A. N., Lecumberri, I., Gonzales, I. D., Ruzsics, B., Fisher, M., Dewey, M., and Francone, M.

Subjects
TAVI, CT, MR, Radiology, Nuclear Medicine and imaging, General Medicine
Published
2020

8. Coronary arteries in fibromuscular dysplasia. 3-Dimensional coronary CT, case-control study. The ARCARDIA-POL BIS study

Author

Kruk, M, primary, Szkamruk, K, additional, Adlam, D, additional, Persu, A, additional, Pappaccogli, M, additional, Van Der Niepen, P, additional, Kepka, C, additional, Januszewicz, M, additional, Pregowski, J, additional, Skowronski, J, additional, Kabat, M, additional, Warchol-Celinska, E, additional, Prejbisz, A, additional, Dobrowolski, P, additional, and Januszewicz, A, additional

Published
2020
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9. Aortic invovement in fibromuscular dysplasia. 3-dimensional CT, case-control study. THE ARCADIA-POL BIS study

Author

Szkamruk, K, primary, Kruk, M, additional, Kepka, C, additional, Adlam, D, additional, Persu, A, additional, Canning, C, additional, Pappaccogli, M, additional, Van Der Niepen, P, additional, Januszewicz, M, additional, Kabat, M, additional, Warchol-Celinska, E, additional, Prejbisz, A, additional, Jozwik-Plebanek, K, additional, Dobrowolski, P, additional, and Januszewicz, A, additional

Published
2020
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14. P5866Economic outcomes of the CAT-CAD randomised trial assessing coronary artery computed tomography as the first-choice anatomic test for individuals with suspected significant coronary artery disease

Author

Rudzinski, P.N., primary, Kruk, M., additional, Kepka, C., additional, Schoepf, U.J., additional, Duguay, T., additional, Dzielinska, Z., additional, Pregowski, J., additional, Witkowski, A., additional, Ruzyllo, W., additional, and Demkow, M., additional

Published
2017
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16. The prevalence of abnormal glucose regulation in patients with coronary artery disease across Europe. The Euro Heart Survey on diabetes and the heart

Author

Bartnik M., Ryden L., Ferrari R., Malmberg K., Pyorala K., Simoons M., Standl E., Soler-Soler J., Ohrvik J., Manini M., Bramley C., Laforest V., Taylor C., Del Gaiso S., Huber K., De Backer G., Sirakova V., Cerbak R., Thayssen P., Lehto S., Delahaye F., Kobulia B., Zeymer U., Cokkinos D., Karlocai K., Shelley E., Behar S., Maggioni A., Grabauskiene V., Deckers J., Asmussen I., Stepinska J., Goncalves L., Mareev V., Riecansky I., Kenda M. F., Lopez-Sendon J. L., Rosengren A., Buser P., Okay T., Sychov O., Fox K., Wood D., Alonso A., Boersma E., Crijns H., Gitt A., McGregor K., Mulder B., Nieminen M., Priori S., Tavazzi L., Vahanian A., Vardas P., Wijns W., Aydinkoc K., Spenka M., Wascher T. C., Sourij H., Dusko V., Radivojevic M., Goudev A. R., Tzekova M. L., Simeonov P., Pentchev V., Yotov Y., Torbova S. G., Stoyanovsky V., Stoynev E., Ostrovsky I., Moroz-Vadalazhskaya N., Cocco G., Antoniades L., Kyprianou D., Florian J., Yaghmaee S., Kvasnika J., Krizova A., Rosolova H., Petrlova B., Borivoj S., Poloczek M., Niebauer J., Drechsler K., Sechtem U., Vogelsberg H., Blank E., Breithardt G., Wedekind H., Ksoll B., Laks T., Ambos A., Tupits H., Kalinina L., Anton L., Planken U., Saad A., Andraos A. W., Shafy S. A., Metias B. D., Ibrahim M. A., Tantawi H., Lopez Bescos L., Huelmos A., Fernandez Aviles F., De La Fuente Galan L., Vinuela P. T., Velasco Rami J. A., Soriano F. R., Soledad Alcasena-Juango M., Berjon-Reyero J., Orcajo N. A., Garcia Calabozo R., Masia R., Sala J., Rohlfs I., De Diego J. J. G., Martin L. S., De El Escorial S. L., Latasa M. I., Miranda I. A., Garcia A. A., Andrade M. A., Conde A. C., Ortuno F. M., Climent V., Gonzalo F. E., Martinez V. B., Ortega J. A. R., De Alicante S. J., Galvez C. P., Rivero R. F., Belsue F. V., Rubio J. R. S., Escorihuela A. L., Gonzalez V. B., Iglesias F. C., Minguezy Enriquez De Salamanca I., Rejon F. R., Cobo A. L., Tarin N., Savolainen K., Nieminen M. S., Syvanne M., Pietila M., Mustonen J., Juntunen I., Marco J., Gilliume S., Bassand J. P., Espinosa D. P., Adgey J., Brien A. O., Cleland J. G. F., Reddy D. H., Pathmanathan R. K., Fairbrother K. L., Tabidze G., Tvildiani L., Chumburidze V., Kikalishvili T., Kurashvili R., Khelashvili M., Anifantakis A., Voudris V., Tsiavou N., Toutouzas P. K., Latsios G., Richter D., Karabinos I. K., Giannopoulou G., Gotsis A., Bozia P., Savvopoulou A., Kotsis V., Bozas G., Efstathios M., Koulouris S., Vardas P. E., Marketou M., Papadopoulos G., Patsourakos N., Anastassios L., Keltai M., Ostor E., Borbola J., Liptia C., Lupkovics G., Barnabas N., Matoltsy A., Hontvari L., Sido Z., Szamosi K., Forster T., Nemes A., Szakal I., Topal L., Badics A., Engelthaler G., Nagy A., Di Sciascio G., Cecilia Scimia M., Ambrosio D., Pesola A., Robiglio L., Aloisi B., Cavallaro A., Mazzola C., Ciconte V., Giancotti D., Naccarella F., Maranga S. S., Lepera G., Sergnoli E., Zanetti M., Causarano A., Zoli V., Novo S., Coppola G., Evola G., Tanzi P., Colecchia D., Macali L., Terrana R., Zanetta M., Vegis D., Bernardi D., Tramarin R., Opasich C., Slapikas R., Gustiene O., Petrulioniene Z., Kovaite M., Georgievska-Ismail L., Poposka L., Davceva-Pavlovska J., Peovska I., Bosevski M., Deckers J. W., Jansen C. G., De Boer M. J., Van Rijn N., Brons R., Bootsma A., Van Hoogenhuyze D. C. A., Leenders C. M., Veerhoek M. J., Haan D., Baur L., Van Den Dool A., Fransen H., Nieuwlaat R., Widdershofen J. W. M. G., Broers H., Werter C., Bijl M., Koppelaar C., Ruzyllo W., Przyluski J., Kepka C., Maczynska R., Krzciuk M., Kubicka B., Dluzniewski M., Krzyzak P., Supinski W., Myczka T., Schulowska A., Zinka E., Gsecki M., Budaj A., Kokowicz P., Opolski G., Roik M., Rekosz J., Biegajlo J., Kleinrok A., Czochra W., Rynkiewicz A., Grzybowski A., Bellwon J., De Oliveira E. I., Nobrega J., Ferreira R., Baptista S., Veloso Gomes M. J., Candeias R. A. C., Rufino E., Providencia L. A., Monteiro P., Carrageta M., Bento L., Albert I., Svensson A. M., Petersson A., Torelund G., Patel H., Hage C., Lidin M., Lainscak M., Dernic J., Ambrozic J., Mocnik F. S., Glavnmik A., Fras Z., Latific-Jasnic D., Bunc M., Klemenc M., Lobnik A., Kompara G., Koval O. A., Prog R. V., Tkachenko J., Knyazkova I., Tasic I., Cardiology, Bartnik M., Ryden L., Ferrari R., Malmberg K., Pyorala K., Simoons M., Standl E., Soler-Soler J., Ohrvik J., Manini M., Bramley C., Laforest V., Taylor C., Del Gaiso S., Huber K., De Backer G., Sirakova V., Cerbak R., Thayssen P., Lehto S., Delahaye F., Kobulia B., Zeymer U., Cokkinos D., Karlocai K., Shelley E., Behar S., Maggioni A., Grabauskiene V., Deckers J., Asmussen I., Stepinska J., Goncalves L., Mareev V., Riecansky I., Kenda M.F., Lopez-Sendon J.L., Rosengren A., Buser P., Okay T., Sychov O., Fox K., Wood D., Alonso A., Boersma E., Crijns H., Gitt A., McGregor K., Mulder B., Nieminen M., Priori S., Tavazzi L., Vahanian A., Vardas P., Wijns W., Aydinkoc K., Spenka M., Wascher T.C., Sourij H., Dusko V., Radivojevic M., Goudev A.R., Tzekova M.L., Simeonov P., Pentchev V., Yotov Y., Torbova S.G., Stoyanovsky V., Stoynev E., Ostrovsky I., Moroz-Vadalazhskaya N., Cocco G., Antoniades L., Kyprianou D., Florian J., Yaghmaee S., Kvasnika J., Krizova A., Rosolova H., Petrlova B., Borivoj S., Poloczek M., Niebauer J., Drechsler K., Sechtem U., Vogelsberg H., Blank E., Breithardt G., Wedekind H., Ksoll B., Laks T., Ambos A., Tupits H., Kalinina L., Anton L., Planken U., Saad A., Andraos A.W., Shafy S.A., Metias B.D., Ibrahim M.A., Tantawi H., Lopez Bescos L., Huelmos A., Fernandez Aviles F., De La Fuente Galan L., Vinuela P.T., Velasco Rami J.A., Soriano F.R., Soledad Alcasena-Juango M., Berjon-Reyero J., Orcajo N.A., Garcia Calabozo R., Masia R., Sala J., Rohlfs I., De Diego J.J.G., Martin L.S., De El Escorial S.L., Latasa M.I., Miranda I.A., Garcia A.A., Andrade M.A., Conde A.C., Ortuno F.M., Climent V., Gonzalo F.E., Martinez V.B., Ortega J.A.R., De Alicante S.J., Galvez C.P., Rivero R.F., Belsue F.V., Rubio J.R.S., Escorihuela A.L., Gonzalez V.B., Iglesias F.C., Minguezy Enriquez De Salamanca I., Rejon F.R., Cobo A.L., Tarin N., Savolainen K., Nieminen M.S., Syvanne M., Pietila M., Mustonen J., Juntunen I., Marco J., Gilliume S., Bassand J.P., Espinosa D.P., Adgey J., Brien A.O., Cleland J.G.F., Reddy D.H., Pathmanathan R.K., Fairbrother K.L., Tabidze G., Tvildiani L., Chumburidze V., Kikalishvili T., Kurashvili R., Khelashvili M., Anifantakis A., Voudris V., Tsiavou N., Toutouzas P.K., Latsios G., Richter D., Karabinos I.K., Giannopoulou G., Gotsis A., Bozia P., Savvopoulou A., Kotsis V., Bozas G., Efstathios M., Koulouris S., Vardas P.E., Marketou M., Papadopoulos G., Patsourakos N., Anastassios L., Keltai M., Ostor E., Borbola J., Liptia C., Lupkovics G., Barnabas N., Matoltsy A., Hontvari L., Sido Z., Szamosi K., Forster T., Nemes A., Szakal I., Topal L., Badics A., Engelthaler G., Nagy A., Di Sciascio G., Cecilia Scimia M., Ambrosio D., Pesola A., Robiglio L., Aloisi B., Cavallaro A., Mazzola C., Ciconte V., Giancotti D., Naccarella F., Maranga S.S., Lepera G., Sergnoli E., Zanetti M., Causarano A., Zoli V., Novo S., Coppola G., Evola G., Tanzi P., Colecchia D., Macali L., Terrana R., Zanetta M., Vegis D., Bernardi D., Tramarin R., Opasich C., Slapikas R., Gustiene O., Petrulioniene Z., Kovaite M., Georgievska-Ismail L., Poposka L., Davceva-Pavlovska J., Peovska I., Bosevski M., Deckers J.W., Jansen C.G., De Boer M.J., Van Rijn N., Brons R., Bootsma A., Van Hoogenhuyze D.C.A., Leenders C.M., Veerhoek M.J., Haan D., Baur L., Van Den Dool A., Fransen H., Nieuwlaat R., Widdershofen J.W.M.G., Broers H., Werter C., Bijl M., Koppelaar C., Ruzyllo W., Przyluski J., Kepka C., Maczynska R., Krzciuk M., Kubicka B., Dluzniewski M., Krzyzak P., Supinski W., Myczka T., Schulowska A., Zinka E., Gsecki M., Budaj A., Kokowicz P., Opolski G., Roik M., Rekosz J., Biegajlo J., Kleinrok A., Czochra W., Rynkiewicz A., Grzybowski A., Bellwon J., De Oliveira E.I., Nobrega J., Ferreira R., Baptista S., Veloso Gomes M.J., Candeias R.A.C., Rufino E., Providencia L.A., Monteiro P., Carrageta M., Bento L., Albert I., Svensson A.M., Petersson A., Torelund G., Patel H., Hage C., Lidin M., Lainscak M., Dernic J., Ambrozic J., Mocnik F.S., Glavnmik A., Fras Z., Latific-Jasnic D., Bunc M., Klemenc M., Lobnik A., Kompara G., Koval O.A., Prog R.V., Tkachenko J., Knyazkova I., and Tasic I.

Subjects
Adult, Blood Glucose, Male, Diabetes mellitu, medicine.medical_specialty, Abnormal glucose, Diabetic Angiopathie, Oral glucose tolerance test, Coronary Artery Disease, Impaired glucose tolerance, Coronary artery disease, SDG 3 - Good Health and Well-being, Risk Factors, Diabetes mellitus, Internal medicine, Epidemiology, medicine, Humans, In patient, Aged, Glucose tolerance test, medicine.diagnostic_test, business.industry, Glucose Tolerance Test, Middle Aged, medicine.disease, Surgery, Europe, Diabetes Mellitus, Type 2, Blood sugar regulation, Female, Cardiology and Cardiovascular Medicine, business, Diabetic Angiopathies, Human
Abstract

Aim The objective behind the Euro Heart Survey on diabetes and the heart was to study the prevalence of abnormal glucose regulation in adult patients with coronary artery disease (CAD). Methods and results The survey engaged 110 centres in 25 countries recruiting 4196 patients referred to a cardiologist due to CAD out of whom 2107 were admitted on an acute basis and 2854 had an elective consultation. Patient data were collected via a web-based case record form. An oral glucose tolerance test (OGTT) was used for the characterisation of the glucose metabolism. Thirty-one per cent of the patients had diabetes. An OGTT was performed on the 1920 patients without known diabetes, of whom 923 had acute and 997 had a stable manifestation of CAD, respectively. In patients with acute CAD, 36% had impaired glucose regulation and 22% newly detected diabetes. In the stable group these proportions were 37% and 14%. Conclusion This survey demonstrates that normal glucose regulation is less common than abnormal glucose regulation in patients with CAD. OGTT easily discloses the glucometabolic state and should be a routine procedure. The knowledge of glucometabolic state among these patients should influence their future management because it has great potential to improve the outcome.

Published
2004

19. Decreased eGFR is associated with increased all-cause and cardiovascular mortality even nine years after ST-Elevated Myocardial Infarction (STEMI). The ANIN Myocardial Infarction registry

Author

Polanska-Skrzypczyk, M., primary, Karcz, M., additional, Bekta, P., additional, Kepka, C., additional, Przyluski, J., additional, Kruk, M., additional, Ksiezycka, E., additional, Ciszewski, A., additional, Ruzyllo, W., additional, and Witkowski, A., additional

Published
2013
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22. Abstracts

Author

Dunet, V., primary, Dabiri, A., additional, Allenbach, G., additional, Goyeneche Achigar, A., additional, Waeber, B., additional, Feihl, F., additional, Heinzer, R., additional, Prior, J. O., additional, Van Velzen, J. E., additional, Schuijf, J. D., additional, De Graaf, F. R., additional, De Graaf, M. A., additional, Schalij, M. J., additional, Kroft, L. J., additional, De Roos, A., additional, Jukema, J. W., additional, Van Der Wall, E. E., additional, Bax, J. J., additional, Lankinen, E., additional, Saraste, A., additional, Noponen, T., additional, Klen, R., additional, Teras, M., additional, Kokki, T., additional, Kajander, S., additional, Pietila, M., additional, Ukkonen, H., additional, Knuuti, J., additional, Pazhenkottil, A. P., additional, Nkoulou, R. N., additional, Ghadri, J. R., additional, Herzog, B. A., additional, Buechel, R. R., additional, Kuest, S. M., additional, Wolfrum, M., additional, Gaemperli, O., additional, Husmann, L., additional, Kaufmann, P. A., additional, Andreini, D., additional, Pontone, G., additional, Mushtaq, S., additional, Antonioli, L., additional, Bertella, E., additional, Formenti, A., additional, Cortinovis, S., additional, Ballerini, G., additional, Fiorentini, C., additional, Pepi, M., additional, Koh, A. S., additional, Flores, J. S., additional, Keng, F. Y. J., additional, Tan, R. S., additional, Chua, T. S. J., additional, Annoni, A. D., additional, Tamborini, G., additional, Fusari, M., additional, Bartorelli, A. L., additional, Ewe, S. H., additional, Ng, A. C. T., additional, Delgado, V., additional, Schuijf, J., additional, Van Der Kley, F., additional, Colli, A., additional, De Weger, A., additional, Marsan, N. A., additional, Yiu, K. H., additional, Ng, A. C., additional, Timmer, S. A. J., additional, Knaapen, P., additional, Germans, T., additional, Dijkmans, P. A., additional, Lubberink, M., additional, Ten Berg, J. M., additional, Ten Cate, F. J., additional, Russel, I. K., additional, Lammertsma, A. A., additional, Van Rossum, A. C., additional, Wong, Y. Y., additional, Ruiter, G., additional, Raijmakers, P., additional, Van Der Laarse, W. J., additional, Westerhof, N., additional, Vonk-Noordegraaf, A., additional, Youssef, G., additional, Leung, E., additional, Wisenberg, G., additional, Marriot, C., additional, Williams, K., additional, Etele, J., additional, Dekemp, R. A., additional, Dasilva, J., additional, Birnie, D., additional, Beanlands, R. S. B., additional, Thompson, R. C., additional, Allam, A. H., additional, Wann, L. S., additional, Nureldin, A. H., additional, Adelmaksoub, G., additional, Badr, I., additional, Sutherland, M. L., additional, Sutherland, J. D., additional, Miyamoto, M. I., additional, Thomas, G. S., additional, Harms, H. J., additional, De Haan, S., additional, Huisman, M. C., additional, Schuit, R. C., additional, Windhorst, A. D., additional, Allaart, C., additional, Einstein, A. J., additional, Khawaja, T., additional, Greer, C., additional, Chokshi, A., additional, Jones, M., additional, Schaefle, K., additional, Bhatia, K., additional, Shimbo, D., additional, Schulze, P. C., additional, Srivastava, A., additional, Chettiar, R., additional, Moody, J., additional, Weyman, C., additional, Natale, D., additional, Bruni, W., additional, Liu, Y., additional, Ficaro, E., additional, Sinusas, A. J., additional, Peix, A., additional, Batista, E., additional, Cabrera, L. O., additional, Padron, K., additional, Rodriguez, L., additional, Sainz, B., additional, Mendoza, V., additional, Carrillo, R., additional, Fernandez, Y., additional, Mena, E., additional, Naum, A., additional, Bach-Gansmo, T., additional, Kleven-Madsen, N., additional, Biermann, M., additional, Johnsen, B., additional, Aase Husby, J., additional, Rotevatn, S., additional, Nordrehaug, J. E., additional, Schaap, J., additional, Kauling, R. M., additional, Post, M. C., additional, Rensing, B. J. W. M., additional, Verzijlbergen, J. F., additional, Sanchez, J., additional, Giamouzis, G., additional, Tziolas, N., additional, Georgoulias, P., additional, Karayannis, G., additional, Chamaidi, A., additional, Zavos, N., additional, Koutrakis, K., additional, Sitafidis, G., additional, Skoularigis, J., additional, Triposkiadis, F., additional, Radovanovic, S., additional, Djokovic, A., additional, Simic, D. V., additional, Krotin, M., additional, Savic-Radojevic, A., additional, Pljesa-Ercegovac, M., additional, Zdravkovic, M., additional, Saponjski, J., additional, Jelic, S., additional, Simic, T., additional, Eckardt, R., additional, Kjeldsen, B. J., additional, Andersen, L. I., additional, Haghfelt, T., additional, Grupe, P., additional, Johansen, A., additional, Hesse, B., additional, Pena, H., additional, Cantinho, G., additional, Wilk, M., additional, Srour, Y., additional, Godinho, F., additional, Zafrir, N., additional, Gutstein, A., additional, Mats, I., additional, Battler, A., additional, Solodky, A., additional, Sari, E., additional, Singh, N., additional, Vara, A., additional, Peters, A. M., additional, De Belder, A., additional, Nair, S., additional, Ryan, N., additional, James, R., additional, Dizdarevic, S., additional, Depuey, G., additional, Friedman, M., additional, Wray, R., additional, Old, R., additional, Babla, H., additional, Chuanyong, B., additional, Maddahi, J., additional, Tragardh Johansson, E., additional, Sjostrand, K., additional, Edenbrandt, L., additional, Aguade-Bruix, S., additional, Cuberas-Borros, G., additional, Pizzi, M. N., additional, Sabate-Fernandez, M., additional, De Leon, G., additional, Garcia-Dorado, D., additional, Castell-Conesa, J., additional, Candell-Riera, J., additional, Casset-Senon, D., additional, Edjlali-Goujon, M., additional, Alison, D., additional, Delhommais, A., additional, Cosnay, P., additional, Low, C. S., additional, Notghi, A., additional, O'brien, J., additional, Tweddel, A. C., additional, Bingham, N., additional, O Neil, P., additional, Harbinson, M., additional, Lindner, O., additional, Burchert, W., additional, Schaefers, M., additional, Marcassa, C., additional, Campini, R., additional, Calza, P., additional, Zoccarato, O., additional, Kisko, A., additional, Kmec, J., additional, Babcak, M., additional, Vereb, M., additional, Vytykacova, M., additional, Cencarik, J., additional, Gazdic, P., additional, Stasko, J., additional, Abreu, A., additional, Pereira, E., additional, Oliveira, L., additional, Colarinha, P., additional, Veloso, V., additional, Enriksson, I., additional, Proenca, G., additional, Delgado, P., additional, Rosario, L., additional, Sequeira, J., additional, Kosa, I., additional, Vassanyi, I., additional, Egyed, C. S., additional, Kozmann, G. 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L., additional, Giordano, A., additional, Kapitan, M., additional, Paolino, A., additional, Nunez, M., additional, Sweeny, J., additional, Kulkarni, N., additional, Guma, K., additional, Akashi, Y., additional, Takano, M., additional, Takai, M., additional, Koh, S., additional, Miyake, F., additional, Torun, N., additional, Durmus Altun, G., additional, Altun, A., additional, Kaya, E., additional, Saglam, H., additional, Matsuoka, D. T., additional, Sanchez, A., additional, Bartolozzi, C., additional, Padua, D., additional, Ponta, G., additional, Ponte, A., additional, Carneiro, A., additional, Thom, A., additional, Ashrafi, R., additional, Garg, P., additional, Davis, G., additional, Falcao, A., additional, Costa, M., additional, Bussolini, F., additional, Meneghetti, J. A. C., additional, Tobisaka, M., additional, Correia, E., additional, Jansen, J. W., additional, Van Der Vleuten, P. A., additional, Willems, T. P., additional, Zijlstra, F., additional, Sato, M., additional, Taniguchi, K., additional, Kurabayashi, M., additional, Pop Gjorcheva, D., additional, Zdraveska-Kochovska, M., additional, Moriwaki, K., additional, Kawamura, A., additional, Watanabe, K., additional, Omura, T., additional, Sakabe, S., additional, Seko, T., additional, Kasai, A., additional, Ito, M., additional, Obana, M., additional, Akasaka, T., additional, Hruska, C., additional, Truong, D., additional, Pletta, C., additional, Collins, D., additional, Tortorelli, C., additional, Rhodes, D., additional, El-Prince, M., additional, Martinez-Moeller, A., additional, Marinelli, M., additional, Weismueller, S., additional, Hillerer, C., additional, Jensen, B., additional, Nekolla, S. G., additional, Wakabayashi, H., additional, Tsukamoto, K., additional, Baker, S. M. E. A., additional, Sirajul Haque, K. M. H. 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A., additional, Perna, F., additional, Lago, M., additional, Leo, M., additional, Pelargonio, G., additional, Bencardino, G., additional, Narducci, M. L., additional, Casella, M., additional, Bellocci, F., additional, Kirac, S., additional, Yaylali, O., additional, Serteser, M., additional, Yaylali, T., additional, Okizaki, A., additional, Urano, Y., additional, Nakayama, M., additional, Ishitoya, S., additional, Sato, J., additional, Ishikawa, Y., additional, Sakaguchi, M., additional, Nakagami, N., additional, Aburano, T., additional, Solav, S. V., additional, Bhandari, R., additional, Burrell, S., additional, Dorbala, S., additional, Bruno, I., additional, Caldarella, C., additional, Collarino, A., additional, Mattoli, M. V., additional, Stefanelli, A., additional, Cannarile, A., additional, Maggi, F., additional, Soukhov, V., additional, Bondarev, S., additional, Yalfimov, A., additional, Khan, M., additional, Priyadharshan, P. 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A., additional, Luotolahti, M., additional, Wendelin-Saarenhovi, M., additional, Sundell, J., additional, Raitakari, O., additional, Huidu, S., additional, Gadiraju, R., additional, Ghesani, M., additional, Uddin, Q., additional, Wosnitzer, B., additional, Takahashi, N., additional, Alhaj, E., additional, Legasto, A., additional, Abiri, B., additional, Elsaban, K., additional, El Khouly, T., additional, El Kammash, T., additional, Al Ghamdi, A., additional, Kyung Deok, B., additional, Bon Seung, K., additional, Sang Geun, Y., additional, Chang Min, D., additional, and Gwan Hong, M., additional

Published
2011
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23. Pilot-scale extraction of an intracellular recombinant cutinase from E-coli cell homogenate using a thermoseparating aqueous two-phase system

Author

Kepka, C., Collet, E., Persson, J., Stahl, A., Lagerstedt, T., Tjerneld, F., Veide, Andres, Kepka, C., Collet, E., Persson, J., Stahl, A., Lagerstedt, T., Tjerneld, F., and Veide, Andres

Abstract

A thermoseparating aqueous two-phase system for extraction of a recombinant cutinase fusion protein from Escherichia coli homogenate has been scaled up to pilot scale. The target protein ZZ-cutinase-(WP)(4) was produced in a fed batch process at 500 1 to a concentration of 12% of the total protein and at a cell concentration of 19.7 g l(-1). After harvest and high-pressure homogenisation a first extraction step was performed in an EO50PO50 (50% (w/w) ethylene oxide and 50% (w/w) propylene oxide) thermopolymer/amylopectin rich Waxy barley starch system. The (WP)4 tag was used for enhanced target protein partitioning to the EO50PO50 phase while the cell debris was collected in the starch phase. A second extraction step followed where the recovered EO50PO50 phase from the first step was supplemented with a non-ionic detergent (C12-18EO5) and heated to the cloud point (CP) temperature (45 degreesC). One polymer-rich liquid phase and one almost pure aqueous phase were formed. The target protein could be obtained in a water phase after the thermal phase separation at a total recovery over the extraction steps of 71% and a purification factor of 2.5. We were able to demonstrate that a disk-stack centrifugal separator could be adapted for rapid separation of both primary and thermoseparated phase systems., QC 20100525

Published
2003
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26. Comparison of different methods of ST segment resolution analysis for prediction of 1-year mortality after primary angioplasty for acute myocardial infarction.

Author

Przyluski J, Karcz M, Kalinczuk L, Kruk M, Pregowski J, Kaczmarska E, Petryka J, Bekta P, Deptuch T, Kepka C, Witkowski A, Ruzyllo W, ANIN Myocardial Infarction Registry, Przyluski, Jakub, Karcz, Maciej, Kalińczuk, Lukasz, Kruk, Mariusz, Pregowski, Jerzy, Kaczmarska, Edyta, and Petryka, Joanna

Abstract

Background: Resolution of ST segment elevation corresponds with myocardial tissue reperfusion and correlates with clinical outcome after ST elevation myocardial infarction. Simpler method evaluating the extent of maximal deviation persisting in a single ECG lead was an even stronger mortality predictor. Our aim was to evaluate and compare prognostic accuracy of different methods of ST segment elevation resolution analysis after primary percutaneous coronary intervention (PCI) in a real-life setting.Methods: Paired 12-lead ECGs were analyzed in 324 consecutive and unselected patients treated routinely with primary PCI in a single high-volume center. ST segment resolution was quantified and categorized into complete, partial, or none, upon the (1) sum of multilead ST elevations (sumSTE) and (2) sum of ST elevations plus reciprocal depressions (sumSTE+D); or into the low-, medium-, and high-risk groups by (3) the single-lead extent of maximal postprocedural ST deviation (maxSTE).Results: Complete, partial, and nonresolution groups by sumSTE constituted 39%, 40%, and 21% of patients, respective groups by sumSTE+D comprised 40%, 39%, and 21%. The low-, medium-, and high-risk groups constituted 43%, 32%, and 25%. One-year mortality rates for rising risk groups by sumSTE were 4.7%, 10.2%, and 14.5% (P = 0.049), for sumSTE+D 3.8%, 9.6%, and 17.6% (P = 0.004) and for maxSTE 5.1%, 6.7%, and 18.5% (P = 0.001), respectively. After adjustment for multiple covariates only maxSTE (high vs low-risk, odds ratio [OR] 3.10; 95% confidence interval [CI] 1.11-8.63; P = 0.030) and age (OR 1.07; 95% CI 1.02-1.11; P = 0.002) remained independent predictors of mortality.Conclusions: In unselected population risk stratifications based on the postprocedural ST resolution analysis correlate with 1-year mortality after primary PCI. However, only the single-lead ST deviation analysis allows an independent mortality prediction. [ABSTRACT FROM AUTHOR]

Published
2007

27. Polymer-glue as a life and kidney function saver: a life-threatening hemorrhage in the ruptured upper pole artery of the kidney managed by percutaneous embolization — a case report and literature review

Author

Antoniewicz Artur, Wojtkowska Izabela, Dziekiewicz Mirosław, Kępka Cezary, Kremis Elżbieta, Zapała Łukasz, Skiba Ryszard, and Stępińska Janina

Subjects
polymer-glue, percutaneous embolization, a life-threatening kidney hemorrhage, Medicine
Published
2013
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30. Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial.

Author

Kim Y, Choi AD, Telluri A, Lipkin I, Bradley AJ, Sidahmed A, Jonas R, Andreini D, Bathina R, Baggiano A, Cerci R, Choi EY, Choi JH, Choi SY, Chung N, Cole J, Doh JH, Ha SJ, Her AY, Kepka C, Kim JY, Kim JW, Kim SW, Kim W, Pontone G, Villines TC, Cho I, Danad I, Heo R, Lee SE, Lee JH, Park HB, Sung JM, Crabtree T, Earls JP, Min JK, and Chang HJ

Subjects
Humans, Female, Male, Coronary Angiography methods, Constriction, Pathologic complications, Artificial Intelligence, Tomography, X-Ray Computed, Computed Tomography Angiography methods, Referral and Consultation, Predictive Value of Tests, Coronary Artery Disease diagnostic imaging, Coronary Artery Disease complications, Coronary Stenosis complications, Atherosclerosis complications, Fractional Flow Reserve, Myocardial
Abstract

Aims: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA)., Methods: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up., Results: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively., Conclusions: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE., (© 2023 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC.)

Published
2023
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31. CT in Transcatheter-delivered Treatment of Valvular Heart Disease.

Author

Rudzinski PN, Leipsic JA, Schoepf UJ, Dudek D, Schwarz F, Andreas M, Zlahoda-Huzior A, Thilo C, Renker M, Burt JR, Emrich T, Varga-Szemes A, Amoroso NS, Steinberg DH, Pukacki P, Demkow M, Kepka C, and Bayer RR

Subjects
Aortic Valve diagnostic imaging, Cardiac Catheterization, Echocardiography, Humans, Multimodal Imaging, Tomography, X-Ray Computed methods, Aortic Valve Stenosis diagnostic imaging, Aortic Valve Stenosis surgery, Heart Valve Diseases diagnostic imaging, Heart Valve Diseases surgery, Heart Valve Prosthesis Implantation methods, Transcatheter Aortic Valve Replacement
Abstract

Minimally invasive strategies to treat valvular heart disease have emerged over the past 2 decades. The use of transcatheter aortic valve replacement in the treatment of severe aortic stenosis, for example, has recently expanded from high- to low-risk patients and became an alternative treatment for those with prohibitive surgical risk. With the increase in transcatheter strategies, multimodality imaging, including echocardiography, CT, fluoroscopy, and cardiac MRI, are used. Strategies for preprocedural imaging strategies vary depending on the targeted valve. Herein, an overview of preprocedural imaging strategies and their postprocessing approaches is provided, with a focus on CT. Transcatheter aortic valve replacement is reviewed, as well as less established minimally invasive treatments of the mitral and tricuspid valves. In addition, device-specific details and the goals of CT imaging are discussed. Future imaging developments, such as peri-procedural fusion imaging, machine learning for image processing, and mixed reality applications, are presented., (© RSNA, 2022.)

Published
2022
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32. Dynamic Myocardial Perfusion CT for the Detection of Hemodynamically Significant Coronary Artery Disease.

Author

Nous FMA, Geisler T, Kruk MBP, Alkadhi H, Kitagawa K, Vliegenthart R, Hell MM, Hausleiter J, Nguyen PK, Budde RPJ, Nikolaou K, Kepka C, Manka R, Sakuma H, Malik SB, Coenen A, Zijlstra F, Klotz E, van der Harst P, Artzner C, Dedic A, Pugliese F, Bamberg F, and Nieman K

Subjects
Computed Tomography Angiography methods, Coronary Angiography methods, Humans, Perfusion, Predictive Value of Tests, Tomography, X-Ray Computed methods, Coronary Artery Disease diagnostic imaging, Coronary Stenosis diagnostic imaging, Fractional Flow Reserve, Myocardial physiology, Myocardial Perfusion Imaging methods
Abstract

Objectives: In this international, multicenter study, using third-generation dual-source computed tomography (CT), we investigated the diagnostic performance of dynamic stress CT myocardial perfusion imaging (CT-MPI) in addition to coronary CT angiography (CTA) compared to invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR)., Background: CT-MPI combined with coronary CTA integrates coronary artery anatomy with inducible myocardial ischemia, showing promising results for the diagnosis of hemodynamically significant coronary artery disease in single-center studies., Methods: At 9 centers in Europe, Japan, and the United States, 132 patients scheduled for ICA were enrolled; 114 patients successfully completed coronary CTA, adenosine-stress dynamic CT-MPI, and ICA. Invasive FFR was performed in vessels with 25% to 90% stenosis. Data were analyzed by independent core laboratories. For the primary analysis, for each coronary artery the presence of hemodynamically significant obstruction was interpreted by coronary CTA with CT-MPI compared to coronary CTA alone, using an FFR of ≤0.80 and angiographic severity as reference. Territorial absolute myocardial blood flow (MBF) and relative MBF were compared using C-statistics., Results: ICA and FFR identified hemodynamically significant stenoses in 74 of 289 coronary vessels (26%). Coronary CTA with ≥50% stenosis demonstrated a per-vessel sensitivity, specificity, and accuracy for the detection of hemodynamically significant stenosis of 96% (95% CI: 91%-100%), 72% (95% CI: 66%-78%), and 78% (95% CI: 73%-83%), respectively. Coronary CTA with CT-MPI showed a lower sensitivity (84%; 95% CI: 75%-92%) but higher specificity (89%; 95% CI: 85%-93%) and accuracy (88%; 95% CI: 84%-92%). The areas under the receiver-operating characteristic curve of absolute MBF and relative MBF were 0.79 (95% CI: 0.71-0.86) and 0.82 (95% CI: 0.74-0.88), respectively. The median dose-length product of CT-MPI and coronary CTA were 313 mGy·cm and 138 mGy·cm, respectively., Conclusions: Dynamic CT-MPI offers incremental diagnostic value over coronary CTA alone for the identification of hemodynamically significant coronary artery disease. Generalized results from this multicenter study encourage broader consideration of dynamic CT-MPI in clinical practice. (Dynamic Stress Perfusion CT for Detection of Inducible Myocardial Ischemia [SPECIFIC]; NCT02810795)., Competing Interests: Funding Support and Author Disclosures This study was supported by unrestricted grants from Siemens Healthineers and Bayer Healthcare. Dr Nguyen’s research is supported by the National Institutes of Health (R01HL134830-01). Koen Nieman’s research is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (R01HL141712; R01HL146754). Dr Geisler has received research grants from Medtronic and Edwards Lifesciences. Dr Kitagawa has received an endowed chair position supported by Siemens Healthineers. Dr Vliegenthart has received an institutional research grant from Siemens Healthineers. Dr Hausleiter has received receiving speaker honoraria and research support from Abbott Vascular and Edwards Lifesciences; and has served as a consultant for Edwards Lifesciences. Dr Pugliese has received research support from Siemens Healthineers. Dr Budde has received institutional research support to the Erasmus MC from Siemens Healthineers. Dr Nikolauo has received research grants from Siemens Healthineers, GE Healthcare, and Bayer Healthcare; and has served as a consultant for Siemens Healthineers; and Bayer Healthcare. Dr Sakuma has received departmental research grants from FUJIFILM Toyama Chemical Co, Ltd, and Guerbet Japan KK. Dr Klotz is a retired employee of and serves as a consultant for Siemens Healthineers. Dr Bamberg has received research grants from Siemens Healthineers and Bayer Healthcare; and has served as a consultant for Siemens Healthineers, Bayer Healthcare, and Bracco. Dr Nieman has received unrestricted institutional research support from Siemens Healthineers and HeartFlow Inc; has served as a consultant for Siemens Medical Systems USA; and holds equity in Lumen Therapeutics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2022
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33. Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study.

Author

Baskaran L, Ying X, Xu Z, Al'Aref SJ, Lee BC, Lee SE, Danad I, Park HB, Bathina R, Baggiano A, Beltrama V, Cerci R, Choi EY, Choi JH, Choi SY, Cole J, Doh JH, Ha SJ, Her AY, Kepka C, Kim JY, Kim JW, Kim SW, Kim W, Lu Y, Kumar A, Heo R, Lee JH, Sung JM, Valeti U, Andreini D, Pontone G, Han D, Villines TC, Lin F, Chang HJ, Min JK, and Shaw LJ

Subjects
Aged, Coronary Artery Disease epidemiology, Coronary Artery Disease pathology, Coronary Artery Disease surgery, Female, Humans, Male, Middle Aged, Models, Statistical, Coronary Angiography, Coronary Artery Disease diagnostic imaging, Machine Learning, Myocardial Revascularization statistics & numerical data
Abstract

Background: Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization (CONSERVE) study, as well as to compare prediction of obstructive CAD to the CAD consortium clinical score (CAD2). We further perform ML analysis to gain insight into the role of imaging and clinical variables for revascularization., Methods: For prediction of obstructive CAD, the entire ICA arm of the study, comprising 719 patients was used. For revascularization, 1,028 patients were randomized to invasive coronary angiography (ICA) or coronary computed tomographic angiography (CCTA). Data was randomly split into 80% training 20% test sets for building and validation. Models used extreme gradient boosting (XGBoost)., Results: Mean age was 60.6 ± 11.5 years and 64.3% were female. For the prediction of obstructive CAD, the AUC was significantly higher for ML at 0.779 (95% CI: 0.672-0.886) than for CAD2 (0.696 [95% CI: 0.594-0.798]) (P = 0.01). BMI, age, and angina severity were the most important variables. For revascularization, the model obtained an overall area under the receiver-operation curve (AUC) of 0.958 (95% CI = 0.933-0.983). Performance did not differ whether the imaging parameters used were from ICA (AUC 0.947, 95% CI = 0.903-0.990) or CCTA (AUC 0.941, 95% CI = 0.895-0.988) (P = 0.90). The ML model obtained sensitivity and specificity of 89.2% and 92.9%, respectively. Number of vessels with ≥70% stenosis, maximum segment stenosis severity (SSS) and body mass index (BMI) were the most important variables. Exclusion of imaging variables resulted in performance deterioration, with an AUC of 0.705 (95% CI 0.614-0.795) (P <0.0001)., Conclusions: For obstructive CAD, the ML model outperformed CAD2. BMI is an important variable, although currently not included in most scores. In this ML model, imaging variables were most associated with revascularization. Imaging modality did not influence model performance. Removal of imaging variables reduced model performance., Competing Interests: The authors have read the journal's policy and the authors of this paper have the following competing interests: James K. Min is founder, paid employee, and has equity interest in Cleerly Health. However, he was an employee of the Dalio Institute of Cardiovascular Imaging at the time of the study. James K. Min also serves on the scientific advisory board of Arineta and GE Healthcare. Leslee Shaw has an equity interest in Cleerly Health. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Published
2020
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34. Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry.

Author

Tesche C, Otani K, De Cecco CN, Coenen A, De Geer J, Kruk M, Kim YH, Albrecht MH, Baumann S, Renker M, Bayer RR, Duguay TM, Litwin SE, Varga-Szemes A, Steinberg DH, Yang DH, Kepka C, Persson A, Nieman K, and Schoepf UJ

Subjects
Aged, Asia, Coronary Artery Disease physiopathology, Europe, Female, Humans, Male, Middle Aged, North America, Predictive Value of Tests, Registries, Reproducibility of Results, Severity of Illness Index, Vascular Calcification physiopathology, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease diagnostic imaging, Diagnosis, Computer-Assisted, Fractional Flow Reserve, Myocardial, Machine Learning, Radiographic Image Interpretation, Computer-Assisted, Vascular Calcification diagnostic imaging
Abstract

Objectives: This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-FFR)., Background: CT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated., Methods: A total of 482 vessels from 314 patients (age 62.3 ± 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and ≥400) on a per-vessel level with invasive FFR as the reference standard., Results: The diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC ≥400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57 to 0.85] vs. 0.85 [95% CI: 0.82 to 0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC ≥ 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001)., Conclusions: Machine-learning-based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621)., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Published
2020
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36. Selective Referral Using CCTA Versus Direct Referral for Individuals Referred to Invasive Coronary Angiography for Suspected CAD: A Randomized, Controlled, Open-Label Trial.

Author

Chang HJ, Lin FY, Gebow D, An HY, Andreini D, Bathina R, Baggiano A, Beltrama V, Cerci R, Choi EY, Choi JH, Choi SY, Chung N, Cole J, Doh JH, Ha SJ, Her AY, Kepka C, Kim JY, Kim JW, Kim SW, Kim W, Pontone G, Valeti U, Villines TC, Lu Y, Kumar A, Cho I, Danad I, Han D, Heo R, Lee SE, Lee JH, Park HB, Sung JM, Leflang D, Zullo J, Shaw LJ, and Min JK

Subjects
Aged, Asia, Coronary Artery Disease mortality, Coronary Artery Disease therapy, Europe, Female, Humans, Male, Middle Aged, North America, Predictive Value of Tests, Prognosis, Time Factors, Computed Tomography Angiography, Coronary Angiography methods, Coronary Artery Disease diagnostic imaging, Referral and Consultation
Abstract

Objectives: This study compared the safety and diagnostic yield of a selective referral strategy using coronary computed tomographic angiography (CCTA) compared with a direct referral strategy using invasive coronary angiography (ICA) as the index procedure., Background: Among patients presenting with signs and symptoms suggestive of coronary artery disease (CAD), a sizeable proportion who are referred to ICA do not have a significant, obstructive stenosis., Methods: In a multinational, randomized clinical trial of patients referred to ICA for nonemergent indications, a selective referral strategy was compared with a direct referral strategy. The primary endpoint was noninferiority with a multiplicative margin of 1.33 of composite major adverse cardiovascular events (blindly adjudicated death, myocardial infarction, unstable angina, stroke, urgent and/or emergent coronary revascularization or cardiac hospitalization) at a median follow-up of 1-year., Results: At 22 sites, 823 subjects were randomized to a selective referral and 808 to a direct referral strategy. At 1 year, selective referral met the noninferiority margin of 1.33 (p = 0.026) with a similar event rate between the randomized arms of the trial (4.6% vs. 4.6%; hazard ratio: 0.99; 95% confidence interval: 0.66 to 1.47). Following CCTA, only 23% of the selective referral arm went on to ICA, which was a rate lower than that of the direct referral strategy. Coronary revascularization occurred less often in the selective referral group compared with the direct referral to ICA (13% vs. 18%; p < 0.001). Rates of normal ICA were 24.6% in the selective referral arm compared with 61.1% in the direct referral arm of the trial (p < 0.001)., Conclusions: In stable patients with suspected CAD who are eligible for ICA, the comparable 1-year major adverse cardiovascular events rates following a selective referral and direct referral strategy suggests that both diagnostic approaches are similarly effective. In the selective referral strategy, the reduced use of ICA was associated with a greater diagnostic yield, which supported the usefulness of CCTA as an efficient and accurate method to guide decisions of ICA performance. (Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization [CONSERVE]; NCT01810198)., (Published by Elsevier Inc.)

Published
2019
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37. Giant Intrapericardial Myxoma Adjacent to the Left Main Coronary Artery.

Author

Rudziński PN, Lubiszewska B, Różański J, Michałowska I, Kruk M, Kepka C, Kryczka K, Kurowski A, Grajkowska W, Pronicki M, and Demkow M

Abstract

A 62-years-old woman was admitted to the hospital because of chronic cough, expectoration of thick mucus, hoarseness and tightness in the precordial area. Computed Tomography (CT) examination revealed the presence of a giant intrapericardial tumor with the dimensions of 80 × 38 × 32 mm. It was located anteriorly and laterally to the left atrium, posteriorly to the pulmonary trunk and the ascending aorta. This hypodense change modeled the left atrium without evidence of invasion. CT coronary angiography and 3-dimensional reconstruction were applied to enable precise planning of cardiac surgery. CT evaluation confirmed that it is possible to remove the tumor without damage to the adjacent left main coronary artery. The patient underwent cardiac surgery with sternotomy and cardiopulmonary bypass. A cohesive, smooth, vascularized tumor pedunculated to the left atrial epicardium was visualized. The location and dimensions corresponded to those determined by CT scan examination. The entire tumor was successfully dissected together with adjacent adipose and fibrous tissue. Histological evaluation revealed the presence of myxoid cells, blood vessels, degenerative changes, and microcalcifications embedded in profuse hyalinized stroma. Those histological features enabled identification of the intrapericardial tumor as a myxoma. Follow-up CT examination did not demonstrate any signs of recurrence of the myxoma. According to our knowledge, a myxoma located inside the pericardial sac has never been described before.

Published
2018
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38. Percutaneous Closure of Ventricular Septal Defect Resulting From Chest Stab Wound in an 18-Year-Old Boy.

Author

Pracon R, Grygoruk R, Konka M, Kepka C, and Demkow M

Subjects
Adolescent, Heart Injuries complications, Heart Injuries diagnosis, Heart Septal Defects, Ventricular diagnosis, Heart Septal Defects, Ventricular etiology, Heart Septum diagnostic imaging, Heart Septum surgery, Humans, Male, Printing, Three-Dimensional, Septal Occluder Device, Wounds, Stab diagnosis, Wounds, Stab surgery, Cardiac Catheterization methods, Cardiac Surgical Procedures methods, Heart Injuries surgery, Heart Septal Defects, Ventricular surgery, Heart Septum injuries, Surgery, Computer-Assisted methods, Wounds, Stab complications
Published
2018
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39. Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium.

Author

Coenen A, Kim YH, Kruk M, Tesche C, De Geer J, Kurata A, Lubbers ML, Daemen J, Itu L, Rapaka S, Sharma P, Schwemmer C, Persson A, Schoepf UJ, Kepka C, Hyun Yang D, and Nieman K

Subjects
Aged, Asia, Coronary Artery Disease physiopathology, Coronary Stenosis physiopathology, Coronary Vessels physiopathology, Europe, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Reproducibility of Results, Retrospective Studies, Severity of Illness Index, United States, Computed Tomography Angiography methods, Coronary Angiography methods, Coronary Artery Disease diagnostic imaging, Coronary Stenosis diagnostic imaging, Coronary Vessels diagnostic imaging, Deep Learning, Fractional Flow Reserve, Myocardial, Radiographic Image Interpretation, Computer-Assisted methods
Abstract

Background: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease., Methods and Results: At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent ( R =0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; P <0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%-63%) by CTA to 78% (75%-82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%-76%) by CTA to 85% (81%-89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR., Conclusions: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR., (© 2018 American Heart Association, Inc.)

Published
2018
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40. Device Thrombosis After Percutaneous Left Atrial Appendage Occlusion Is Related to Patient and Procedural Characteristics but Not to Duration of Postimplantation Dual Antiplatelet Therapy.

Author

Pracon R, Bangalore S, Dzielinska Z, Konka M, Kepka C, Kruk M, Kaczmarska-Dyrda E, Petryka-Mazurkiewicz J, Bujak S, Solecki M, Pskit A, Dabrowska A, Sieradzki B, Plonski A, Ruzyllo W, Witkowski A, and Demkow M

Subjects
Atrial Fibrillation diagnostic imaging, Atrial Fibrillation epidemiology, Atrial Fibrillation physiopathology, Drug Administration Schedule, Drug Therapy, Combination, Echocardiography, Transesophageal, Humans, Incidence, Platelet Aggregation Inhibitors adverse effects, Poland epidemiology, Prospective Studies, Prosthesis Design, Registries, Risk Assessment, Risk Factors, Thrombosis diagnosis, Thrombosis prevention & control, Time Factors, Tomography, X-Ray Computed, Treatment Outcome, Atrial Appendage diagnostic imaging, Atrial Appendage physiopathology, Atrial Fibrillation therapy, Cardiac Catheterization adverse effects, Cardiac Catheterization instrumentation, Platelet Aggregation Inhibitors administration & dosage, Septal Occluder Device, Thrombosis epidemiology
Abstract

Background: Device-related thrombus (DRT) after left atrial appendage occlusion is a worrisome finding with little knowledge about when to expect it and how to prevent it. This study sought to investigate correlates of DRT after left atrial appendage occlusion, its time of diagnosis, and particularly, association with postimplantation dual antiplatelet therapy duration., Methods and Results: Consecutive patients (n=102) after left atrial appendage occlusion with AMPLATZER Cardiac Plug/Amulet (n=59) or WATCHMAN (n=43) were included in a prospective registry (October 2011-May 2016). Follow-up was done at 1.5, 3 to 6, and 12 months postimplantation. DRT was classified as early (at 1.5 month), late (at 3-6 month), or very late (at 12-month follow-up). Postimplantation dual antiplatelet therapy was recommended for 30 to 180 days and decided independently by attending physicians. Final analysis included 99 patients, 42 (42.4%) females, with median CHA 2 DS 2 -VASc of 4.0 (interquartile range [IQR], 3.0-5.0) and median HAS-BLED score of 2.0 (IQR, 1.0-3.0). DRTs were observed in 7 (7.1%) patients: 2 (28.6%) early, 2 (28.6%) late, and 3 (42.9%) very late. When compared with patients without DRT, those with DRT presented more often with a history of prior thromboembolism (5 [71.4%] versus 28 [30.4%]; P =0.04), had lower left ventricular ejection fraction (50.0 [IQR, 35.0-55.0] versus 60.0 [IQR, 55.0-66.0]; P <0.01), and had greater proportion of patients with deep device implantation (6 [85.7%] versus 36 [39.1%]; P =0.04) and with larger devices implanted (30.0 mm [IQR, 27.0-33.0] versus 25.0 mm [IQR, 24.0-28.0]; P <0.01). Postimplantation dual antiplatelet therapy duration was not different between the 2 groups (12.4 weeks [IQR, 6.0-49.7] with DRT versus 13.0 weeks [IQR, 7.3-26.0] without DRT; P =0.77)., Conclusions: In this real-world series, DRT was observed early, late, and very late after left atrial appendage occlusion. It was related to patient and procedural characteristics but not to postimplantation dual antiplatelet therapy duration., (© 2018 American Heart Association, Inc.)

Published
2018
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41. CT Angiography for the Detection of Coronary Artery Stenoses in Patients Referred for Cardiac Valve Surgery: Systematic Review and Meta-Analysis.

Author

Opolski MP, Staruch AD, Jakubczyk M, Min JK, Gransar H, Staruch M, Witkowski A, Kepka C, Kim WK, Hamm CW, Möllmann H, and Achenbach S

Subjects
Aged, Coronary Stenosis complications, Female, Heart Valve Diseases complications, Heart Valve Diseases diagnostic imaging, Heart Valve Diseases physiopathology, Humans, Linear Models, Male, Middle Aged, Odds Ratio, Predictive Value of Tests, Reproducibility of Results, Risk Factors, Computed Tomography Angiography, Coronary Angiography methods, Coronary Stenosis diagnostic imaging, Heart Valve Diseases surgery, Multidetector Computed Tomography, Referral and Consultation
Abstract

Objectives: This study aimed to evaluate the diagnostic accuracy of coronary computed tomography angiography (CTA) for detecting coronary artery stenoses in patients with valvular heart disease undergoing valve surgery., Background: Coronary CTA is currently not routinely recommended for detecting coronary artery stenoses before cardiac valve surgery. However, recent improvements in computed tomography technology may enable the identification of the most appropriate candidates for coronary CTA before valve surgery., Methods: A systematic review was performed of PubMed, EMBASE, and the Cochrane databases for all studies that used ≥16-detector row computed tomography scanning to perform coronary CTA in patients with valvular heart disease scheduled for valve surgery and validated the results against invasive angiography. Summary diagnostic accuracies were calculated by using a bivariate random effects model, and a generalized linear mixed model was applied for heterogeneity analysis., Results: Seventeen studies analyzing 1,107 patients and 12,851 coronary segments were included. Patient-based analysis revealed a pooled sensitivity of 93% (95% confidence interval [CI]: 86 to 97), specificity of 89% (95% CI: 86 to 91), a negative likelihood ratio (LR) of 0.07 (95% CI: 0.04 to 0.16), and a positive LR of 8.44 (95% CI: 6.49 to 10.99) for coronary CTA to identify individuals with stenosis ≥50%. Specificity and positive LR were higher in patients without aortic stenosis (AS) versus those with AS (96% vs. 87% and 21.2 vs. 7.4, respectively), as well as with ≥64 detectors versus <64 detectors (90% vs. 86% and 9.5 vs. 6.9). Heterogeneity analysis revealed a significant impact of AS and the number of detectors on specificity of CTA., Conclusions: Coronary CTA using currently available technology is a reliable imaging alternative to invasive angiography with excellent sensitivity and negative LR for the detection of significant coronary stenoses in patients undergoing cardiac valve surgery. The specificity of coronary CTA may be decreased against the background of AS (Computed Tomography Angiography for the Detection of Coronary Artery Disease in Patients Referred for Cardiac Valve Surgery: A Meta-Analysis; CRD42015016213)., (Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Published
2016
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42. Computed tomography angiography for prediction of atrial fibrillation after coronary artery bypass grafting: proof of concept.

Author

Opolski MP, Staruch AD, Kusmierczyk M, Witkowski A, Kwiecinska S, Kosek M, Jastrzebski J, Pregowski J, Kruk M, Rozanski J, Demkow M, Ruzyllo W, and Kepka C

Subjects
Aged, Atrial Fibrillation physiopathology, Body Mass Index, Female, Heart Atria diagnostic imaging, Heart Atria physiopathology, Humans, Male, Middle Aged, Odds Ratio, Pericardium diagnostic imaging, Pericardium physiopathology, Postoperative Period, Risk Factors, Tomography, X-Ray Computed, Atrial Fibrillation etiology, Coronary Angiography, Coronary Artery Bypass adverse effects
Abstract

Background: Postoperative atrial fibrillation (AF) is a serious complication of coronary artery bypass grafting (CABG). There are scant data on the application of coronary computed tomography angiography (CCTA) for prediction of postoperative AF., Methods: A total of 102 patients (77 male, mean age: 64±10 years) with pre-procedural CCTA undergoing isolated CABG were enrolled. Clinical risk factors were collected. Qualitative and quantitative CCTA analysis of the atria, pulmonary veins (PV), and epicardial adipose tissue (EAT) along the left atrium (LA) was performed to determine the predictors for postoperative AF. The primary endpoint was defined as any in-hospital AF requiring treatment., Results: Postoperative AF occurred in 24% of patients. Patients with AF had higher body mass index (29.7±4.8kg/m(2) vs 27.3±3.9kg/m(2), p=0.013), larger right atrial area (25.4±5.3cm(2) vs 22.3±6.4cm(2), p=0.035), LA systolic volume (114.7±32.8ml vs 96.8±30.4ml, p=0.015), LA EAT volume (5.6±3ml vs 4±2.5ml, p=0.009), and right superior PV ostium area (3.8±1.3cm(2) vs 3±1cm(2), p=0.021) compared to non-AF patients. By multivariable analysis, only LA EAT volume [odds ratio (OR): 1.21, 95% confidence interval (CI): 1.01-1.44, p=0.036] and right superior PV ostium area (OR: 1.63, 95% CI: 1.06-2.50, p=0.026) were independent predictors of AF. The optimal cut-offs for LA EAT volume and right superior PV ostium were >3.4ml and >4.1cm(2), respectively (max. sensitivity: 83%, max. specificity: 86%)., Conclusions: Increased LA EAT and right superior PV ostial size are independently associated with AF after CABG. CCTA might be used as a noninvasive prediction tool for AF in patients undergoing CABG., (Copyright © 2014 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.)

Published
2015
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43. Coronary computed tomographic prediction rule for time-efficient guidewire crossing through chronic total occlusion: insights from the CT-RECTOR multicenter registry (Computed Tomography Registry of Chronic Total Occlusion Revascularization).

Author

Opolski MP, Achenbach S, Schuhbäck A, Rolf A, Möllmann H, Nef H, Rixe J, Renker M, Witkowski A, Kepka C, Walther C, Schlundt C, Debski A, Jakubczyk M, and Hamm CW

Subjects
Chronic Disease, Coronary Angiography, Female, Humans, Male, Middle Aged, Percutaneous Coronary Intervention instrumentation, Predictive Value of Tests, Registries, Treatment Outcome, Coronary Occlusion diagnostic imaging, Coronary Occlusion surgery, Percutaneous Coronary Intervention methods, Tomography, X-Ray Computed methods
Abstract

Objectives: This study sought to establish a coronary computed tomography angiography prediction rule for grading chronic total occlusion (CTO) difficulty for percutaneous coronary intervention (PCI)., Background: The uncertainty of procedural outcome remains the strongest barrier to PCI in CTO., Methods: Data from 4 centers involving 240 consecutive CTO lesions with pre-procedural coronary computed tomography angiography were analyzed. Successful guidewire (GW) crossing ≤30 min was set as an endpoint to eliminate operator bias. The CT-RECTOR (Computed Tomography Registry of Chronic Total Occlusion Revascularization) score was developed by assigning 1 point for each independent predictor, and then summing all points accrued. Continuous distribution of scores was used to stratify CTO into 4 difficulty groups: easy (score 0); intermediate (score 1); difficult (score 2); and very difficult (score ≥3). Discriminatory performance was tested by 10-fold cross-validation and compared with the angiographic J-CTO (Multicenter CTO Registry of Japan) score., Results: Study endpoint was achieved in 55% of cases. Multivariable analysis yielded multiple occlusions, blunt stump, severe calcification, bending, duration of CTO ≥12 months, and previously failed PCI as independent predictors for GW crossing. The probability of successful GW crossing ≤30 min for each group (from easy to very difficult) was 95%, 88%, 57%, and 22%, respectively. Areas under receiver-operator characteristic curves for the CT-RECTOR and J-CTO scores were 0.83 and 0.71, respectively (p < 0.001). Both the original model fit and 10-fold cross-validation correctly classified 77.3% of lesions., Conclusions: The CT-RECTOR score represents a simple and accurate noninvasive tool for predicting time-efficient GW crossing that may aid in grading CTO difficulty before PCI. (Computed Tomography Angiography Prediction Score for Percutaneous Revascularization for Chronic Total Occlusions [CT-RECTOR]; NCT02022878)., (Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Published
2015
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48. Epicardial adipose tissue radiodensity is independently related to coronary atherosclerosis. A multidetector computed tomography study.

Author

Pracon R, Kruk M, Kepka C, Pregowski J, Opolski MP, Dzielinska Z, Michalowska I, Chmielak Z, Demkow M, and Ruzyłło W

Subjects
Aged, Anthropometry, Calcinosis diagnostic imaging, Calcinosis epidemiology, Calcium analysis, Comorbidity, Coronary Artery Disease epidemiology, Diabetes Mellitus epidemiology, Female, Humans, Hyperlipidemias epidemiology, Hypertension epidemiology, Linear Models, Male, Middle Aged, Risk Factors, Smoking epidemiology, Adipose Tissue pathology, Coronary Artery Disease diagnostic imaging, Pericardium diagnostic imaging, Tomography, Spiral Computed
Abstract

Background: Preliminary research indicates that epicardial adipose tissue (EAT) may display both anti- and proatherosclerotic properties. Because the varying radiodensities of selected human tissues assessed by multidetector computed tomography (MDCT) has been shown to reflect differences in biological characteristics, the present study investigated the hypothesis that the proatherosclerotic properties of EAT may be linked to its radiodensity., Methods and Results: The study included 164 consecutive patients (82 females, mean age 58.8±11.1 years) with suspected coronary artery disease (CAD) undergoing MDCT coronary angiography. Coronary atherosclerosis was defined as: (1) CAD (ie, presence of at least 1 coronary stenosis ≥50%) and (2) coronary artery calcium (CAC) determined by Agatston score. EAT radiodensity was assessed by MDCT and averaged 81.00±4.64 HU (Hounsfield unit (HU) scale). Mean CAC score was 96.0±220.0. Patients with CAD (n=36) showed higher EAT radiodensity than patients without CAD (78.99±4.12 vs. -81.57±4.64 HU, P<0.01). In the multivariable model, only EAT radiodensity and age were independently associated with CAD (odds ratio (OR) 1.15, 95%confidence interval (CI) 1.04-1.28; OR 1.08, 95%CI 1.04-1.12; respectively). EAT radiodensity also correlated with CAC score (r=0.23, P<0.01). EAT radiodensity (P<0.001), age (P<0.001), sex (P=0.03), and past smoking (P<0.01) were independent correlates of CAC in the multivariable linear regression model., Conclusions: Increased EAT radiodensity is independently associated with coronary atherosclerosis, which may reflect the unfavorable, proatherosclerotic metabolic properties of more radiodense epicardial fat.

Published
2011
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49. Sodium level on admission and in-hospital outcomes of STEMI patients treated with primary angioplasty: the ANIN Myocardial Infarction Registry.

Author

Klopotowski M, Kruk M, Przyluski J, Kalinczuk L, Pregowski J, Bekta P, Malek LA, Kepka C, Ciszewski A, Chmielak Z, Demkow M, Karcz M, Witkowski A, and Ruzyllo W

Subjects
Aged, Hospital Mortality, Humans, Male, Middle Aged, Treatment Outcome, Angioplasty, Hyponatremia, Myocardial Infarction blood, Myocardial Infarction mortality, Myocardial Infarction therapy, Patient Admission, Registries, Sodium blood
Abstract

Background: Hyponatremia is a common electrolyte disorder reported to be a predictor of poor prognosis among hospitalized patients, but individuals with high levels also tend to have less favorable outcomes. This study investigated whether sodium level on admission is predictive of in-hospital outcome in patients with ST-elevation myocardial infarction (STEMI) treated with primary angioplasty., Material/methods: Included were 1858 patients admitted with STEMI for primary angioplasty. Sodium level was measured on admission and analyzed as hypo- versus normonatremia and by grouping patients into sodium quintiles. The relationships between sodium level and in-hospital mortality as well as the composite of death or heart failure were assessed., Results: Ninety-six patients had hyponatremia on admission. The hypo- and normonatremic groups were comparable with respect to baseline characteristics and in-hospital management. Hyponatremics had higher rates of in-hospital mortality (13.5% vs. 3.8%, p<0.001) composite of death and heart failure (27.8% vs. 18.4%, p=0.022). After adjustment for covariates, hyponatremia independently correlated with in-hospital mortality (HR: 3.89, 95%CI: 1.59-9.56, p=0.003) and the combined endpoint (HR: 1.73, 95%CI: 1.01-2.99, p=0.047). Patients in the lowest and highest sodium quintiles were 3.27 (95%CI: 1.34-8.02, p=0.009) and 2.65 (95%CI: 1.07-6.60, p=0.036) times more likely to die during hospitalization than those in the 2nd quintile (best survival). In the adjusted model, only patients in the lowest quintile had significantly increased risk of in-hospital death (HR: 6.35, 95%CI: 1.83-21.72, p=0.004)., Conclusions: Hyponatremia is a simple laboratory marker independently associated with increased risk of death in STEMI patients treated with primary angioplasty.

Published
2009

50. Intracoronary infusion of bone marrow-derived selected CD34+CXCR4+ cells and non-selected mononuclear cells in patients with acute STEMI and reduced left ventricular ejection fraction: results of randomized, multicentre Myocardial Regeneration by Intracoronary Infusion of Selected Population of Stem Cells in Acute Myocardial Infarction (REGENT) Trial.

Author

Tendera M, Wojakowski W, Ruzyłło W, Chojnowska L, Kepka C, Tracz W, Musiałek P, Piwowarska W, Nessler J, Buszman P, Grajek S, Breborowicz P, Majka M, and Ratajczak MZ

Subjects
Adult, Aged, Angioplasty, Balloon, Coronary, Cardiac Output, Low therapy, Coronary Angiography, Drug Administration Schedule, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Myocardial Infarction physiopathology, Prospective Studies, Research Design, Stroke Volume, Ventricular Dysfunction, Left physiopathology, Bone Marrow Transplantation methods, Myocardial Infarction therapy, Ventricular Dysfunction, Left therapy
Abstract

Aims: Comparison of intracoronary infusion of bone marrow (BM)-derived unselected mononuclear cells (UNSEL) and selected CD34(+)CXCR4(+) cells (SEL) in patients with acute myocardial infarction (AMI) and reduced <40% left ventricular ejection fraction (LVEF)., Methods and Results: Two hundred patients were randomized to intracoronary infusion of UNSEL (n = 80) or SEL (n = 80) BM cells or to the control (CTRL) group without BM cell treatment. Primary endpoint: change of LVEF and volumes measured by magnetic resonance imaging before and 6 months after the procedure. After 6 months, LVEF increased by 3% (P = 0.01) in patients treated with UNSEL, 3% in patients receiving SEL (P = 0.04) and remained unchanged in CTRL group (P = 0.73). There were no significant differences in absolute changes of LVEF between the groups. Absolute changes of left ventricular end-systolic volume and left ventricular end-diastolic volume were not significantly different in all groups. Significant increase of LVEF was observed only in patients treated with BM cells who had baseline LVEF < median (37%). Baseline LVEF < median and time from the onset of symptoms to primary percutaneous coronary intervention > or = median were predictors of LVEF improvement in patients receiving BM cells. There were no differences in major cardiovascular event (death, re-infarction, stroke, target vessel revascularization) between groups., Conclusion: In patients with AMI and impaired LVEF, treatment with BM cells does not lead to a significant improvement of LVEF or volumes. There was however a trend in favour of cell therapy in patients with most severely impaired LVEF and longer delay between the symptoms and revascularization.

Published
2009
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