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1. Everyday Discrimination in Adults with Knee Pain: The Role of Perceived Stress and Pain Catastrophizing

Author

Terry EL, Fullwood MD, Booker SQ, Cardoso JS, Sibille KT, Glover TL, Thompson KA, Addison AS, Goodin BR, Staud R, Hughes LB, Bradley LA, Redden DT, Bartley EJ, and Fillingim RB

Subjects
experiences of discrimination, perceived stress scale, pain catastrophizing, knee osteoarthritis, pain, disability, physical function, Medicine (General), R5-920
Abstract

Ellen L Terry,1 M Dottington Fullwood,2 Staja Q Booker,1 Josue S Cardoso,3 Kimberly T Sibille,2 Toni L Glover,4 Kathryn A Thompson,5 Adriana S Addison,6 Burel R Goodin,5 Roland Staud,7 Laura B Hughes,6 Laurence A Bradley,6 David T Redden,8 Emily J Bartley,9 Roger B Fillingim3,9 1Department of Biobehavioral Nursing Science, University of Florida, Gainesville, Florida, United States; 2Department of Aging and Geriatric Research, University of Florida, Gainesville, Florida, United States; 3Pain Research and Intervention Center of Excellence (PRICE), Gainesville, Florida, United States; 4Oakland University, School of Nursing, Rochester, MI, United States; 5Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, United States; 6University of Alabama at Birmingham, Division of Clinical Immunology & Rheumatology, Birmingham, Alabama, United States; 7Department of Medicine, University of Florida, Gainesville, Florida, United States; 8Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States; 9University of Florida, Community Dentistry and Behavioral Science, Gainesville, Florida, United StatesCorrespondence: Ellen L TerryDepartment of Biobehavioral Nursing Science, University of Florida, PO Box 100197, Gainesville FL 32610-0197 Tel ( +352) 273-6441Email elterry@ufl.eduPurpose: Research indicates pain-related disparities in the impact of knee osteoarthritis (OA) across both sex and ethnicity/race. While several factors likely contribute to these disparities, experiences of discrimination are associated with poor OA-related pain, disability, and functional performance. However, the mechanisms that mediate experiences of discrimination and OA-related outcomes are unclear. The current cross-sectional study examined the associations between everyday experiences of discrimination and clinical pain, disability and functional performance among non-Hispanic Black (NHB) and non-Hispanic White (NHW) persons with or at risk of knee OA and assessed the serial mediated model of perceived stress and pain catastrophizing on these relationships in women only.Patients and Methods: Participants were 188 community-dwelling adults who presented with unilateral or bilateral knee pain and screened positive for clinical knee pain. Participants completed several measures including experiences of discrimination, Perceived Stress Scale, Coping Strategies Questionnaire-Revised (CSQ-R): Pain Catastrophizing subscale, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Graded Chronic Pain Scale (GCPS), and Short Physical Performance Battery (SPPB).Results: As compared to NHW participants, NHB individuals reported experiencing significantly higher levels of discrimination (F(1, 175)=26.660, p< 0.001), greater levels of pain catastrophizing (F(1, 180)=12.919, p< 0.001), higher levels of clinical pain and disability, and lower levels of physical function (ps< 0.05). However, perceived stress was positively correlated with discrimination in the NHW group only (NHW females: r=0.40, p< 0.01; NHW males: r=0.37, p< 0.05). Further, perceived stress and pain catastrophizing mediated the relationship between discrimination and outcome variables (WOMAC pain, GCPS interference [pain disability], and SPPB function) in female participants after controlling for relevant sociodemographic variables (study site, age, race, income, and body mass index).Conclusion: These results may have implications for the treatment of perceived stress and catastrophizing as a means to reduce the negative impact of experiences of discrimination on the experience of chronic pain, particularly for women.Keywords: experiences of discrimination, Perceived Stress Scale, pain catastrophizing, knee osteoarthritis, pain, disability, physical function

Published
2020

9. Hypohidrotic Ectodermal Dysplasia and Its Manifestations in the Oral Cavity

Author

Carvalho Silva C, Moura Teles A, Cardoso Js, Faria Carvalho D, Leal F, and Lopes Cardoso I

Subjects
medicine.medical_specialty, business.industry, medicine, Hypohidrotic ectodermal dysplasia, medicine.disease, business, Oral cavity, Dermatology
Abstract

The Ectodermal Dysplasias generally present orofacial manifestations, such as skeletal discrepancies and dental alterations. Therefore, the role of a paediatric dentist in the detection and recognition of these repercussions can be crucial in early diagnosis of the disease. The oral rehabilitation of paediatric patients with this condition is extremely important, ideally, at a very early stage, yet contributing for the re-establishment of normal chewing, swallowing and phonetics functions, and, naturally, aesthetics increase. The purpose of this narrative review aims to elucidate dentists about their role in the detection, diagnosis, treatment and monitoring of the Ectodermal Dysplasia’ oral manifestations in paediatric patients, through the presentation of general physical and specific craniofacial characteristics.

Published
2021

10. MFR 2021: Masked Face Recognition Competition

Author

Yu, S, Yuen, PC, Fierrez, J, DeMarsico, M, Boutros, F, Damer, N, Kolf, JN, Raja, K, Kirchbuchner, F, Ramachandra, R, Kuijper, A, Fang, P, Zhang, C, Wang, F, Montero, D, Aginako, N, Sierra, B, Nieto, M, Erakin, ME, Demir, U, Ekenel, HK, Kataoka, A, Ichikawa, K, Kubo, S, Zhang, J, He, M, Han, D, Shan, S, Grm, K, Struc, V, Seneviratne, S, Kasthuriarachchi, N, Rasnayaka, S, Neto, PC, Sequeira, AF, Pinto, JR, Saffari, M, Cardoso, JS, Yu, S, Yuen, PC, Fierrez, J, DeMarsico, M, Boutros, F, Damer, N, Kolf, JN, Raja, K, Kirchbuchner, F, Ramachandra, R, Kuijper, A, Fang, P, Zhang, C, Wang, F, Montero, D, Aginako, N, Sierra, B, Nieto, M, Erakin, ME, Demir, U, Ekenel, HK, Kataoka, A, Ichikawa, K, Kubo, S, Zhang, J, He, M, Han, D, Shan, S, Grm, K, Struc, V, Seneviratne, S, Kasthuriarachchi, N, Rasnayaka, S, Neto, PC, Sequeira, AF, Pinto, JR, Saffari, M, and Cardoso, JS

Published
2021

13. Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

Author

Fernandes, K, Chicco, D, Cardoso, J, Fernandes, J, Cardoso, JS, Fernandes, K, Chicco, D, Cardoso, J, Fernandes, J, and Cardoso, JS

Abstract

Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.

Published
2018

14. Comparing LCZ696 With Enalapril According to Baseline Risk Using the MAGGIC and EMPHASIS-HF Risk Scores

Author

Simpson, J, Jhund, P, Cardoso, J, Martinez, F, Mosterd, A, Ramires, F, Rizkala, A, Senni, M, Squire, I, Gong, J, Lefkowitz, M, Shi, V, Desai, A, Rouleau, J, Swedberg, K, Zile, M, Mcmurray, J, Packer, M, Solomon, S, Simpson J, Jhund PS, Cardoso JS, Martinez F, Mosterd A, Ramires F, Rizkala AR, Senni M, Squire I, Gong JJ, Lefkowitz MP, Shi VC, Desai AS, Rouleau JL, Swedberg K, Zile MR, McMurray JJV, Packer M, Solomon SD, Simpson, J, Jhund, P, Cardoso, J, Martinez, F, Mosterd, A, Ramires, F, Rizkala, A, Senni, M, Squire, I, Gong, J, Lefkowitz, M, Shi, V, Desai, A, Rouleau, J, Swedberg, K, Zile, M, Mcmurray, J, Packer, M, Solomon, S, Simpson J, Jhund PS, Cardoso JS, Martinez F, Mosterd A, Ramires F, Rizkala AR, Senni M, Squire I, Gong JJ, Lefkowitz MP, Shi VC, Desai AS, Rouleau JL, Swedberg K, Zile MR, McMurray JJV, Packer M, and Solomon SD

Abstract

Background Although most patients in the PARADIGM-HF (Prospective Comparison of ARNI With ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure) trial had mild symptoms, there is a poor correlation between reported functional limitation and prognosis in heart failure. Objectives The aim of this study was to examine the spectrum of risk in PARADIGM-HF and the effect of LCZ696 across that spectrum. Methods This study analyzed rates of the primary composite outcome of cardiovascular death or heart failure hospitalization, its components, and all-cause mortality using the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) and EMPHASIS-HF (Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure) risk scores to categorize patients. The authors determined whether risk, on the basis of these scores, modified the treatment effect of LCZ696. Results The complete MAGGIC risk score was available for 8,375 of the 8,399 patients in PARADIGM-HF. The median MAGGIC score was 20 (IQR: 16 to 24). An increase of 1 point was associated with a 6% increased risk for the primary endpoint (p < 0.001) and a 7% increased risk for cardiovascular death (p < 0.001). The benefit of LCZ696 over enalapril for the primary endpoint was similar across the spectrum of risk (p = 0.159). Treating 100 patients for 2 years with LCZ696 instead of enalapril led to 7 fewer patients in the highest quintile of risk experiencing primary outcomes, compared with 3 in the lowest quintile. Analyses using the EMPHASIS-HF risk score gave similar findings. Conclusions Although most PARADIGM-HF patients had mild symptoms, many were at high risk for adverse outcomes and obtained a large absolute benefit from LCZ696, compared with enalapril, over a relatively short treatment period. LCZ696's benefit was consistent across the spectrum of risk.

Published
2015

15. Poster session 5The imaging examinationP1097Correlation between visual and quantitative assessment of left ventricle: intra- and inter-observer agreementP1099Incremental prognostic value of late gadolinium-enhanced by cardiac magnetic resonance in patients with heart failureAnatomy and physiology of the heart and great vesselsP1100Left ventricular geometry and diastolic performance in erectile dysfunction patients; a topic of differential arterial stiffness influenceAssessment of diameters, volumes and massP1101Impact of the percutaneous closure of atrial septal defect on the right heart "remodeling"P1102Left Ventricular Mass Indexation in Infants, Children and Adolescents: a Simplified Approach for the Identification of Left Ventricular Hypertrophy in Clinical PracticeP1103Impact of trabecules while quantifying cardiac magnetic resonance exams in patients with systemic right ventricleP1104Detection of subclinical atherosclerosis by carotid intima-media thickness: correlation with leukocytes telomere shorteningAssessments of haemodynamicsP1105Flow redirection towards the left ventricular outflow tract: vortex formation is not affected by variations in atrio-ventricular delayAssessment of systolic functionP1106Reproducibility and feasibility of cardiac MRI feature tracking in Fabry diseaseP1107Normal left ventricular strain values by two-dimensional strain echocardiography; result of normal (normal echocardiographic dimensions and functions in korean people) studyP1108Test-retest repeatability of global strain following st-elevation myocardial infarction - a comparison of tagging and feature trackingP1109Cardiotoxicity induced by tyrosine kinase inhibitors in patients with gastrointestinal stromal tumors (GIST)P1110Finite strain ellipses for the analysis of left ventricular principal strain directions using 3d speckle tracking echocardiographyP1111Antihypertensive therapy reduces time to peak longitudinal strainP1112Right ventricular systolic function as a marker of prognosis after inferior myocardial infarction - 5-year follow-upP1113Is artery pulmonary dilatation related with right but also early left ventricle dysfunction in pulmonary artery hypertension?P1114Right ventricular mechanics changes according to pressure overload increasing, a 2D-speckle tracking echocardiographic evaluationAssessment of diastolic functionP1115Paired comparison of left atrial strain from P-wave to P-wave and R-wave to R-waveP1116Diagnostic role of Tissue Doppler Imaging echocardiographic criteria in obese heart failure with preserved ejection fraction patientsP1117Evaluation of diastolic function of right ventricle in idiopathic pulmonary arterial hypertensionP1118Severity and predictors of diastolic dysfunction in a non-hypertensive non-ischemic cohort of Egyptian patients with documented systemic autoimmune disease; pilot reportP1119correlation between ST segment shift and cardiac diastolic function in patients with acute myocardial infarctionIschemic heart diseaseP1120Computed tomography coronary angiography verSus sTRess cArdiac magneTic rEsonance for the manaGement of sYmptomatic revascularized patients: a cost effectiveness study (STRATEGY study)P1121Utility of transmural myocardial mechanic for early infarct size prediction after primary percutaneous coronary intervention in STEMI patientsP1122Progressive Improvements of the echocardiographic deformation parameters in ST Elevation Myocardial Infarction after five years follow-upP1123Long-term prognostic value of left ventricular dyssynchrony as assessed by cardiac magnetic resonance feature-tracking imaging after a first st-segment elevation myocardial infarctionP1124Differences in mitral annulus remodeling in acute anterior ST elevation and acute inferior ST elevation myocardial infarctionP1125Reduction of microvascular injury using a novel theragnostic ultrasound strategy: a first in men feasibility and safety studyP1126Impact of focused echocardiography in clinical decision of patient presented with st elevation myocardial infarction underwent primary angioplastyHeart valve DiseasesP1127Aortic valve area calculation in aortic stenosis: a comparison among conventional and 3D-transesophageal echocardiography and computed tomographyP1128Myocardial fibrosis and microRNA-21 expression in patients with severe aortic valve stenosis and preserved ejection fraction: a 2D speckle tracking echocardiography, tissutal and plasmatic studyP1129Quantification of calcium amount in a new experimental model: a comparison between calibrated integrated backscatter of ultrasound and computed tomographyP1130Altered diffusion capacity in aortic stenosis: role of the right heartP1131Osteoprotegerin predicts all-cause mortality in calcific aortic stenosis patients with preserved left ventricle ejection fraction in long term observationP1132Mitral regurgitation as a risk factor for pulmonary hypertension in patients with aortic stenosisP1133The relationship between the level of plasma B-type natriuretic peptide and mitral stenosisP1134Aortic regurgitation, left ventricle mechanics and vascular load: a single centre 2d derived-speckle tracking studyP1135Feasibility and reproducibility issues limit the usefulness of quantitative colour Doppler parameters in the assessment of chronic aortic and mitral regurgitation severityP1136Predictors of postoperative outcome in degenerative mitral regurgitationP1137Left ventricular mechanical dyssynchrony in patients with severe mitral regurgitation of rheumatic etiology; three dimensional echocardiography studyP1138Functional mitral regurgitation and left atrial dysfunction concur in determining pulmonary hypertension and functional status in subjects with left ventricular systolic dysfunctionP11393D echocardiography allows more effective quantitative assessment of the severity of functional tricuspid regurgitation than conventional 2D/Doppler echocardiographyP1140Prosthetic valve thrombosis: still a severe disease? 10-years experience in a university hospitalP1141Validity of echocardiography in the hospital course of patients with feverP1142Do baseline 3DTEE characteristics of mitral valve apparatus predict long term result in patients undergoing percutaneous valve repair for degenerative regurgitation?P1143Influence of baseline aortic regurgitation on mitral regurgitation change after transcatheter aortic valve replacement for aortic stenosisP1144Prevalence of echocardiography detected significant valvular regurge in subclinical rheumatic carditis in assiut childrenCardiomyopathiesP1145Can we early detect left ventricular systolic dysfunction in patients with Duchenne muscular dystrophy using global longitudinal strain assessment?P1146Prevalence of isolated papillary muscle hypertrophy in young competitive athletesP1147Troponin release after exercise in patients with hypertrophic cardiomyopathy: associations with clinical and mr imaging characteristicsP1148Atrial fibrillation in hypertrophic cardiomyopathy: can we score the risk?P1149Impact of hypertrophy on multiple layer longitudinal deformation in hypertrophy cardiomyopathy and cardiac amyloidosis compared to controlsP1150Functional evaluation in hypertrophic cardiomyopathy combining cardiopulmonary exercise testing combined with exercise-echocardiographyP1151Refinement of the old diagnostic criteria of left ventricular noncompaction cardiomyopathy (LVNC) based on cardiac magnetic resonance (CMR)P1152Differences of clinical characteristics and outcomes between acute myocarditis with preserved and reduced left ventricular systolic functionP1153Value of longitudinal strain for distinguishing left ventricular non-compaction from idiopathic dilated cardiomyopathyP1154Speed of recovery of left ventricular function is not related to the prognosis of Takotsubo cardiomyopathy. A Portuguese multicentre studyP1155Predictors of in-hospital left ventricular systolic function recovery after admission with takotsubo cardiomyopathy. Portuguese multicentre studyP1156Mid-ventricular takotsubo detected by initial echocardiogram associates with recurrence of takotsubo cardiomyopathy - a portuguese multicentre studySystemic diseases and other conditionsP1157Relations between left ventricle remodelling and expression of angiotensin 2 AT2R1 geneP1158Impact of renal denervation on long-term blood pressure variability and surrogate markers of target organ damage in individuals with drug-resistant arterial hypertensionP1159Greater improvement of coronary artery function, left ventricular deformation and twisting by IL12/23 compared to TNF-a inhibition in psoriasisP1160Advanced glycation end products play a role in adverse LV remodeling following MIP1161Incidence of subclinical myocardial dysfunction in patients with systemic sclerosis and normal left ventricular systolic and diastolic functionP1162Left atrial remodeling and dysfunction occur early in patients with systemic sclerosis and normal left ventricular functionP1163Intrinsic vortex formation : a unique performance indicatorP1164P-wave morphology is unaffected by training-induced biatrial dilatation: a prospective, longitudinal study in healthy athletesP1165Usefulness of transthoracic echocardiography in diagnosis of young patients with ischemic strokeP1166Primary cardiac lymphoma: role of echocardiography in the clinical managementP1167Abnormal echocardiographic findings in cancer patients before chemotherapyMasses, tumors and sources of embolismP1168Three-dimensional transesophageal echocardiography of the left atrial appendage reduces rate of postpone electrical cardioversionP1169Detection of ventricular thrombus by cmr after reperfused st-segment elevation myocardial infarction correlated with echocardiographyP1170Clinical and transthoracic echocardiographic predictors of left atrial appendage thrombus in patients with atrial fibrillationStress echocardiographyP1171Pharmacological stress echocardiography complications: a 4-year single center experienceP1172Myocardial functional and perfusion reserve in type I diabetesP1173Feasibility of incorporating 3D Dobutamine stress echocardiography into routine clinical practiceP1174Right ventricular isovolumic acceleration at rest and during exercise in children after heart transplantP1175Right ventricular systolic and diastolic response to exercise in children after heart transplant -a bicycle exercise studyP1176Determinants of functional capacity in heart failure patients with reduced ejection fractionP1177Handgrip stress echocardiography with emotional component compared to conventional isometric exercise in coronary artery disease diagnosisP1178The relationship between resting transthoracic echocardiography and exercise capacity in patients with paroxysmal atrial fibrillationP1179Correlation between NT-proBNP and selected echocardiography parameters at rest and after exercise in patients with functional ischemic mitral regurgitation qualified for cardiosurgical treatmentReal-time three-dimensional TEEP1180Vena contracta area for severity grading in functional and degenerative mitral regurgitation: A study based on transesophageal 3D colour Doppler in 419 patientsP1181Proximal flow convergence by 3D echocardiography in the evaluation of mitral valve area in rheumatic mitral stenosisP1182Quantification of valve dimensions by transesophageal 3D echocardiography in patients with functional and degenerative mitral regurgitationTissue Doppler and speckle trackingP1183Automatic calculation of left ventricular volume changes over a cardiac cycle from echocardiography images by nonlinear dimensionality reductionP1184Effect of the mitral valve repairs on the left ventricular blood flow formationP1185Quantification of left atrial strain using cardiovascular magnetic resonance. a comparison between hypertrophic cardiomyopathy and healthy controlsP1186The role of early systolic lengthening in patients with non-ST elevation acute coronary syndrome and its relation to syntax scoreP1187Different standard two dimensional strain methods to quantity left ventricular mechanicsP1188Atrial function and electrocardiography caracteristics in sportsmen with or without paroxysmal atrial fibrillationP1189Right ventricular outflow premature contractions induce regional left ventricular dysfunctionP1190Ultrasound guided venous access for pacemaker and defibrillators. Randomized TrialP1191Atrial function analysis correlates with symptoms and quality of life of heart failure patientsP1192The use of tissue doppler echocardiography in myocardial iron overload in patients with thalassaemia majorP1193Independent association between pulse pressure and left ventricular global longitudinal strainP1194Global and regional longitudinal strain identifies the presence of coronary artery disease in patients with suspected reduction of coronary flow reserve and absence of wall motion abnormalitiesP1195Prognostic value of invasive and noninvasive parameters of right ventricular function in patients with pulmonary arterial hypertension receiving specific vasodilator therapyP1196Myocardial deformation analysis to improve arrhythmic risk stratificationP1197Quantitative assessment of regional systolic and diastolic function parameters for detecting prior transient ischemia in normokinetic segmentsP1198Left atrial function in patients with corrected tetralogy of Fallot - a three-dimensional speckle-tracking echocardiographic studyP1199Left atrial ejection force correlates with left atrial strain and volume-based functional properties as assessed by three-dimensional speckle tracking echocardiographyP1200Acute angulation of the aortic arch late after the arterial switch operation for transposition of the great arteries: impact on cardiac mechanicsP1201Circumferential deformation of the ascending thoracic aorta in hypertensive patients by three-dimensional speckle tracking echocardiographyCardiac Magnetic ResonanceP1202The incremental value of cardiac magnetic resonance on diagnosis myocardial infarction and non-obstructed coronary arteriesP1204Reference ranges of global and regional myocardial T1 values derived from MOLLI and shMOLLI at 3TComputed Tomography & Nuclear CardiologyP1205Deformation of the left atrial appendage after percutaneous closure with the Amplatzer cardiac plugP1206Prognostic impact of non-obstructive coronary artery disease on coronary computed tomographic angiography: A single-center study

Author

Colunga Blanco, S, primary, Gonzalez Matos, C, primary, Angelis, A, primary, Dinis, P G, primary, Chinali, M, primary, Toth, A, primary, Andreassi, M G, primary, Rodriguez Munoz, D, primary, Reid, A B, primary, Park, JH, primary, Shetye, A, primary, Novo, G, primary, De Marchi, S F, primary, Cikes, M, primary, Smarz, K, primary, Illatopa, V, primary, Peluso, D, primary, Wellnhofer, E, primary, De La Rosa Riestra, A, primary, Sattarzadeh Badkoubeh, R, primary, Mandour Ali, M, primary, Azoz, A, primary, Pontone, G, primary, Krljanac, G, primary, Acar, R, primary, Nucifora, G, primary, Sirtautas, A, primary, Roos, S T, primary, Qasem, M S, primary, Marini, C, primary, Fabiani, I, primary, Gillis, K, primary, Bandera, F, primary, Borowiec, A, primary, Lim, YJ, primary, Chalbia, T E, primary, Santos, M, primary, Gao, S A, primary, Zilberszac, R, primary, Farrag, AAM, primary, Palmiero, G, primary, Aruta, P, primary, De Diego Soler, O, primary, Fasano, D, primary, Tamborini, G, primary, Ancona, F, primary, Raafat, D M, primary, Marchel, M, primary, De Gregorio, C, primary, Gommans, D H F, primary, Godinho, A R, primary, Mielczarek, M, primary, Kubik, M, primary, Cho, J Y, primary, Tarando, F, primary, Lourenco Marmelo, B F, primary, Reis, L, primary, Domingues, K, primary, Krestjyaninov, MV, primary, Mesquita, J, primary, Ikonomidis, I, primary, Ferferieva, V, primary, King, GJ, primary, D'ascenzi, F, primary, Ferrera Duran, C, primary, Sormani, P, primary, Gonzalez Fernandez, O, primary, Tereshina, O, primary, Cambronero Cortinas, E, primary, Kupczynska, K, primary, Carvalho, J F, primary, Shivalkar, B, primary, Aghamohammadzadeh, R, primary, Cifra, B, primary, Kuznetsov, VA, primary, Van Zalen, JJ, primary, Kochanowski, J, primary, Goebel, B, primary, Ladeiras-Lopes, R, primary, Karvandi, M, primary, Alonso Salinas, G, primary, Unkun, T, primary, Ranjbar, S, primary, Hubert, A, primary, Enescu, OA, primary, Liccardo, M, primary, Cameli, M, primary, Ako, E, primary, Lembo, M, primary, Goffredo, C, primary, Enache, R, primary, Wdowiak-Okrojek, K, primary, Nemes, A, primary, Di Salvo, G, primary, Capotosto, L, primary, Caravaca, P, primary, Maceira Gonzalez, A M, primary, Iriart, X, primary, Jug, B, primary, Garcia Campos, A, additional, Capin Sampedro, E, additional, Corros Vicente, C, additional, Martin Fernandez, M, additional, Leon Arguero, V, additional, Fidalgo Arguelles, A, additional, Velasco Alonso, E, additional, Lopez Iglesias, F, additional, De La Hera Galarza, JM, additional, Chaparro-Munoz, M, additional, Recio-Mayoral, A, additional, Vlachopoulos, C, additional, Ioakeimidis, N, additional, Felekos, I, additional, Abdelrasoul, M, additional, Aznaouridis, K, additional, Chrysohoou, C, additional, Rousakis, G, additional, Aggeli, K, additional, Tousoulis, D, additional, Faustino, AC, additional, Paiva, L, additional, Fernandes, A, additional, Costa, M, additional, Cachulo, MC, additional, Goncalves, L, additional, Emma, F, additional, Rinelli, G, additional, Esposito, C, additional, Franceschini, A, additional, Doyon, A, additional, Raimondi, F, additional, Schaefer, F, additional, Pongiglione, G, additional, Mateucci, MC, additional, Vago, H, additional, Juhasz, C, additional, Janosa, C, additional, Oprea, V, additional, Balint, OH, additional, Temesvari, A, additional, Simor, T, additional, Kadar, K, additional, Merkely, B, additional, Bruno, R M, additional, Borghini, A, additional, Stea, F, additional, Gargani, L, additional, Mercuri, A, additional, Sicari, R, additional, Picano, E, additional, Lozano Granero, C, additional, Carbonell San Roman, A, additional, Moya Mur, JL, additional, Fernandez-Golfin, C, additional, Moreno Planas, J, additional, Fernandez Santos, S, additional, Casas Rojo, E, additional, Hernandez-Madrid, A, additional, Zamorano Gomez, JL, additional, Pearce, K, additional, Gamlin, W, additional, Miller, C, additional, Schmitt, M, additional, Seong, IW, additional, Kim, KH, additional, Kim, MJ, additional, Jung, HO, additional, Sohn, IS, additional, Park, SM, additional, Cho, GY, additional, Choi, JO, additional, Park, SW, additional, Nazir, SA, additional, Khan, JN, additional, Singh, A, additional, Kanagala, P, additional, Squire, I, additional, Mccann, GP, additional, Di Lisi, D, additional, Meschisi, MC, additional, Brunco, V, additional, Badalamenti, G, additional, Bronte, E, additional, Russo, A, additional, Novo, S, additional, Von Tscharner, M, additional, Urheim, S, additional, Aakhus, S, additional, Seiler, C, additional, Schmalholz, S, additional, Biering-Sorensen, T, additional, Cheng, S, additional, Oparil, S, additional, Izzo, J, additional, Pitt, B, additional, Solomon, SD, additional, Zaborska, B, additional, Jaxa-Chamiec, T, additional, Tysarowski, M, additional, Budaj, A, additional, Cordova, F, additional, Aguirre, O, additional, Sanabria, S, additional, Ortega, J, additional, Romeo, G, additional, Perazzolo Marra, M, additional, Tona, F, additional, Famoso, G, additional, Pigatto, E, additional, Cozzi, F, additional, Iliceto, S, additional, Badano, LP, additional, Kriatselis, C, additional, Gerds-Li, JH, additional, Kropf, M, additional, Pieske, B, additional, Graefe, M, additional, Martinez Santos, P, additional, Batlle Lopez, E, additional, Vilacosta, I, additional, Sanchez Sauce, B, additional, Espana Barrio, E, additional, Jimenez Valtierra, J, additional, Campuzano Ruiz, R, additional, Alonso Bello, J, additional, Martin Rios, MD, additional, Farrashi, M, additional, Abtahi, H, additional, Sadeghi, H, additional, Sadeghipour, P, additional, Tavoosi, A, additional, Abdel Rahman, TA, additional, Mohamed, LA, additional, Maghraby, HM, additional, Kora, IM, additional, Abdel Hameed, FR, additional, Ali, MN, additional, Al Shehri, A, additional, Youssef, A, additional, Gad, A, additional, Alsharqi, M, additional, Alsaikhan, L, additional, Andreini, D, additional, Rota, C, additional, Guglielmo, M, additional, Mushtaq, S, additional, Baggiano, A, additional, Beltrama, V, additional, Solbiati, A, additional, Guaricci, AI, additional, Pepi, M, additional, Trifunovic, D, additional, Sobic Saranovic, D, additional, Savic, L, additional, Grozdic Milojevic, I, additional, Asanin, M, additional, Srdic, M, additional, Petrovic, M, additional, Zlaic, N, additional, Mrdovic, I, additional, Dogan, C, additional, Izci, S, additional, Gecmen, C, additional, Unkun, T, additional, Cap, M, additional, Erdogan, E, additional, Onal, C, additional, Yilmaz, F, additional, Ozdemir, N, additional, Muser, D, additional, Tioni, C, additional, Zanuttini, D, additional, Morocutti, G, additional, Spedicato, L, additional, Bernardi, G, additional, Proclemer, A, additional, Pranevicius, R, additional, Zapustas, N, additional, Briedis, K, additional, Valuckiene, Z, additional, Jurkevicius, R, additional, Juffermans, LJM, additional, Enait, V, additional, Van Royen, N, additional, Van Rossum, AC, additional, Kamp, O, additional, Khalaf, HASSEN, additional, Hitham, SAKER, additional, Osama, AS, additional, Abazid, RAMI, additional, Guall, RAHIM, additional, Durdan, SHAFAT, additional, Mohammed, ZYAD, additional, Stella, S, additional, Rosa, I, additional, Ancona, F, additional, Spartera, M, additional, Italia, L, additional, Latib, A, additional, Colombo, A, additional, Margonato, A, additional, Agricola, E, additional, Scatena, C, additional, Mazzanti, C, additional, Conte, L, additional, Pugliese, N, additional, Barletta, V, additional, Bortolotti, U, additional, Naccarato, AG, additional, Di Bello, V, additional, Bala, G, additional, Roosens, B, additional, Hernot, S, additional, Remory, I, additional, Droogmans, S, additional, Cosyns, B, additional, Generati, G, additional, Labate, V, additional, Donghi, V, additional, Pellegrino, M, additional, Carbone, F, additional, Alfonzetti, E, additional, Guazzi, M, additional, Dabrowski, R, additional, Kowalik, I, additional, Firek, B, additional, Chwyczko, T, additional, Szwed, H, additional, Kawamura, A, additional, Kawano, S, additional, Zaroui, A, additional, Ben Said, R, additional, Ben Halima, M, additional, Kheder, N, additional, Farhati, A, additional, Mourali, S, additional, Mechmech, R, additional, Leite, L, additional, Martins, R, additional, Baptista, R, additional, Barbosa, A, additional, Ribeiro, N, additional, Oliveira, A, additional, Castro, G, additional, Pego, M, additional, Polte, C L, additional, Lagerstrand, K, additional, Johnsson, Å A, additional, Janulewicz, M, additional, Bech-Hanssen, O, additional, Gabriel, H, additional, Wisser, W, additional, Maurer, G, additional, Rosenhek, R, additional, El Aroussy, W, additional, Abdel Ghany, M, additional, Al Adeeb, K, additional, Ascione, L, additional, Carlomagno, G, additional, Sordelli, C, additional, Ferro, A, additional, Ascione, R, additional, Severino, S, additional, Caso, P, additional, Muraru, D, additional, Janei, C, additional, Haertel Miglioranza, M, additional, 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additional, Magaudda, L, additional, Cramer, GE, additional, Bakker, J, additional, Michels, M, additional, Dieker, HJ, additional, Fouraux, MA, additional, Marcelis, CLM, additional, Timmermans, J, additional, Brouwer, MA, additional, Kofflard, MJM, additional, Vasconcelos, M, additional, Araujo, V, additional, Almeida, P, additional, Sousa, C, additional, Macedo, F, additional, Cardoso, JS, additional, Maciel, MJ, additional, Voilliot, D, additional, Huttin, O, additional, Venner, C, additional, Olivier, A, additional, Villemin, T, additional, Deballon, R, additional, Manenti, V, additional, Juilliere, Y, additional, Selton-Suty, C, additional, Dabrowska-Kugacka, A, additional, Dorniak, K, additional, Lewicka, E, additional, Szalewska, D, additional, Kutniewska-Kubik, M, additional, Raczak, G, additional, Kim, K H, additional, Yoon, H J, additional, Park, H J, additional, Ahn, Y, additional, Jeong, M H, additional, Cho, J G, additional, Park, J C, additional, Kim, J H, additional, Galli, E, additional, Habib, G, additional, Schnell, F, additional, Lederlin, M, additional, Daubert, JC, additional, Mabo, P, additional, Donal, E, additional, Faria, R, additional, Magalhaes, P, additional, Marques, N, additional, Domingues, K, additional, Lourenco, C, additional, Almeida, AR, additional, Teles, L, additional, Picarra, B, additional, Azevedo, O, additional, Oliveira, M, additional, Marmelo, B, additional, Almeida, A, additional, Bento, D, additional, Cruz, I, additional, Reis, L, additional, Gimaev, RH, additional, Melnikova, MA, additional, Olezov, NV, additional, Ruzov, VI, additional, Goncalves, P, additional, Almeida, M S, additional, Branco, P, additional, Carvalho, M S, additional, Dores, H, additional, Gaspar, M A, additional, Sousa, H, additional, Andrade, M J, additional, Mendes, M, additional, Makavos, G, additional, Varoudi, M, additional, Papadavid, E, additional, Andreadou, I, additional, Gravanis, K, additional, Liarakos, N, additional, Pavlidis, G, additional, Rigopoulos, D, additional, Lekakis, J, additional, Deluyker, D, additional, Bito, V, additional, Punzi, L, additional, Neilan, T, additional, Coen, K, additional, Gannon, S, additional, Bennet, K, additional, Clarke, JG, additional, Solari, M, additional, Cameli, M, additional, Focardi, M, additional, Corrado, D, additional, Bonifazi, M, additional, Henein, M, additional, Mondillo, S, additional, Gomez-Escalonilla, C, additional, De Agustin, A, additional, Egido, J, additional, Islas, F, additional, Simal, P, additional, Gomez De Diego, JJ, additional, Luaces, M, additional, Macaya, C, additional, Perez De Isla, L, additional, Zancanella, M, additional, Rusconi, C, additional, Musca, F, additional, Santambrogio, G, additional, De Chiara, B, additional, Vallerio, P, additional, Cairoli, R, additional, Giannattasio, G, additional, Moreo, A, additional, Alvarez Ortega, C, additional, Mori Junco, R, additional, Caro Codon, J, additional, Meras Colunga, P, additional, Ponz De Antonio, I, additional, Lopez Fernandez, T, additional, Valbuena Lopez, S, additional, Moreno Yanguela, M, additional, Lopez-Sendon, JL, additional, Surkova, E, additional, Bonanad-Lozano, C, additional, Lopez-Lereu, MP, additional, Monmeneu-Menadas, JV, additional, Gavara, J, additional, De Dios, E, additional, Paya-Chaume, A, additional, Escribano-Alarcon, D, additional, Chorro-Gasco, FJ, additional, Bodi-Peris, V, additional, Michalski, BW, additional, Miskowiec, D, additional, Kasprzak, JD, additional, Lipiec, P, additional, Morgado, G, additional, Caldeira, D, additional, Joao, I, additional, Almeida, A R, additional, Lopes, L, additional, Fazendas, P, additional, Cotrim, C, additional, Pereira, H, additional, De Block, C, additional, Buys, D, additional, Salgado, R, additional, Vrints, C, additional, Van Gaal, L, additional, Mctear, C, additional, Irwin, RB, additional, Dragulescu, A, additional, Friedberg, M, additional, Mertens, L, additional, Krinochkin, DV, additional, Yaroslavskaya, EI, additional, Zaharova, EH, additional, Pushkarev, GS, additional, Sugihara, C, additional, Patel, NR, additional, Sulke, AN, additional, Lloyd, GW, additional, Piatkowski, R, additional, Scislo, P, additional, Grabowski, M, additional, Marchel, M, additional, Roland, H, additional, Hamadanchi, A, additional, Otto, S, additional, Jung, C, additional, Lauten, A, additional, Figulla, HC, additional, Poerner, TC, additional, Sampaio, F, additional, Fonseca, P, additional, Fontes-Carvalho, R, additional, Pinho, M, additional, Campos, AS, additional, Castro, P, additional, Fonseca, C, additional, Ribeiro, J, additional, Gama, V, additional, Heck, R, additional, Hamdanchi, A, additional, Figulla, HR, additional, Ranjbar, S, additional, Ghaffaripour Jahromi, M, additional, Hinojar, R, additional, Fernandez Golfin, C, additional, Esteban, A, additional, Pascual-Izco, M, additional, Garcia-Martin, A, additional, Jimenez-Nacher, JJ, additional, Zamorano, JL, additional, Acar, R, additional, Bakal, RB, additional, Kaymaz, C, additional, Karvandi, M, additional, Galand, V, additional, Matelot, D, additional, Leclercq, C, additional, Carre, F, additional, Suran, BC, additional, Margulescu, AD, additional, Rimbas, RC, additional, Siliste, C, additional, Vinereanu, D, additional, Nocerino, P, additional, Urso, AC, additional, Borrino, A, additional, Carbone, C, additional, Follero, P, additional, Ciardiello, C, additional, Prato, L, additional, Salzano, G, additional, Marino, F, additional, Ruspetti, A, additional, Sparla, S, additional, Di Tommaso, C, additional, Loiacono, F, additional, D'ascenzi, F, additional, Porter, J, additional, Walker, M, additional, Lo Iudice, F, additional, Esposito, R, additional, Santoro, C, additional, Cocozza, S, additional, Izzo, R, additional, De Luca, N, additional, De Simone, G, additional, Trimarco, B, additional, Galderisi, M, additional, Gervasi, F, additional, Patti, G, additional, Mega, S, additional, Bono, M, additional, Di Sciascio, G, additional, Buture, A, additional, Badea, R, additional, Platon, P, additional, Ghiorghiu, I, additional, Jurcut, R, additional, Coman, IM, additional, Popescu, BA, additional, Ginghina, C, additional, Lunetta, M, additional, Spoto, MS, additional, Lo Vi, AM, additional, Pensabene, G, additional, Carita, P, additional, Coppola, G, additional, Assennato, P, additional, Shim, A, additional, Wejner-Mik, P, additional, Havasi, K, additional, Domsik, P, additional, Kalapos, A, additional, Forster, T, additional, Piros, GA, additional, Lengyel, C, additional, Orosz, A, additional, Bulbul, Z, additional, Issa, Z, additional, Al Sehly, A, additional, Pergola, V, additional, Oufi, S, additional, Conde, Y, additional, Cimino, E, additional, Rinaldi, E, additional, Ashurov, R, additional, Ricci, S, additional, Pergolini, M, additional, Vitarelli, A, additional, Lujan Valencia, JE, additional, Chaparro, M, additional, Garcia-Guerrero, A, additional, Cristo Ropero, MJ, additional, Izquierdo Bajo, A, additional, Madrona, L, additional, Monmeneu, JV, additional, Igual, B, additional, Lopez Lereu, P, additional, Garcia, MP, additional, Selmi, W, additional, Jalal, Z, additional, Thambo, JB, additional, Kosuta, D, additional, and Fras, Z, additional

Published
2015
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25. Poster session 5

Author

Colunga Blanco, S, Garcia Campos, A, Capin Sampedro, E, Corros Vicente, C, Martin Fernandez, M, Leon Arguero, V, Fidalgo Arguelles, A, Velasco Alonso, E, Lopez Iglesias, F, De La Hera Galarza, JM, Gonzalez Matos, C, Chaparro-Munoz, M, Recio-Mayoral, A, Angelis, A, Vlachopoulos, C, Ioakeimidis, N, Felekos, I, Abdelrasoul, M, Aznaouridis, K, Chrysohoou, C, Rousakis, G, Aggeli, K, Tousoulis, D, Dinis, P G, Faustino, AC, Paiva, L, Fernandes, A, Costa, M, Cachulo, MC, Goncalves, L, Chinali, M, Emma, F, Rinelli, G, Esposito, C, Franceschini, A, Doyon, A, Raimondi, F, Schaefer, F, Pongiglione, G, Mateucci, MC, Toth, A, Vago, H, Juhasz, C, Janosa, C, Oprea, V, Balint, OH, Temesvari, A, Simor, T, Kadar, K, Merkely, B, Andreassi, M G, Bruno, R M, Borghini, A, Stea, F, Gargani, L, Mercuri, A, Sicari, R, Picano, E, Rodriguez Munoz, D, Lozano Granero, C, Carbonell San Roman, A, Moya Mur, JL, Fernandez-Golfin, C, Moreno Planas, J, Fernandez Santos, S, Casas Rojo, E, Hernandez-Madrid, A, Zamorano Gomez, JL, Reid, A B, Pearce, K, Gamlin, W, Miller, C, Schmitt, M, Park, JH, Seong, IW, Kim, KH, Kim, MJ, Jung, HO, Sohn, IS, Park, SM, Cho, GY, Choi, JO, Park, SW, study, NORMAL, Shetye, A, Nazir, SA, Khan, JN, Singh, A, Kanagala, P, Squire, I, Mccann, GP, Novo, G, Di Lisi, D, Meschisi, MC, Brunco, V, Badalamenti, G, Bronte, E, Russo, A, Novo, S, De Marchi, S F, Von Tscharner, M, Urheim, S, Aakhus, S, Seiler, C, Schmalholz, S, Cikes, M, Biering-Sorensen, T, Cheng, S, Oparil, S, Izzo, J, Pitt, B, Solomon, SD, Smarz, K, Zaborska, B, Jaxa-Chamiec, T, Tysarowski, M, Budaj, A, Illatopa, V, Cordova, F, Aguirre, O, Sanabria, S, Ortega, J, Peluso, D, Romeo, G, Perazzolo Marra, M, Tona, F, Famoso, G, Pigatto, E, Cozzi, F, Iliceto, S, Badano, LP, Wellnhofer, E, Kriatselis, C, Gerds-Li, JH, Kropf, M, Pieske, B, Graefe, M, De La Rosa Riestra, A, Martinez Santos, P, Batlle Lopez, E, Vilacosta, I, Sanchez Sauce, B, Espana Barrio, E, Jimenez Valtierra, J, Campuzano Ruiz, R, Alonso Bello, J, Martin Rios, MD, Sattarzadeh Badkoubeh, R, Farrashi, M, Abtahi, H, Sadeghi, H, Sadeghipour, P, Tavoosi, A, Mandour Ali, M, Abdel Rahman, TA, Mohamed, LA, Maghraby, HM, Kora, IM, Abdel Hameed, FR, Ali, MN, Azoz, A, Al Shehri, A, Youssef, A, Gad, A, Alsharqi, M, Alsaikhan, L, Pontone, G, Andreini, D, Rota, C, Guglielmo, M, Mushtaq, S, Baggiano, A, Beltrama, V, Solbiati, A, Guaricci, AI, Pepi, M, Krljanac, G, Trifunovic, D, Sobic Saranovic, D, Savic, L, Grozdic Milojevic, I, Asanin, M, Srdic, M, Petrovic, M, Zlaic, N, Mrdovic, I, Acar, R, Dogan, C, Izci, S, Gecmen, C, Unkun, T, Cap, M, Erdogan, E, Onal, C, Yilmaz, F, Ozdemir, N, Nucifora, G, Muser, D, Tioni, C, Zanuttini, D, Morocutti, G, Spedicato, L, Bernardi, G, Proclemer, A, Sirtautas, A, Pranevicius, R, Zapustas, N, Briedis, K, Valuckiene, Z, Jurkevicius, R, Roos, S T, Juffermans, LJM, Enait, V, Van Royen, N, Van Rossum, AC, Kamp, O, Qasem, M S, Khalaf, HASSEN, Hitham, SAKER, Osama, AS, Abazid, RAMI, Guall, RAHIM, Durdan, SHAFAT, Mohammed, ZYAD, Marini, C, Stella, S, Rosa, I, Ancona, F, Spartera, M, Italia, L, Latib, A, Colombo, A, Margonato, A, Agricola, E, Fabiani, I, Scatena, C, Mazzanti, C, Conte, L, Pugliese, N, Barletta, V, Bortolotti, U, Naccarato, AG, Di Bello, V, Gillis, K, Bala, G, Roosens, B, Hernot, S, Remory, I, Droogmans, S, Cosyns, B, Bandera, F, Generati, G, Labate, V, Donghi, V, Pellegrino, M, Carbone, F, Alfonzetti, E, Guazzi, M, Borowiec, A, Dabrowski, R, Kowalik, I, Firek, B, Chwyczko, T, Szwed, H, Lim, YJ, Kawamura, A, Kawano, S, Chalbia, T E, Zaroui, A, Ben Said, R, Ben Halima, M, Kheder, N, Farhati, A, Mourali, S, Mechmech, R, Santos, M, Leite, L, Martins, R, Baptista, R, Barbosa, A, Ribeiro, N, Oliveira, A, Castro, G, Pego, M, Gao, S A, Polte, C L, Lagerstrand, K, Johnsson, A A, Janulewicz, M, Bech-Hanssen, O, Zilberszac, R, Gabriel, H, Wisser, W, Maurer, G, Rosenhek, R, Farrag, AAM, El Aroussy, W, Abdel Ghany, M, Al Adeeb, K, Palmiero, G, Ascione, L, Carlomagno, G, Sordelli, C, Ferro, A, Ascione, R, Severino, S, Caso, P, Aruta, P, Muraru, D, Janei, C, Haertel Miglioranza, M, Cavalli, G, Romeo, G, Peluso, D, Cucchini, U, Iliceto, S, Badano, L, De Diego Soler, O, Armario Bel, X, Garcia-Garcia, C, Ferrer Sistach, E, Rueda Sobella, F, Oliveras Vila, T, Labata Salvador, C, Serra Flores, J, Lopez-Ayerbe, J, Bayes-Genis, A, Fasano, D, Conte, E, Gonella, A, Morena, L, Civelli, D, Losardo, L, Margaria, F, Riva, L, Tanga, M, Tamborini, G, Carminati, C, Muratori, M, Gripari, P, Ghulam Ali, S, Fusini, L, Vignati, C, Bartorelli, AL, Alamanni, F, Pepi, M, Ancona, F, Rosa, I, Stella, S, Marini, C, Spartera, M, Latib, A, Montorfano, M, Colombo, A, Margonato, A, Agricola, E, Raafat, D M, Ismaiel, A, Ali, N, Amry, S, Medicine, Faculty of, University, Assiut, Assiut, Egypt, Department, Pediatric, Marchel, M, Serafin, A, Kochanowski, J, Filipiak, KJ, Opolski, G, De Gregorio, C, Speranza, G, Ando', G, Magaudda, L, Gommans, D H F, Cramer, GE, Bakker, J, Michels, M, Dieker, HJ, Fouraux, MA, Marcelis, CLM, Timmermans, J, Brouwer, MA, Kofflard, MJM, Godinho, A R, Vasconcelos, M, Araujo, V, Almeida, P, Sousa, C, Macedo, F, Cardoso, JS, Maciel, MJ, Mielczarek, M, Voilliot, D, Huttin, O, Venner, C, Olivier, A, Villemin, T, Deballon, R, Manenti, V, Juilliere, Y, Selton-Suty, C, Bandera, F, Generati, G, Pellegrino, M, Labate, V, Carbone, F, Alfonzetti, E, Guazzi, M, Kubik, M, Dabrowska-Kugacka, A, Dorniak, K, Lewicka, E, Szalewska, D, Kutniewska-Kubik, M, Raczak, G, Cho, J Y, Kim, K H, Yoon, H J, Park, H J, Ahn, Y, Jeong, M H, Cho, J G, Park, J C, Kim, J H, Tarando, F, Galli, E, Habib, G, Schnell, F, Lederlin, M, Daubert, JC, Mabo, P, Donal, E, Lourenco Marmelo, B F, Faria, R, Magalhaes, P, Marques, N, Domingues, K, Lourenco, C, Almeida, AR, Teles, L, Picarra, B, Azevedo, O, SUNSHINE, Grupo, Reis, L, Lourenco, C, Oliveira, M, Magalhaes, P, Domingues, K, Marmelo, B, Almeida, A, Picarra, B, Faria, R, Marques, N, Sunshine, Domingues, K, Bento, D, Lourenco, C, Magalhaes, P, Cruz, I, Marmelo, B, Reis, L, Picarra, B, Faria, R, Azevedo, O, group, Sunshine, Krestjyaninov, MV, Gimaev, RH, Melnikova, MA, Olezov, NV, Ruzov, VI, Mesquita, J, Goncalves, P, Almeida, M S, Branco, P, Carvalho, M S, Dores, H, Gaspar, M A, Sousa, H, Andrade, M J, Mendes, M, Ikonomidis, I, Makavos, G, Varoudi, M, Papadavid, E, Andreadou, I, Gravanis, K, Liarakos, N, Pavlidis, G, Rigopoulos, D, Lekakis, J, Ferferieva, V, Deluyker, D, Bito, V, Peluso, D, Pigatto, E, Romeo, G, Muraru, D, Cozzi, F, Punzi, L, Iliceto, S, Badano, LP, Peluso, D, Pigatto, E, Romeo, G, Muraru, D, Cozzi, F, Iliceto, S, Badano, LP, King, GJ, Neilan, T, Coen, K, Gannon, S, Bennet, K, Clarke, JG, D'ascenzi, F, Solari, M, Cameli, M, Focardi, M, Corrado, D, Bonifazi, M, Henein, M, Mondillo, S, Ferrera Duran, C, Gomez-Escalonilla, C, De Agustin, A, Egido, J, Islas, F, Simal, P, Gomez De Diego, JJ, Luaces, M, Macaya, C, Perez De Isla, L, Sormani, P, Zancanella, M, Rusconi, C, Musca, F, Santambrogio, G, De Chiara, B, Vallerio, P, Cairoli, R, Giannattasio, G, Moreo, A, Gonzalez Fernandez, O, Alvarez Ortega, C, Mori Junco, R, Caro Codon, J, Meras Colunga, P, Ponz De Antonio, I, Lopez Fernandez, T, Valbuena Lopez, S, Moreno Yanguela, M, Lopez-Sendon, JL, Tereshina, O, Surkova, E, Cambronero Cortinas, E, Bonanad-Lozano, C, Lopez-Lereu, MP, Monmeneu-Menadas, JV, Gavara, J, De Dios, E, Paya-Chaume, A, Escribano-Alarcon, D, Chorro-Gasco, FJ, Bodi-Peris, V, Kupczynska, K, Michalski, BW, Miskowiec, D, Kasprzak, JD, Lipiec, P, Carvalho, J F, Morgado, G, Caldeira, D, Cruz, I, Joao, I, Almeida, A R, Lopes, L, Fazendas, P, Cotrim, C, Pereira, H, Shivalkar, B, De Block, C, Buys, D, Salgado, R, Vrints, C, Van Gaal, L, Aghamohammadzadeh, R, Mctear, C, Irwin, RB, Cifra, B, Dragulescu, A, Friedberg, M, Mertens, L, Cifra, B, Dragulescu, A, Friedberg, M, Mertens, L, Bandera, F, Carbone, F, Generati, G, Pellegrino, M, Labate, V, Alfonzetti, E, Guazzi, M, Kuznetsov, VA, Krinochkin, DV, Yaroslavskaya, EI, Zaharova, EH, Pushkarev, GS, Van Zalen, JJ, Sugihara, C, Patel, NR, Sulke, AN, Lloyd, GW, Kochanowski, J, Piatkowski, R, Scislo, P, Grabowski, M, Marchel, M, Opolski, G, Goebel, B, Roland, H, Hamadanchi, A, Otto, S, Jung, C, Lauten, A, Figulla, HC, Poerner, TC, Ladeiras-Lopes, R, Sampaio, F, Fonseca, P, Fontes-Carvalho, R, Pinho, M, Campos, AS, Castro, P, Fonseca, C, Ribeiro, J, Gama, V, Goebel, B, Heck, R, Hamdanchi, A, Otto, S, Jung, C, Lauten, A, Figulla, HR, Poerner, TC, Karvandi, M, Ranjbar, S, Ghaffaripour Jahromi, M, Karvandi, M, Ranjbar, S, Alonso Salinas, G, Hinojar, R, Fernandez Golfin, C, Esteban, A, Pascual-Izco, M, Garcia-Martin, A, Casas Rojo, E, Jimenez-Nacher, JJ, Zamorano, JL, Unkun, T, Gecmen, C, Cap, M, Izci, S, Erdogan, E, Onal, C, Acar, R, Bakal, RB, Kaymaz, C, Ozdemir, N, Ranjbar, S, Karvandi, M, Ghaffaripour Jahromi, M, Hubert, A, Galand, V, Schnell, F, Matelot, D, Martins, R, Leclercq, C, Carre, F, Enescu, OA, Suran, BC, Margulescu, AD, Rimbas, RC, Siliste, C, Vinereanu, D, Liccardo, M, Nocerino, P, Urso, AC, Borrino, A, Carbone, C, Follero, P, Ciardiello, C, Prato, L, Salzano, G, Cameli, M, Marino, F, Ruspetti, A, Sparla, S, Di Tommaso, C, Loiacono, F, Focardi, M, D'ascenzi, F, Henein, M, Mondillo, S, Ako, E, Porter, J, Walker, M, Lembo, M, Lo Iudice, F, Esposito, R, Santoro, C, Cocozza, S, Izzo, R, De Luca, N, De Simone, G, Trimarco, B, Galderisi, M, Goffredo, C, Gervasi, F, Patti, G, Mega, S, Bono, M, Di Sciascio, G, Enache, R, Buture, A, Badea, R, Platon, P, Ghiorghiu, I, Jurcut, R, Coman, IM, Popescu, BA, Ginghina, C, Novo, G, Lunetta, M, Spoto, MS, Lo Vi, AM, Pensabene, G, Meschisi, MC, Carita, P, Coppola, G, Novo, S, Assennato, P, Wdowiak-Okrojek, K, Shim, A, Wejner-Mik, P, Kasprzak, JD, Lipiec, P, Nemes, A, Havasi, K, Domsik, P, Kalapos, A, Forster, T, Nemes, A, Piros, GA, Domsik, P, Kalapos, A, Lengyel, C, Orosz, A, Forster, T, Di Salvo, G, Bulbul, Z, Issa, Z, Al Sehly, A, Pergola, V, Oufi, S, Capotosto, L, Conde, Y, Cimino, E, Rinaldi, E, Ashurov, R, Ricci, S, Pergolini, M, Vitarelli, A, Caravaca, P, Lujan Valencia, JE, Chaparro, M, Garcia-Guerrero, A, Cristo Ropero, MJ, Izquierdo Bajo, A, Madrona, L, Recio-Mayoral, A, Maceira Gonzalez, A M, Monmeneu, JV, Igual, B, Lopez Lereu, P, Garcia, MP, Iriart, X, Selmi, W, Jalal, Z, Thambo, JB, Jug, B, Kosuta, D, and Fras, Z

Abstract

Background: Handheld ultrasound devices allow for a bedside screening although quantitative parameters are not easily obtained. We aim to assess the reliability of visual qualitative evaluation of left ventricle (LV) compared with standard quantitative evaluation with 2D transthoracic echocardiography (TTE). Methods: Two cardiologists have reviewed 135 consecutive standard TTE examinations. Both observers visually assessed LV size, hypertrophy (LVH) and ejection fraction (EF). LV diameter, volume, wall thickness and EF (Teichholz and Simpson) were also measured by both observers. Visual and quantitative agreement and inter and intraobserver variability were calculated. Results: Image quality allowed for evaluation of 130 examinations. Visually assessed EF compared with Simpson had better consistency (Intraclass correlation coefficient [ICC] 0,91 IC95% 0,88-0,94) than with Teichholz (ICC 0,75 IC95% 0,66-0,82). We have also observed good interobserver agreement regarding visually assessed EF (ICC 0,81 IC95% 0,71-0,87) and Simpson EF (ICC 0,80 IC95% 0,70-0,89) as well as good intraobserver agreement (visual EF: ICC 0,81 IC95% 0,74-0,86; Simpson: ICC 0,89 IC95% 0,84-0,93). Regarding LVH we found moderate agreement between visual and quantitative assessment (weighted Kappa [wK] 0,44 (IC95% 0,32-0,56)), moderate interobserver agreement for quantitative assessment (ICC 0,59 IC95% 0,44-0,71) and poor interobserver agreement for visual assessment (wK 0,19 IC95% 0,08-0,30). Intraobserver variability regarding LVH visual estimation was moderate (wK 0,40 IC95% 0,29-0,52) and regarding LVH quantification was good (ICC 0,78 IC95% 0,70-0,84). LVH was visually overestimated in 25% of examinations. Regarding LV size, we found poor agreement between visual assessment and its quantification with end-diastolic diameter (wK 0,22 IC95% 0,06-0,39) and moderate agreement between visual assessment and end-systolic LV volume (wK 0,62 IC95% 0,47-0,77). Interobserver agreement regarding quantitative volume assessment was good (ICC 0,90 IC95% 0,85-0,94) and regarding visual assessment was moderate (wK 0,43 IC95% 0,26-0,70). We found good intraobserver variability of volume measurement (wK 0,64 IC95% 0,50-0,78) and of visual size assessment (ICC 0,96 IC95% 0,94-0,97). Conclusions: Visual LVEF assessment is consistent with quantitative assessment and should be regarded as a reliable parameter that can be obtained from bedside examination with a handheld device. Visual assessment of LV size and wall thickness is less reliable than its quantification and should be confirmed with standard measurements.

Published
2015
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26. Poster session Thursday 12 December - PM: 12/12/2013, 14:00-18:00 * Location: Poster area

Author

Garcia Martin, A, Fernandez Golfin, C, Salido Tahoces, L, Fernandez Santos, S, Jimenez Nacher, JJ, Moya Mur, JL, Velasco Valdazo, E, Hernandez Antolin, R, Zamorano Gomez, JL, Veronesi, F, Corsi, C, Caiani, EG, Lamberti, C, Tsang, W, Holmgren, C, Guo, X, Bateman, M, Iaizzo, P, Vannier, M, Lang, RM, Patel, AR, Adamayn, KG, Tumasyan, L R, Chilingaryan, AL, Nasr, G, Eleraki, A, Farouk, N, Axelsson, A, Langhoff, L, Jensen, MK, Vejlstrup, N, Iversen, K, Bundgaard, H, Watanabe, T, Iwai-Takano, M, Attenhofer Jost, C H, Pfyffer, M, Seifert, B, Scharf, C, Candinas, R, Medeiros-Domingo, A, Chin, J-Y, Yoon, HJ, Vollbon, W, Singbal, Y, Rhodes, K, Wahi, S, Katova, T M, Simova, I I, Hristova, K, Kostova, V, Pauncheva, B, Bircan, A, Sade, LE, Eroglu, S, Pirat, B, Okyay, K, Bal, U, Muderrisoglu, H, Heggemann, F, Buggisch, H, Welzel, G, Doesch, C, Hansmann, J, Schoenberg, S, Borggrefe, M, Wenz, F, Papavassiliu, T, Lohr, F, Roussin, I, Drakopoulou, M, Rosen, S, Sharma, R, Prasad, S, Lyon, AR, Carpenter, JP, Senior, R, Breithardt, O-A, Razavi, H, Arya, A, Nabutovsky, Y, Ryu, K, Gaspar, T, Kosiuk, J, Eitel, C, Hindricks, G, Piorkowski, C, Pires, S, Nunes, A, Cortez-Dias, N, Belo, A, Zimbarra Cabrita, I, Sousa, C, Pinto, F, Baron, T, Johansson, K, Flachskampf, FA, Christersson, C, Pires, S, Cortez-Dias, N, Nunes, A, Belo, A, Zimbarra Cabrita, I, Sousa, C, Pinto, F, Santoro, A, Federico Alvino, FA, Giovanni Antonelli, GA, Raffaella De Vito, RDV, Roberta Molle, RM, Sergio Mondillo, SM, Gustafsson, M, Alehagen, U, Johansson, P, Tsukishiro, Y, Onishi, T, Chimura, M, Yamada, S, Taniguchi, Y, Yasaka, Y, Kawai, H, Souza, J R M, Zacharias, L G T, Pithon, K R, Ozahata, T M, Cliquet, A JR, Blotta, M H, Nadruz, W JR, Fabiani, I, Conte, L, Cuono, C, Liga, R, Giannini, C, Barletta, V, Nardi, C, Delle Donne, MG, Palagi, C, Di Bello, V, Glaveckaite, S, Valeviciene, N, Palionis, D, Laucevicius, A, Hristova, K, Bogdanova, V, Ferferieva, V, Shiue, I, Castellon, X, Boles, U, Rakhit, R, Shiu, M F, Gilbert, T, Papachristidis, A, Henein, M Y, Westholm, C, Johnson, J, Jernberg, T, Winter, R, Ghosh Dastidar, A, Augustine, D, Cengarle, M, Mcalindon, E, Bucciarelli-Ducci, C, Nightingale, A, Onishi, T, Watanabe, T, Fujita, M, Mizukami, Y, Sakata, Y, Nakatani, S, Nanto, S, Uematsu, M, Saraste, A, Luotolahti, M, Varis, A, Vasankari, T, Tunturi, S, Taittonen, M, Rautakorpi, P, Airaksinen, J, Ukkonen, H, Knuuti, J, Boshchenko, A, Vrublevsky, A, Karpov, R, Yoshikawa, H, Suzuki, M, Hashimoto, G, Kusunose, Y, Otsuka, T, Nakamura, M, Sugi, K, Rosner, SJ, Orban, M, Lesevic, H, Karl, M, Hadamitzky, M, Sonne, C, Panaro, A, Martinez, F, Huguet, M, Moral, S, Palet, J, Oller, G, Cuso, I, Jornet, A, Rodriguez Palomares, J, Evangelista, A, Stoebe, S, Tarr, A, Pfeiffer, D, Hagendorff, A, Gilmanov, DSH, Baroni, MB, Cerone, EC, Galli, EG, Berti, SB, Glauber, MG, Soesanto, A, Yuniadi, Y, Mansyur, M, Kusmana, D, Venkateshvaran, A, Dash, P K, Sola, S, Govind, S C, Shahgaldi, K, Winter, R, Brodin, L A, Manouras, A, Dokainish, H, Sadreddini, M, Nieuwlaat, R, Lonn, E, Healey, J, Nguyen, V, Cimadevilla, C, Dreyfus, J, Codogno, I, Vahanian, A, Messika-Zeitoun, D, Lim, Y-J, Kawamura, A, Kawano, S, Polte, CL, Gao, S, Lagerstrand, KM, Cederbom, U, Bech-Hanssen, O, Baum, J, Beeres, F, Van Hall, S, Boering, YC, Zeus, T, Kehmeier, ES, Kelm, M, Balzer, JC, Della Mattia, A, Pinamonti, B, Abate, E, Nicolosi, GL, Proclemer, A, Bassetti, M, Luzzati, R, Sinagra, G, Hlubocka, Z, Jiratova, K, Dostalova, G, Hlubocky, J, Dohnalova, A, Linhart, A, Palecek, T, Sonne, C, Lesevic, H, Karl, M, Rosner, S, Hadamitzky, M, Ott, I, Malev, E, Reeva, S, Zemtsovsky, E, Igual Munoz, B, Alonso Fernandez Pau, PAF, Miro Palau Vicente, VMP, Maceira Gonzalez Alicia, AMG, Estornell Erill, JEE, Andres La Huerta, AALH, Donate Bertolin, LDB, Valera Martinez, FVM, Salvador Sanz Antonio, ASS, Montero Argudo Anastasio, AMA, Nemes, A, Kalapos, A, Domsik, P, Chadaide, S, Sepp, R, Forster, T, Onaindia, JJ, Arana, X, Cacicedo, A, Velasco, S, Rodriguez, I, Capelastegui, A, Sadaba, M, Gonzalez, J, Salcedo, A, Laraudogoitia, E, Archontakis, S, Gatzoulis, K, Vlasseros, I, Arsenos, P, Tsiachris, D, Vouliotis, A, Sideris, S, Karistinos, G, Kalikazaros, I, Stefanadis, C, Ancona, R, Comenale Pinto, S, Caso, P, Coppola, MG, Arenga, F, Cavallaro, C, Vecchione, F, Donofrio, A, Calabro, R, Correia, C E, Moreira, D, Cabral, C, Santos, JO, Cardoso, JS, Igual Munoz, B, Maceira Gonzalez, AMG, Estornell Erill Jordi, JEE, Jimenez Carreno, RJC, Arnau Vives, MAV, Monmeneu Menadas, JVMM, Domingo-Valero, DDV, Sanchez Fernandez, ESF, Montero Argudo Anastasio, AMA, Zorio Grima, EZG, Cincin, A, Tigen, K, Karaahmet, T, Dundar, C, Sunbul, M, Guler, A, Bulut, M, Basaran, Y, Mordi, I, Carrick, D, Berry, C, Tzemos, N, Cruz, I, Ferreira, A, Rocha Lopes, L, Joao, I, Almeida, AR, Fazendas, P, Cotrim, C, Pereira, H, Ochoa, J P, Fernandez, A, Filipuzzi, JM, Casabe, JH, Salmo, JF, Vaisbuj, F, Ganum, G, Di Nunzio, HJ, Veron, LF, Guevara, E, Salemi, VMC, Nerbass, FB, Portilho, N, Ferreira Filho, JCA, Pedrosa, RP, Arteaga-Fernandez, E, Mady, C, Drager, LF, Lorenzi-Filho, G, Marques, JS, Almeida, A M G, Menezes, M, Silva, GL, Placido, R, Amaro, C, Brito, D, Diogo, AN, Lourenco, M R, Azevedo, O, Moutinho, J, Nogueira, I, Machado, I, Portugues, J, Quelhas, I, Lourenco, A, Calore, C, Muraru, D, Melacini, P, Badano, LP, Mihaila, S, Puma, L, Peluso, D, Casablanca, S, Ortile, A, Iliceto, S, Kang, M-K, Yu, SH, Park, JJ, Kim, SH, Park, TY, Mun, H-S, C, S, Cho, S-R, Han, SW, Lee, N, Khalifa, E A, Hamodraka, E, Kallistratos, M, Zacharopoulou, I, Kouremenos, N, Mavropoulos, D, Tsoukas, A, Kontogiannis, N, Papanikolaou, N, Tsoukanas, K, Manolis, A, Villagraz Tecedor, L, Jimenez Lopez Guarch, C, Alonso Chaterina, S, Blazquez Arrollo, L, Lopez Melgar, B, Veitia Sarmiento, AL, Mayordomo Gomez, S, Escribano Subias, MP, Lichodziejewska, B, Kurnicka, K, Goliszek, S, Dzikowska Diduch, O, Kostrubiec, M, Krupa, M, Grudzka, K, Ciurzynski, M, Palczewski, P, Pruszczyk, P, Sakata, K, Ishiguro, M, Kimura, G, Uesugo, Y, Takemoto, K, Minamishima, T, Futuya, M, Matsue, S, Satoh, T, Yoshino, H, Signorello, MC, Gianturco, L, Colombo, C, Stella, D, Atzeni, F, Boccassini, L, Sarzi-Puttini, PC, Turiel, M, Kinova, E, Deliiska, B, Krivoshiev, S, Goudev, A, De Stefano, F, Santoro, C, Buonauro, A, Schiano-Lomoriello, V, Muscariello, R, De Palma, D, Galderisi, M, Ranganadha Babu, B, Chidambaram, SUNDAR, Sangareddi, V, Dhandapani, VE, Ravi, MS, Meenakshi, K, Muthukumar, D, Swaminathan, N, Ravishankar, G, Bruno, R M, Giardini, G, Catizzo, B, Brustia, R, Malacrida, S, Armenia, S, Cauchy, E, Pratali, L, Resamont2, Cesana, F, Alloni, M, Vallerio, P, De Chiara, B, Musca, F, Belli, O, Ricotta, R, Siena, S, Moreo, A, Giannattasio, C, Magnino, C, Omede, P, Avenatti, E, Presutti, D, Sabia, L, Moretti, C, Bucca, C, Gaita, F, Veglio, F, Milan, A, Eichhorn, JG, Springer, W, Helling, A, Alarajab, A, Loukanov, T, Ikeda, M, Kijima, Y, Akagi, T, Toh, N, Oe, H, Nakagawa, K, Tanabe, Y, Watanabe, N, Ito, H, Hascoet, S, Hadeed, K, Marchal, P, Bennadji, A, Peyre, M, Dulac, Y, Heitz, F, Alacoque, X, Chausseray, G, Acar, P, Kong, WILL, Ling, LH, Yip, JAMES, Poh, KK, Vassiliou, V, Rekhraj, S, Hoole, SP, Watkinson, O, Kydd, A, Boyd, J, Mcnab, D, Densem, C, Shapiro, LM, Rana, BS, Potpara, TS, Djikic, D, Polovina, M, Marcetic, Z, Peric, V, Lip, GYH, Gaudron, P, Niemann, M, Herrmann, S, Hu, K, Strotmann, J, Beer, M, Bijnens, B, Liu, D, Ertl, G, Weidemann, F, Peric, V, Jovanovic, A, Djikic, D, Otasevic, P, Kochanowski, J, Piatkowski, R, Scislo, P, Grabowski, M, Marchel, M, Opolski, G, Bandera, F, Guazzi, M, Arena, R, Corra, U, Ghio, S, Forfia, P, Rossi, A, Dini, F, Cahalin, LP, Temporelli, L, Rallidis, L, Tsangaris, I, Makavos, G, Anthi, A, Pappas, A, Orfanos, S, Lekakis, J, Anastasiou-Nana, M, Kuznetsov, V A, Krinochkin, D V, Yaroslavskaya, E I, Zaharova, E H, Pushkarev, G S, Mizia-Stec, K, Wita, K, Mizia, M, Loboz-Grudzien, K, Szwed, H, Kowalik, I, Kukulski, T, Gosciniak, P, Kasprzak, J, Plonska-Gosciniak, E, Cimino, S, Pedrizzetti, G, Tonti, G, Cicogna, F, Petronilli, V, De Luca, L, Iacoboni, C, Agati, L, Hoffmann, R, Barletta, G, Von Bardeleben, S, Kasprzak, J, Greis, C, Vanoverschelde, J, Becher, H, Galrinho, A, Moura Branco, L, Fiarresga, A, Cacela, D, Ramos, R, Cruz Ferreira, R, Van Den Oord, SCH, Akkus, Z, Bosch, JG, Renaud, G, Sijbrands, EJG, Verhagen, HJM, Van Der Lugt, A, Van Der Steen, AFW, Schinkel, AFL, Mordi, I, Tzemos, N, Stanton, T, Delgado, D, Yu, E, Drakopoulou, M, Gonzalez-Gonzalez, AM, Karonis, T, Roussin, I, Babu-Narayan, S, Swan, L, Senior, R, Li, W, Parisi, V, Pagano, G, Pellegrino, T, Femminella, GD, De Lucia, C, Formisano, R, Cuocolo, A, Perrone Filardi, P, Leosco, D, Rengo, G, Unlu, S, Farsalinos, K, Amelot, K, Daraban, A, Ciarka, A, Delcroix, M, Voigt, JU, Miskovic, A, Poerner, TD, Goebel, B, Stiller, CH, Moritz, A, Sakata, K, Uesugo, Y, Kimura, G, Ishiguro, M, Takemoto, K, Minamishima, T, Futuya, M, Satoh, T, Yoshino, H, Miyoshi, T, Tanaka, H, Kaneko, A, Matsumoto, K, Imanishi, J, Motoji, Y, Mochizuki, Y, Minami, H, Kawai, H, Hirata, K, Wutthimanop, A, See, O, Vathesathokit, P, Yamwong, S, Sritara, P, Rosner, A, Kildal, AB, Stenberg, TA, Myrmel, T, How, OJ, Capriolo, M, Frea, S, Giustetto, C, Scrocco, C, Benedetto, S, Grosso Marra, W, Morello, M, Gaita, F, Garcia-Gonzalez, P, Cozar-Santiago, P, Chacon-Hernandez, N, Ferrando-Beltran, M, Fabregat-Andres, O, De La Espriella-Juan, R, Fontane-Martinez, C, Jurado-Sanchez, R, Morell-Cabedo, S, Ridocci-Soriano, F, Mihaila, S, Piasentini, E, Muraru, D, Peluso, D, Casablanca, S, Puma, L, Naso, P, Iliceto, S, Vinereanu, D, Badano, LP, Tarzia, P, Villano, A, Figliozzi, S, Russo, G, Parrinello, R, Lamendola, P, Sestito, A, Lanza, GA, Crea, F, Sulemane, S, Panoulas, VF, Bratsas, A, Frankel, AH, Nihoyannopoulos, P, Dores, H, Andrade, MJ, Almeida, MS, Goncalves, PA, Branco, P, Gaspar, A, Gomes, A, Horta, E, Carvalho, MS, Mendes, M, Yue, WS, Li, XY, Chen, Y, Luo, Y, Gu, P, Yiu, KH, Siu, CW, Tse, HF, Cho, EJ, Lee, SH, Hwang, BH, Kim, DB, Jang, SW, Jeon, HK, Youn, HJ, and Kim, JH

Abstract

Background: Progress in the technique of TAVR requires good knowledge of the aortic root. With this aim new specialized software appears, with the ability of automated quantitative modeling of the AV and root from 3D TEE.The purpose of this study was to validate this model with the measurements made manually. Methods: Eight patients undergoing TAVR in our center where included. The diameters of the aortic annulus, sinotubular union (STU) and sinus of valsalva (SV) were measured by 2D TEE; diameters and areas of aortic annulus, STU and SV as well as anatomic aortic valve area were measured by 3D TEE. Afterwards, the images were analyzed using the new software (Figure 1). Results. We showed good correlation with aortic annulus diameter measured by 2D TEE (r:,832 p:,01) and excellent correlation with one of the aortic annulus diameter measured by 3D TEE (r:,941 p:,00). The same happened with the area (r:,720 p:,04). Regarding the measurements at SV level, the correlations between the diameters by 2D TEE and 3D TEE with the measurements obtained with the new model were the following (r:,771;p:,025) and (r:,797;p:,018). The correlation of the area was also good (r:,812 p:,014).An excellent correlation was found between the measurements at UST level. UST diameter by 2D TEE (r:,818;P:,013), by ETE3D (r:,800;p:,017) and area (r:,844;p:,008).Finally, the anatomic aortic valve area measured by the new model showed significant correlation with the 3D TTE (r:,830 p:,011). Conclusions. There is a proper correlation between manual and automated measurements analyzed by the new model. The feasibility of determine the TAVR results with geometric models based on image, prior to procedure, is one of the possibilities of this new software. Prospective studies are necessary to define its applicability. Figure 1

Published
2013
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27. Club 35 Poster Session Wednesday 11 December: 11/12/2013, 09:30-16:00 * Location: Poster area

Author

Montoro Lopez, M, Pons De Antonio, I, Itziar Soto, C, Florez Gomez, R, Alonso Ladreda, A, Rios Blanco, JJ, Refoyo Salicio, E, Moreno Yanguela, M, Lopez Sendon, JL, Guzman Martinez, G, Van De Heyning, C M, Magne, J, Pierard, LA, Bruyere, PJ, Davin, L, De Maeyer, C, Paelinck, BP, Vrints, CJ, Lancellotti, P, Michalski, BW, Krzeminska-Pakula, M, Lipiec, P, Szymczyk, E, Chrzanowski, L, Kasprzak, JD, Leao, R N, Florencio, A F, Oliveira, A R, Bento, B, Lopes, S, Calaca, J, Palma Reis, R, Krestjyaninov, MV, Gimaev, RH, Razin, VA, Arangalage, D, Chiampan, A, Cimadevilla, C, Touati, A, Himbert, D, Brochet, E, Iung, B, Nataf, P, Vahanian, A, Messika-Zeitoun, D, Guvenc, TS, Karacimen, D, Erer, HB, Ilhan, E, Sayar, N, Karakus, G, Eren, M, Iriart, X, Tafer, N, Roubertie, F, Mauriat, P, Thambo, JB, Wang, J, Fang, F, Yip, G WK, Sanderson, J, Feng, W, Yu, CM, Lam, YY, Assabiny, A, Apor, A, Nagy, A, Vago, H, Toth, A, Merkely, B, Kovacs, A, Castaldi, B, Vida, VL, Guariento, A, Padalino, M, Cerutti, A, Maschietto, N, Biffanti, R, Reffo, E, Stellin, G, Milanesi, O, Baronaite-Dudoniene, K, Urbaite, L, Smalinskas, V, Veisaite, R, Vasylius, T, Vaskelyte, J, Puodziukynas, A, Wieczorek, J, Rybicka-Musialik, A, Berger-Kucza, A, Hoffmann, A, Wnuk-Wojnar, A, Mizia-Stec, K, Melao, F, Ribeiro, V, Amorim, S, Araujo, C, Torres, JP, Cardoso, JS, Pinho, P, Maciel, MJ, Storsten, P, Eriksen, M, Boe, E, Estensen, ME, Erikssen, G, Smiseth, OA, Skulstad, H, Miglioranza, MH, Gargani, L, Sant`Anna, RT, Rover, M, Martins, VM, Mantovanni, A, Kalil, RK, Leiria, TL, Luo, XX, Fang, F, Lee, PW, Zhang, ZH, Lam, YY, Sanderson, JE, Kwong, J SW, Yu, CM, Borowiec, A, Dabrowski, R, Wozniak, J, Jasek, S, Chwyczko, T, Kowalik, I, Janas, J, Musiej-Nowakowska, E, Szwed, H, Palinsky, M, Petrovicova, J, Pirscova, M, Baricevic, Z, Lovric, D, Cikes, M, Skoric, B, Ljubas Macek, J, Reskovic Luksic, V, Separovic Hanzevacki, J, Milicic, D, Elmissiri, AM, El Shahid, GS, Abdal-Wahhab, S, Vural, M G, Yilmaz, M, Cetin, S, Akdemir, R, Yoldas, T K, Yeter, E, Karamanou, AG, Hamodraka, ES, Lekakis, IA, Paraskevaidis, IA, Kremastinos, DT, Appiah-Dwomoh, E K, Wang, VC, Otto, C, Mayar, F, Bonaventura, K, Sunman, H, Canpolat, U, Kuyumcu, M, Yorgun, H, Sahiner, L, and Ozer, N

Abstract

Purpose: It is known the higher prevalence of structural heart disease in HIV patients, mostly diastolic dysfunction and pulmonary hypertension. In spite of that, there are few data about predisposing factors. Our objective was to evaluate whether HIV stage or detectable blood viral load correlate with the degree of heart disease. Methods: We conducted a prospective cohort study with HIV patients monitored by the internal medicine unit of our institution. We selected symptomatic patients with functional class ≥ II of NYHA scale. Viral blood load and CD4 count were systematically determined in order to obtain the HIV stage. Patients underwent a transthoracic echocardiogram to assess ventricular hypertrophy, systolic and diastolic dysfunction and pulmonary hypertension, according to the limits set by ESC guidelines. Results: Data were obtained from 65 HIV patients with dyspnea (63% male) with a mean age of 48 years. 50% were in NYHA grade II, 32.3% III and 17.7% IV. 46.7% of patients had some data of structural heart disease (figure). Belong to AIDS group (65.3%) did not correlate with the degree of heart disease. However, patients with positive blood viral load had a significantly higher incidence of structural heart disease than those with undetectable load (75% vs. 43% p <0.04), independent of their cardiovascular risk profile or type of antiretroviral therapy (Table). Conclusion: In our experience, half of HIV patients with dyspnea show echocardiographic data of structural heart disease. Detectable viral load in blood doubles the prevalence of heart disease, so that HIV itself may be an independent causal agent. These data should be taken into account in the screening of structural heart disease in these patients. Figure Prevalence of structural heart disease

Published
2013
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28. Abstracts

Author

Doulaptsis, C, Masci, PG, Goetschalckx, K, Janssens, S, Bogaert, J, Ferreira, VM, Piechnik, SK, DallArmellina, E, Karamitsos, TD, Francis, JM, Ntusi, N, Holloway, C, Choudhury, RP, Kardos, A, Robson, MD, Friedrich, MG, Neubauer, S, Miszalski-Jamka, T, Sokolowska, B, Szczeklik, W, Karwat, K, Miszalski-Jamka, K, Belzak, K, Malek, L, Mazur, W, Kereiakes, DJ, Jazwiec, P, Musial, J, Pedrotti, P, Masciocco, G, DAngelo, L, Milazzo, A, Quattrocchi, G, Zanotti, F, Frigerio, M, Roghi, A, Rimoldi, O, Kaasalainen, T, Kivistö, S, Holmström, M, Pakarinen, S, Hänninen, H, Sipilä, O, Lauerma, K, Banypersad, S.M, Fontana, M, Maestrini, V, Sado, D.M, Pinney, J, Wechalekar, A.D, Gillmore, J.D, Lachmann, H, Hawkins, P.N, Moon, J.C, Barone-Rochette, G, Pierard, S, Seldrum, S, de Ravensteen, CM, Melchior, J, Maes, F, Pouleur, A-C, Vancraeynest, D, Pasquet, A, Vanoverschelde, J-L, L Gerber, B, Captur, G, Muthurangu, V, Flett, AS, Wilson, R, Barison, A, Anderson, S, Cook, C, Sado, DM, McKenna, WJ, Mohun, TJ, Elliott, PM, Moon, JC, Pepe, A, Meloni, A, Gulino, L, Rossi, G, Paci, C, Spasisno, A, keilberg, P, Restaino, G, Resta, MC, Positano, V, lombardi, M, Reiter, U, Reiter, G, Kovacs, G, Schmidt, A, Olschewski, H, Fuchsjäger, M, Macmillan, A, Dabir, D, Rogers, T, Monaghan, M, Nagel, E, Puntmann, V, Semaan, E, Spottiswoode, B, Freed, B, Carr, M, Wasielewski, M, Fortney-Campione, K, Shah, S, Carr, J, Markl, M, Collins, J, Sung, YM, Hinojar, R, Ucar, EA, Dabir, D, Voigt, T, Gaddum, N, Schaeffter, T, Nagel, E, Puntmann, VO, Dabir, D, Rogers, T, Ucar, EA, Kidambi, A, Plein, S, Gebker, R, Schnackenburg, B, Voigt, T, Schaeffter, T, Nagel, E, Puntmann, VO, McAlindon, E, Bucciarelli-Ducci, C, Sado, D, Maestrini, V, Piechnik, S, Porter, J, Yamamura, J, Fischer, R, Moon, J, Symons, R, Doulaptsis, C, Masci, P.G, Goetschalckx, K, Dymarkowski, S, Janssens, S, Bogaert, J, Yalin, K, Golcuk, E, Ozer, CS, Buyukbayrak, H, Yilmaz, R, Dursun, M, Bilge, AK, Adalet, K, Reinstadler, SJ, Klug, G, Feistritzer, HJ, Mayr, A, Harrasser, B, Krauter, L, Mair, J, Schocke, MF, Pachinger, O, Metzler, B, Rigolli, M, To, A, Edwards, C, Ding, P, Christiansen, J, Rodríguez-Palomares, JF, Ortiz, JT, Bucciarelli, C, Lee, D, Wu, E, Bonow, RO, Karwat, K, Tomala, M, Miszalski-Jamka, K, Licholaj, S, Mazur, W, Kereiakes, DJ, Nessler, J, Zmudka, K, Jazwiec, P, Miszalski-Jamka, T, Peltonen, J, Kaasalainen, T, Kivistö, S, Holmström, M, Lauerma, K, Rutz, T, Meierhofer, C, Martinoff, S, Ewert, P, Hess, J, Stern, H, Fratz, S, Groarke, JD, Waller, AH, Blankstein, R, Kwong, RY, Steigner, M, Alizadeh, Z, Alizadeh, A, Khajali, Z, Mohammadzadeh, A, Kaykhavani, A, Heidarali, M, Singh, A, Bekele, S, Gunarathne, A, Khan, J, Nazir, SN, Steadman, CD, Kanagala, P, Horsfield, MA, McCann, GP, Duncan, RF, Dundon, BK, Nelson, AJ, Williams, K, Carbone, A, Worthley, MI, Zaman, A, Worthley, SG, Monney, P, Piccini, D, Rutz, T, Vincenti, G, Koestner, S, Stuber, M, Schwitter, J, Gripari, P, Maffessanti, F, Pontone, G, Andreini, D, Bertella, E, Mushtaq, S, Caiani, EG, Pepi, M, El ghannudi, S, Nghiem, A, Germain, P, Jeung, M-J, Roy, C, Gangi, A, Nucifora, G, Muser, D, Masci, PG, Barison, A, Piccoli, G, Rebellato, L, Puppato, M, Gasparini, D, Lombardi, M, Proclemer, A, Nucifora, G, Muser, D, Masci, PG, Barison, A, Piccoli, G, Rebellato, L, Puppato, M, Gasparini, D, Lombardi, M, Proclemer, A, Pöyhönen, P, Kivistö, S, Holmströn, M, Hänninen, H, Thorning, C, Bickelhaupt, S, Kampmann, C, Wentz, KU, Widmer, U, Juli, CF, Miszalski-Jamka, K, Klys, J, Glowacki, J, Kijas, M, Miszalski-Jamka, T, Adamczyk, T, Kwiecinski, R, Bogucka-Czapska, J, Ozaist, M, Mazur, W, Kluczewska, E, Kalarus, Z, Kukulski, T, Karakus, G, Marzluf, B, Bonderman, D, Tufaro, C, Pfaffenberger, S, Babyev, J, Maurer, G, Mascherbauer, J, Kockova, R, Tintera, J, Kautznerova, D, Cerna, D, Sedlacek, K, Kryze, L, El-Husseini, W, Sikula, V, Segetova, M, Kautzner, J, Vasconcelos, M, Lebreiro, A, Martins, E, Cardoso, JS, Madureira, AJ, Ramos, I, Maciel, MJ, Florian, A, Ludwig, A, Rösch, S, Sechtem, U, Yilmaz, A, Monmeneu, J.V, López-Lereu, M.P, Bonanad, C, Sanchis, J, Chaustre, F, Merlos, P, Valero, E, Bodí, V, Chorro, F.J, Yalin, K, Golcuk, E, Ozer, CS, Buyukbayrak, H, Yilmaz, R, Dursun, M, Bilge, AK, Adalet, K, Klug, G, Reinstadler, SJ, Feistritzer, HJ, Mayr, A, Riegler, N, Schocke, M, Esterhammer, R, Kremser, C, Pachinger, O, Metzler, B, Siddiqi, N, Cameron, D, Neil, C, Jagpal, B, Singh, S, Schwarz, K, Papadopoulou, S, Frenneaux, MP, Dawson, D, Robbers, LFHJ, Eerenberg, ES, Teunissen, PFA, Jansen, MF, Hollander, MR, Horrevoets, AJG, Knaapen, P, Nijveldt, R, Levi, MM, van Rossum, AC, Niessen, HWM, Marcu, CB, Beek, AM, van Royen, N, Everaars, H, Robbers, LFHJ, Nijveldt, R, Beek, AM, Teunissen, PFA, Hirsch, A, van Royen, N, Zijlstra, F, Piek, JJ, van Rossum, AC, Goitein, O, Grupper, A, Hamdan, A, Eshet, Y, Beigel, R, Medvedofsky, D, Herscovici, R, Konen, E, Hod, H, Matetzky, S, Cadenas, R, Iniesta, AM, Refoyo, E, Antorrena, I, Guzman, G, Cuesta, E, Salvador, O, López, T, Moreno, M, López-Sendon, JL, Alam, SR, Spath, N, Richards, J, Dweck, M, Shah, A, Lang, N, Semple, S, MacGillivray, T, Mckillop, G, Mirsadraee, S, Pessotto, R, Zamvar, V, Newby, DE, Henriksen, P, Reiter, G, Reiter, U, Kovacs, G, Olschewski, H, Fuchsjäger, M, Ahmad, S, Raza, U, Malik, A, Sun, JP, Eisner, R, Mazur, W, ODonnell, R, Positano, V, Meloni, A, Santarelli, MF, Landini, L, Tassi, C, Grimaldi, S, Gulino, L, De Marchi, D, Chiodi, E, Renne, S, Lombardi, M, Pepe, A, Wu, L, Germans, T, Güçlü, A, Allaart, CP, van Rossum, AC, Kalisz, K, Lehenbauer, K, Katz, D, Bi, X, Cordts, M, Guetter, C, Jolly, M-P, Freed, B, Shah, S, Markl, M, Flukiger, J, Carr, J, Collins, J, Osiak, A, Tyrankiewicz, U, Jablonska, M, Jasinski, K, Jochym, PT, Chlopicki), S, Skorka, T, Kalisz, K, Semaan, E, Katz, D, Bi, X, Cordts, M, Guetter, C, Jolly, MP, Freed, B, Flukiger, J, Lee, D, Kansal, P, Shah, S, Markl, M, Carr, J, Collins, J, Groarke, JD, Shah, RV, Waller, AH, Abbasi, SA, Kwong, RY, Blankstein, R, Steigner, M, Chin, CWL, Semple, S, Malley, T, White, A, Prasad, S, Newby, DE, Dweck, M, Pepe, A, Meloni, A, Lai, ME, Vaquer, S, Gulino, L, De Marchi, D, Cuccia, L, Midiri, M, Vallone, A, Positano, V, Lombardi, M, Pedrotti, P, Milazzo, A, Quattrocchi, G, Roghi, A, Rimoldi, O, Barison, A, De Marchi, D, Masci, P, Milanesi, M, Aquaro, GD, Keilberg, P, Positano, V, Lombardi, M, Positano, Vincenzo, Barison, Andrea, Pugliese, Nicola Riccardo, Masci, Piergiorgio, Del Franco, Annamaria, Aquaro, Giovanni Donato, Landini, Luigi, Lombardi, Massimo, Dieringer, MA, Deimling, M, Fuchs, K, Winter, L, Kraus, O, Knobelsdorff-Brenkenhoff, FV, Schulz-Menger, J, Niendorf, T, Hinojar, R, Ucar, EA, DCruz, D, Sangle, S, Dabir, D, Voigt, T, Gaddum, N, Schaeffter, T, Nagel, E, Puntmann, VO, Sung, YM, Pontone, G, Andreini, D, Bertella, E, Mushtaq, S, Gripari, P, Cortinovis, S, Loguercio, M, Baggiano, A, Conte, E, Pepi, M, El ghannudi, S, Hop, O, Germain, P, Jeung, M-J, De Cesare, A, Roy, C, Gangi, A, Barone-Rochette, G, Pierard, S, Seldrum, S, De Meester de Ravensteen, C, Melchior, J, Maes, F, Pouleur, A-C, Vancraeynest, D, Pasquet, A, Vanoverschelde, J-L, L Gerber, B, Bekele, S, Singh, A, Khan, JN, Nazir, SA, Kanagala, P, McCann, GP, Singh, A, Steadman, CD, Bekele, S, Khan, JN, Nazir, SA, Kanagala, P, McCann, GP, Paelinck, BP, Vandendriessche, T, De Bock, D, De Maeyer, C, Parizel, PM, Christiaan, J, Trauzeddel, RF, Gelsinger, C, Butter, C, Barker, A, Markl, M, Schulz-Menger, J, von Knobelsdorff, F, Florian, A, Schäufele, T, Ludwig, A, Rösch, S, Wenzelburger, I, Yilmaz, A, Sechtem, U, López-Lereu, M.P, Bonanad, C, Monmeneu, J.V, Sanchís, J, Estornell, J, Igual, B, Maceira, A, Chorro, F.J, Focardi, M, Cameli, M, Bennati, E, Massoni, A, Solari, M, Carbone, F, Banchi, B, Mondillo, S, Miia, H, Kirsi, L, Helena, H, Tiina, H, Jyri, L, Pauli, P, Sari, K, Schumm, J, Greulich, S, Grün, S, Ong, P, Klingel, K, Kandolf, R, Sechtem, U, Mahrholdt, H, Raimondi, F, Ou, P, Boudjemline, Y, Bajolle, F, Iserin, F, Bonnet, D, Collins, J, Kalisz, K, Benefield, B, Sarnari, R, Katz, D, Bi, X, Cordts, M, Guetter, C, Jolly, M-P, Freed, B, Flukiger, J, Kansal, P, Lee, D, Shah, S, Markl, M, Carr, J, Sokolowska, B, Miszalski-Jamka, T, Szczeklik, W, Karwat, K, Miszalski-Jamka, K, Belzak, K, Mazur, W, Kereiakes, DJ, Jazwiec, P, Musial, J, Silva, G, Almeida, AG, Resende, C, Marques, JS, Silva, D, David, C, Amaro, C, Costa, P, Silva, JAP, Diogo, AN, Tsokolov, AV, Senchilo, VG, Vertelkin, AV, Hoffmann, P, Mykjåland, G, Wangberg, H, Tønnessen, T, Sjaastad, I, Nordsletten, L, Hjørnholm, U, Løset, A, Rostrup, M, Meloni, A, Gulino, L, Keilberg, P, Palazzi, G, Maddaloni, D, Ascioti, C, Missere, M, Salvatori, C, Positano, V, Lombardi, M, Pepe, A, Meloni, A, Filosa, A, Gulino, L, Pulini, S, Salvatori, C, Chiodi, E, Ascioti, C, Keilberg, P, Positano, V, Lombardi, M, Pepe, A, Meloni, A, Gulino, L, Pietrapertosa, A, Izzi, G, De Marchi, D, Valeri, G, Preziosi, P, Positano, V, Lombardi, M, Pepe, A, Meloni, A, Ruffo, GB, Keilberg, P, Gulino, L, Gerardi, C, Sallustio, G, Tudisca, C, Positano, V, Lombardi, M, Pepe, A, Greulich, S, Backes, M, Schumm, J, Grün, S, Sechtem, U, Mahrholdt, H, Dorniak, K, MSc, AS, Szurowska, E, Fijalkowski, M, Rawicz-Zegrzda, D, Dudziak, M, Raczak, G, Hamdan, A, Baker, FA, Klein, M, Di Segni, E, Goitein, O, Fibisch, G, Konen, E, Müller-Bierl, B, Tanaka, K, Buls, N, Fierens, Y, van Cauteren, T, Willekens, I, van Laere, S, Luypaert, R, de Mey, J, Muzzarelli, S, Faragasso, E, Pedrazzini, G, Sürder, D, Pasotti, E, Moccetti, T, Faletra, F, Qayyum, AA, Hasbak, P, Larsson, HB, Mathiasen, AB, Vejlstrup, NG, Kjaer, A, Kastrup, J, Moschetti, K, Favre, D, Pinget, C, Pilz, G, Petersen, S, Wagner, A, Wasserfallen, JB, Schwitter, J, Ghosh Dastidar, A, Cengarle, M, McAlindon, E, Augustine, D, Nightingale, AK, Bucciarelli-Ducci, C, Dandekar, VK, Ertel, AW, Dickens, C, Gonzalez, RC, Farzaneh-Far, A, Ripley, DP, Higgins, D, McDiarmid, AK, Bainbridge, GJ, Uddin, A, Kidambi, A, Herzog, B, Greenwood, JP, Plein, S, Khanji, M, Newton, T, Westwood, M, Sekhri, N, and Petersen, SE

Abstract

Background-Aims: Early post-infarction pericardial injury is a common finding but its diagnosis remains elusive. Though C-reactive protein (CRP) is considered a marker of myocardial damage, reflecting myocardial inflammation at the infarcted area, we sought to assess the relationship between CRP and pericardial injury depicted by cardiovascular magnetic resonance (CMR) imaging in patients with ST elevation myocardial infarction (MI). Methods and results: 181 MI patients (84% male) were studied with CMR in the first week and at 4 months post-infarction to assess infarct characteristics, left ventricular volumes/function and pericardial injury. The latter was defined as pericardial fluid >4mm and/or enhancement on late gadolinium enhancement CMR. The CRP-value at day 2 (according to previous literature) was used for correlation with CMR and clinical parameters. Pericardial injury was noted in 87 patients, i.e. effusion (n = 30), inflammation (n = 46), both (n = 11). Patients with pericardial injury had significantly higher peak values of cardiac biomarkers (p<0.001) and higher peak CRP-values than patients with normal pericardium (median 13 vs 43 mg/dl, p<0.001). A strong correlation was found between peak CRP-values and a) left venticular ejection fraction and infarct size both at 1 week and 4 months, b) myocardial hemorrhage, microvascular obstruction (MVO) and pericardial injury at 1 week, c) cardiac biomarkers values and time to PCI. However in a multiple regression model only pericardial injury (p = 0.003) and less importantly time to PCI (p = 0.022) were the independent predictors of CRP values. Conclusion: Pericardial damage described by cardiac MRI occurs often after acute ST elevation MI. CRP-values at the acute phase of MI reflect not only inflammation at the infarcted area but even more the inflammation of the surrounding pericardial tissue. Table 1 Comparison of baseline clinical and biochemical parameters of patients with or without evidence of early post-infarct pericardial damage on CMR Normal Group (n = 94)Pericardial injury group (n = 87)p-valueAgem, years59±1160±120.48Male, n(%)83 (88)69 (79)0.10Diabets, n(%)12 (13)9 (10)0.61Smoker, n(%)52 (55)44 (51)0.52Hyperlipidemia, n(%)56 (60)55 (63)0.62BSA m22.0 ± 0.22.0 ± 0.20.20Time to PCI, min195 (155 − 274)223 (160 − 335)0.20Troponin I, μ/l44 (19 − 92)90 (44 − 149)>0.001CK-MB, U/L128 (77 − 216)250 (143 − 443)>0.001CRP, mg/dL13 (7 − 28)43 (16 − 96)>0.001Day of peak CRP2 (1 − 3)2 (1 − 3)0.39 Table 2 Significant correlations between CRP Values and corresponding CMR measurements, cardic biomarkers and clinical related parameters VariblesSpearmanscorrelations rp-valueCMR parameters 1 weekLV EF−0.28>0,001Infractsize(%ofLV)0.40>0,001Microvasular obstruction0.27>0,001Hemorrhage0.33>0,001Size of area atrisk0.31>0,001Transmurality0.30>0,001Pericaldial damage0.43>0,001CMR parameters 4 monthsLVEF−0.43>0,001Infarctsize(%ofLV)0.46>0,001Cardiac BiomarkersPeak TnI0.34>0,001Peak CK-MB0.32>0,001OtherTime to PCI0,1820,007

Published
2013
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29. Color Correction for Image Stitching by Monotone Cubic Spline Interpolation

Author

Fabio Bellavia, Carlo Colombo, Paredes, R, Cardoso, JS, Pardo, XM, Bellavia F., and Colombo C.

Subjects
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni, Image stitching, Demosaicing, Settore INF/01 - Informatica, business.industry, Color correction, MathematicsofComputing_NUMERICALANALYSIS, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Monotone cubic interpolation, Photometric blending, Cubic Hermite spline, Smoothing spline, Color transfer, Computer vision, Artificial intelligence, Color transfer, image stitching, photometric blending, business, Spline interpolation, Thin plate spline, Algorithm, Image gradient, ComputingMethodologies_COMPUTERGRAPHICS, Mathematics
Abstract

This paper proposes a novel color correction scheme for image stitching where the color map transfer is modelled by a monotone Hermite cubic spline and smoothly propagated into the target image. A three-segments monotone cubic spline minimizing color distribution statistics and gradient differences with respect to both the source and target images is used. While the spline model can handle non-linear color maps, the minimization over the gradient differences limits strong alterations on the image structure. Adaptive heuristics are introduced to reduce the minimization search space and thus computational time. Experimental comparisons with respect to the state-of-the-art linear mapping models show the validity of the proposed method.

Published
2015

30. Fast head pose estimation for human-computer interaction

Author

Roberto J. López-Sastre, Mario Garcia-Montero, Carolina Redondo-Cabrera, Tinne Tuytelaars, Paredes, R, Cardoso, JS, and Pardo, XM

Subjects
Hough forest, Computer science, business.industry, Probabilistic logic, RGB color model, Leverage (statistics), Computer vision, Artificial intelligence, PSI_VISICS, business, Pose
Abstract

© Springer International Publishing Switzerland 2015. This paper describes a Hough Forest based approach for fast head pose estimation in RGB images. The system has been designed for Human-Computer Interaction (HCI), in a way that with just a simple web-cam, our solution is able to detect the head and simultaneously estimate its pose. We leverage the Hough Forest with Probabilistic Locally Enhanced Voting model, and integrate it into a system with a skin detection step and a tracking filter for the head orientation. Our implementation drastically speeds up the head pose estimations, improving their accuracy with respect to the original model. We present extensive experiments on a publicly available and challenging dataset, where our approach outperforms the state-of-the-art. Garcia-Montero M., Redondo-Cabrera C., Lopez-Sastre R., Tuytelaars T., ''Fast head pose estimation for human-computer interaction'', Lecture notes in computer science, vol. 9117, pp. 101-110, 2015 (7th Iberian conference on pattern recognition and image analysis - IbPRIA 2015, June 17-19, 2015, Santiago de Compostela, Spain). ispartof: pages:101-110 ispartof: Pattern recognition and image analysis vol:9117 pages:101-110 ispartof: Iberian conference on pattern recognition and image analysis - IbPRIA 2015 location:Santiago de Compostela, Spain date:17 Jun - 19 Jun 2015 status: published

Published
2015

31. Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review.

Author

Nakayama LF, Matos J, Quion J, Novaes F, Mitchell WG, Mwavu R, Hung CJJ, Santiago APD, Phanphruk W, Cardoso JS, and Celi LA

Abstract

Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps-data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration-and delves into the risks for harm at each step and strategies for mitigating them., Competing Interests: LAC is the Editor-In-Chief of PLOS Digital Health., (Copyright: © 2024 Nakayama et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2024
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32. A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

Author

Nunes JD, Montezuma D, Oliveira D, Pereira T, and Cardoso JS

Abstract

Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nuclei instance segmentation and classification could alleviate the workload of pathologists and clinical researchers and at the same time facilitate the automatic extraction of clinically interpretable features for artificial intelligence (AI) tools. But due to high intra- and inter-class variability of nuclei morphological and chromatic features, as well as H&E-stains susceptibility to artefacts, state-of-the-art algorithms cannot correctly detect and classify instances with the necessary performance. In this work, we hypothesize context and attention inductive biases in artificial neural networks (ANNs) could increase the performance and generalization of algorithms for cell nuclei instance segmentation and classification. To understand the advantages, use-cases, and limitations of context and attention-based mechanisms in instance segmentation and classification, we start by reviewing works in computer vision and medical imaging. We then conduct a thorough survey on context and attention methods for cell nuclei instance segmentation and classification from H&E-stained microscopy imaging, while providing a comprehensive discussion of the challenges being tackled with context and attention. Besides, we illustrate some limitations of current approaches and present ideas for future research. As a case study, we extend both a general (Mask-RCNN) and a customized (HoVer-Net) instance segmentation and classification methods with context- and attention-based mechanisms and perform a comparative analysis on a multicentre dataset for colon nuclei identification and counting. Although pathologists rely on context at multiple levels while paying attention to specific Regions of Interest (RoIs) when analysing and annotating WSIs, our findings suggest translating that domain knowledge into algorithm design is no trivial task, but to fully exploit these mechanisms in ANNs, the scientific understanding of these methods should first be addressed., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Joao Diogo Nunes reports financial support was provided by Foundation for Science and Technology. Joao Diogo Nunes reports a relationship with Foundation for Science and Technology that includes: funding grants., (Copyright © 2024. Published by Elsevier B.V.)

Published
2024
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33. Anonymizing medical case-based explanations through disentanglement.

Author

Montenegro H and Cardoso JS

Subjects
Humans, Deep Learning, Data Anonymization
Abstract

Case-based explanations are an intuitive method to gain insight into the decision-making process of deep learning models in clinical contexts. However, medical images cannot be shared as explanations due to privacy concerns. To address this problem, we propose a novel method for disentangling identity and medical characteristics of images and apply it to anonymize medical images. The disentanglement mechanism replaces some feature vectors in an image while ensuring that the remaining features are preserved, obtaining independent feature vectors that encode the images' identity and medical characteristics. We also propose a model to manufacture synthetic privacy-preserving identities to replace the original image's identity and achieve anonymization. The models are applied to medical and biometric datasets, demonstrating their capacity to generate realistic-looking anonymized images that preserve their original medical content. Additionally, the experiments show the network's inherent capacity to generate counterfactual images through the replacement of medical features., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Published
2024
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35. An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.

Author

Neto PC, Montezuma D, Oliveira SP, Oliveira D, Fraga J, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Reinhard S, Zlobec I, Pinto IM, and Cardoso JS

Abstract

Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996., (© 2024. The Author(s).)

Published
2024
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36. Ordered mesoporous silicas for potential applications in solid vaccine formulations.

Author

Miranda MCR, Nunes CM, Santos LF, da Silva LB, de Jesus VR, Filho NA, Pedro JAF, Lopes JLS, Oliveira CLP, Fantini MCA, Cardoso JS, Trezena AG, Ribeiro OG, Sant'Anna OA, Tino-De-Franco M, and Martins TS

Subjects
Silicon Dioxide chemistry, Vaccines
Abstract

In an effort to develop efficient vaccine formulations, the use of ordered mesoporous silica (SBA-15) as an antigen carrier has been investigated. SBA-15 has required properties such as high surface area and pore volume, including narrow pore size distribution to protect antigens inside its matrix. This study aimed to examine the impact of solvent removal methods, specifically freeze-drying and evaporation on the intrinsic properties of an immunogenic complex. The immunogenic complexes, synthesized and incorporated with BSA, were characterized by various physicochemical techniques. Small Angle X-ray Scattering measurements revealed the characteristic reflections associated to pure SBA-15, indicating the preservation of the silica mesostructured following BSA incorporation and the formation of BSA aggregates within the macropore region. Nitrogen Adsorption Isotherm measurements demonstrated a decrease in surface area and pore volume for all samples, indicating that the BSA was incorporated into the SBA-15 matrix. Fluorescence spectroscopy evidenced that the tryptophan residues in BSA inside SBA-15 or in solution displayed similar spectra, showing the preservation of the aromatic residues' environment. The Circular Dichroism spectra of BSA in both conditions suggest the preservation of its native secondary structure after the encapsulation process. The immunogenic analysis with the detection of anti-BSA IgG did not give any significant difference between the non-dried, freeze-dried or evaporated groups. However, all groups containing BSA and SBA-15 showed results almost three times higher than the groups with pure BSA (control group). These facts indicate that none of the BSA incorporation methods interfered with the immunogenicity of the complex. In particular, the freeze-dried process is regularly used in the pharmaceutical industry, therefore its adequacy to produce immunogenic complexes was proved Furthermore, the results showed that SBA-15 increased the immunogenic activity of BSA., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Tereza S. Martins reports financial support and equipment, drugs, or supplies were provided by State of Sao Paulo Research Foundation. Tereza S. Martins reports financial support and equipment, drugs, or supplies were provided by Coordination of Higher Education Personnel Improvement. Osvaldo A. Sant Anna reports financial support and equipment, drugs, or supplies were provided by State of Sao Paulo Research Foundation. Marcia C.A. Fantini reports financial support and equipment, drugs, or supplies were provided by State of Sao Paulo Research Foundation. Marcia C.A. Fantini reports financial support and equipment, drugs, or supplies were provided by National Council for Scientific and Technological Development. Cristiano L.P. Oliveira reports financial support and equipment, drugs, or supplies were provided by State of Sao Paulo Research Foundation. Cristiano L. P. Oliveira reports financial support and equipment, drugs, or supplies were provided by National Council for Scientific and Technological Development. Jose L.S. Lopes reports financial support and equipment, drugs, or supplies were provided by State of Sao Paulo Research Foundation. Matheus C. R. Miranda reports financial support was provided by State of Sao Paulo Research Foundation. Jessica A. F. Pedro reports financial support was provided by State of Sao Paulo Research Foundation. Leonardo B. da Silva reports financial support was provided by Coordination of Higher Education Personnel Improvement. Carmen M. Nunes reports financial support was provided by National Council for Scientific and Technological Development. Luana F. Santos reports financial support was provided by National Council for Scientific and Technological Development., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Published
2024
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37. Explaining Bounding Boxes in Deep Object Detectors Using Post Hoc Methods for Autonomous Driving Systems.

Author

Nogueira C, Fernandes L, Fernandes JND, and Cardoso JS

Abstract

Deep learning has rapidly increased in popularity, leading to the development of perception solutions for autonomous driving. The latter field leverages techniques developed for computer vision in other domains for accomplishing perception tasks such as object detection. However, the black-box nature of deep neural models and the complexity of the autonomous driving context motivates the study of explainability in these models that perform perception tasks. Moreover, this work explores explainable AI techniques for the object detection task in the context of autonomous driving. An extensive and detailed comparison is carried out between gradient-based and perturbation-based methods (e.g., D-RISE). Moreover, several experimental setups are used with different backbone architectures and different datasets to observe the influence of these aspects in the explanations. All the techniques explored consist of saliency methods, making their interpretation and evaluation primarily visual. Nevertheless, numerical assessment methods are also used. Overall, D-RISE and guided backpropagation obtain more localized explanations. However, D-RISE highlights more meaningful regions, providing more human-understandable explanations. To the best of our knowledge, this is the first approach to obtaining explanations focusing on the regression of the bounding box coordinates.

Published
2024
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38. BOLD: Blood-gas and Oximetry Linked Dataset - Open Source Research.

Author

Matos J, Struja T, Gallifant J, Nakayama L, Charpignon ML, Liu X, Economou-Zavlanos N, Cardoso JS, Johnson KS, Bhavsar N, Gichoya J, Celi LA, and Wong AI

Abstract

Pulse oximeters measure peripheral arterial oxygen saturation (SpO 2 ) noninvasively, while the gold standard (SaO 2 ) involves arterial blood gas measurement. There are known racial and ethnic disparities in their performance. BOLD is a new comprehensive dataset that aims to underscore the importance of addressing biases in pulse oximetry accuracy, which disproportionately affect darker-skinned patients. The dataset was created by harmonizing three Electronic Health Record databases (MIMIC-III, MIMIC-IV, eICU-CRD) comprising Intensive Care Unit stays of US patients. Paired SpO 2 and SaO 2 measurements were time-aligned and combined with various other sociodemographic and parameters to provide a detailed representation of each patient. BOLD includes 49,099 paired measurements, within a 5-minute window and with oxygen saturation levels between 70-100%. Minority racial and ethnic groups account for ∼25% of the data - a proportion seldom achieved in previous studies. The codebase is publicly available. Given the prevalent use of pulse oximeters in the hospital and at home, we hope that BOLD will be leveraged to develop debiasing algorithms that can result in more equitable healthcare solutions.

Published
2023
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39. Evaluating the ability of an artificial-intelligence cloud-based platform designed to provide information prior to locoregional therapy for breast cancer in improving patient's satisfaction with therapy: The CINDERELLA trial.

Author

Kaidar-Person O, Antunes M, Cardoso JS, Ciani O, Cruz H, Di Micco R, Gentilini OD, Gonçalves T, Gouveia P, Heil J, Kabata P, Lopes D, Martinho M, Martins H, Mavioso C, Mika M, Montenegro H, Oliveira HP, Pfob A, Rotmensz N, Schinköthe T, Silva G, Tarricone R, and Cardoso MJ

Subjects
Female, Humans, Cloud Computing, Intelligence, Patient Satisfaction, Prospective Studies, Artificial Intelligence, Breast Neoplasms surgery
Abstract

Background: Breast cancer therapy improved significantly, allowing for different surgical approaches for the same disease stage, therefore offering patients different aesthetic outcomes with similar locoregional control. The purpose of the CINDERELLA trial is to evaluate an artificial-intelligence (AI) cloud-based platform (CINDERELLA platform) vs the standard approach for patient education prior to therapy., Methods: A prospective randomized international multicentre trial comparing two methods for patient education prior to therapy. After institutional ethics approval and a written informed consent, patients planned for locoregional treatment will be randomized to the intervention (CINDERELLA platform) or controls. The patients in the intervention arm will use the newly designed web-application (CINDERELLA platform, CINDERELLA APProach) to access the information related to surgery and/or radiotherapy. Using an AI system, the platform will provide the patient with a picture of her own aesthetic outcome resulting from the surgical procedure she chooses, and an objective evaluation of this aesthetic outcome (e.g., good/fair). The control group will have access to the standard approach. The primary objectives of the trial will be i) to examine the differences between the treatment arms with regards to patients' pre-treatment expectations and the final aesthetic outcomes and ii) in the experimental arm only, the agreement of the pre-treatment AI-evaluation (output) and patient's post-therapy self-evaluation., Discussion: The project aims to develop an easy-to-use cost-effective AI-powered tool that improves shared decision-making processes. We assume that the CINDERELLA APProach will lead to higher satisfaction, better psychosocial status, and wellbeing of breast cancer patients, and reduce the need for additional surgeries to improve aesthetic outcome., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Kaidar-Person et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2023
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40. Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers.

Author

Montezuma D, Oliveira SP, Neto PC, Oliveira D, Monteiro A, Cardoso JS, and Macedo-Pinto I

Subjects
Humans, Software, Machine Learning, Artificial Intelligence, Pathologists
Abstract

Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study's objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2023
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41. Deep Edge Detection Methods for the Automatic Calculation of the Breast Contour.

Author

Freitas N, Silva D, Mavioso C, Cardoso MJ, and Cardoso JS

Abstract

Breast cancer conservative treatment (BCCT) is a form of treatment commonly used for patients with early breast cancer. This procedure consists of removing the cancer and a small margin of surrounding tissue, while leaving the healthy tissue intact. In recent years, this procedure has become increasingly common due to identical survival rates and better cosmetic outcomes than other alternatives. Although significant research has been conducted on BCCT, there is no gold-standard for evaluating the aesthetic results of the treatment. Recent works have proposed the automatic classification of cosmetic results based on breast features extracted from digital photographs. The computation of most of these features requires the representation of the breast contour, which becomes key to the aesthetic evaluation of BCCT. State-of-the-art methods use conventional image processing tools that automatically detect breast contours based on the shortest path applied to the Sobel filter result in a 2D digital photograph of the patient. However, because the Sobel filter is a general edge detector, it treats edges indistinguishably, i.e., it detects too many edges that are not relevant to breast contour detection and too few weak breast contours. In this paper, we propose an improvement to this method that replaces the Sobel filter with a novel neural network solution to improve breast contour detection based on the shortest path. The proposed solution learns effective representations for the edges between the breasts and the torso wall. We obtain state of the art results on a dataset that was used for developing previous models. Furthermore, we tested these models on a new dataset that contains more variable photographs and show that this new approach shows better generalization capabilities as the previously developed deep models do not perform so well when faced with a different dataset for testing. The main contribution of this paper is to further improve the capabilities of models that perform the objective classification of BCCT aesthetic results automatically by improving upon the current standard technique for detecting breast contours in digital photographs. To that end, the models introduced are simple to train and test on new datasets which makes this approach easily reproducible.

Published
2023
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42. Two-Stage Framework for Faster Semantic Segmentation.

Author

Cruz R, Silva DTE, Gonçalves T, Carneiro D, and Cardoso JS

Abstract

Semantic segmentation consists of classifying each pixel according to a set of classes. Conventional models spend as much effort classifying easy-to-segment pixels as they do classifying hard-to-segment pixels. This is inefficient, especially when deploying to situations with computational constraints. In this work, we propose a framework wherein the model first produces a rough segmentation of the image, and then patches of the image estimated as hard to segment are refined. The framework is evaluated in four datasets (autonomous driving and biomedical), across four state-of-the-art architectures. Our method accelerates inference time by four, with additional gains for training time, at the cost of some output quality.

Published
2023
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43. A CAD system for automatic dysplasia grading on H&E cervical whole-slide images.

Author

Oliveira SP, Montezuma D, Moreira A, Oliveira D, Neto PC, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Pinto IM, and Cardoso JS

Subjects
Female, Humans, Cervix Uteri diagnostic imaging, Cervix Uteri pathology, Hyperplasia pathology, Neoplasm Grading, Uterine Cervical Dysplasia pathology, Uterine Cervical Neoplasms diagnosis, Squamous Intraepithelial Lesions pathology, Carcinoma, Squamous Cell pathology
Abstract

Cervical cancer is the fourth most common female cancer worldwide and the fourth leading cause of cancer-related death in women. Nonetheless, it is also among the most successfully preventable and treatable types of cancer, provided it is early identified and properly managed. As such, the detection of pre-cancerous lesions is crucial. These lesions are detected in the squamous epithelium of the uterine cervix and are graded as low- or high-grade intraepithelial squamous lesions, known as LSIL and HSIL, respectively. Due to their complex nature, this classification can become very subjective. Therefore, the development of machine learning models, particularly directly on whole-slide images (WSI), can assist pathologists in this task. In this work, we propose a weakly-supervised methodology for grading cervical dysplasia, using different levels of training supervision, in an effort to gather a bigger dataset without the need of having all samples fully annotated. The framework comprises an epithelium segmentation step followed by a dysplasia classifier (non-neoplastic, LSIL, HSIL), making the slide assessment completely automatic, without the need for manual identification of epithelial areas. The proposed classification approach achieved a balanced accuracy of 71.07% and sensitivity of 72.18%, at the slide-level testing on 600 independent samples, which are publicly available upon reasonable request., (© 2023. The Author(s).)

Published
2023
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44. Diagnostic and therapeutic approach of central sleep apnea in heart failure - the role of adaptive servo-ventilation. A statement of the Portuguese society of pulmonology and the Portuguese sleep association.

Author

Correia S, Sousa S, Drummond M, Pinto P, Staats R, Brito D, Lousada N, Cardoso JS, and Moita J

Subjects
Humans, Quality of Life, Portugal epidemiology, Stroke Volume, Sleep, Sleep Apnea, Central complications, Sleep Apnea, Central therapy, Heart Failure complications, Heart Failure therapy, Pulmonary Medicine
Abstract

It is known that patients with heart failure (HF) have an increased risk of developing central sleep apnoea (CSA), with Cheyne-Stokes respiration. The development of servo-ventilation aimed to treat CSA and improve the quality of life (QoL) of these patients. A large randomized clinical study, SERVE-HF, was conducted in order to test this theory in patients with HF and reduced ejection fraction (HFrEF). The results from this trial seemed to indicate that, in these patients, there was no beneficial effect of the assisted ventilation in CSA treatment. More surprisingly, an increased rate of all-cause or cardiovascular mortality was observed. This has led to dramatic changes in clinical practice, with decreased frequency of servo-ventilation prescription across Europe, including Portugal, due to changes in the guidelines. However, SERVE-HF was conducted only in severe systolic HF patients with CSA, and caution must be taken when extrapolating these results to HF patients with preserved ejection fraction or CSA patients without HF. The study also showed poor adherence, methodological and statistical gaps, including study design, patient selection, data collection and analysis, treatment adherence, and group crossovers, which have not been discussed in the trial as potential confounding factors and raise several concerns. Moreover, the adaptive servo-ventilation (ASV) device used in SERVE-HF was unable to lower the minimum support pressure below 3 mm H 2 0, and this has been suggested as one of the probable contributing reasons to the excess mortality observed in this study. This limitation has since been solved, and this ASV device is no longer used. This paper describes the results of a Portuguese Task Force on the treatment of central sleep apnoea in patients with chronic HF., Competing Interests: Conflicts of interest JSC has consulted and received speaker fees, or advisory boards' participation fees, or investigational grants for Abbott, AstraZeneca Pharmaceuticals, Bial, Boehringer Ingelheim, Menarini, Merck Serono, Merck Sharp & Dohme, Novartis, Orion, Pfizer, Sanofi, Servier and Vifor. All other authors declare no conflicts of interest., (Copyright © 2022 Sociedade Portuguesa de Pneumologia. Published by Elsevier España, S.L.U. All rights reserved.)

Published
2023
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45. Psidium cattleianum fruit extract prevents systemic alterations in an animal model of type 2 diabetes mellitus: comparison with metformin effects.

Author

Cardoso JS, Cardoso Teixeira F, De Mello JE, Soares De Aguiar MS, Souto Oliveira P, Torchelsen Saraiva J, Vizzotto M, Borelli Grecco F, Lencina CL, Spanevello RM, Tavares RG, and Stefanello FM

Subjects
Animals, Rats, Blood Glucose, Fruit, Hypoglycemic Agents pharmacology, Models, Animal, Diabetes Mellitus, Experimental drug therapy, Diabetes Mellitus, Experimental pathology, Diabetes Mellitus, Type 2 drug therapy, Diabetes Mellitus, Type 2 pathology, Metformin pharmacology, Metformin therapeutic use, Psidium
Abstract

Objective: In this study, we aimed to determine the role of Psidium cattleianum extract (PCE) and compare its effects with those of metformin (Met) in an animal model with type 2 diabetes mellitus (T2DM). Methods: T2DM was induced in rats using a high-fat diet (HFD), followed by a single dose of streptozotocin (STZ). Met and PCE were administered intragastrically once a day throughout the experiment, and their effects on biochemical, inflammatory, oxidative, and histological parameters were evaluated. Results: Met and PCE prevented the increase in serum levels of glucose, total cholesterol (TC), triacylglycerol (TG), very low-density lipoprotein (VLDL) and interleukin-6 (IL-6) induced by T2DM, and restored redox homeostasis in the liver and brain. Met increased the serum levels of anti-inflammatory cytokine and interleukin-10 (IL-10). Furthermore, both treatments restored the liver and pancreas from marked cellular disorganisation, vacuolisation, and necrosis, with PCE being more effective than Met in recovering histological changes. Conclusion: PCE is a promising agent for the prevention of T2DM complications.

Published
2023
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47. The effects of three different low-volume aerobic training protocols on cardiometabolic parameters of type 2 diabetes patients: A randomized clinical trial.

Author

Gentil P, Silva LRBE, Antunes DE, Carneiro LB, de Lira CAB, Batista G, de Oliveira JCM, Cardoso JS, Souza DC, and Rebelo ACS

Subjects
Female, Humans, Male, Blood Pressure physiology, Clinical Protocols, Exercise Test, Respiratory Function Tests, Cardiovascular Diseases, Diabetes Mellitus, Type 2 therapy, Exercise
Abstract

Objective: To compare the effects of different aerobic training protocols on cardiometabolic variables in patients with type 2 diabetes mellitus (T2DM)., Methods: This study was a parallel clinical trial. Fifty-two men and women with T2DM (>40 years) were randomly allocated into three groups, and 44 (22 males/22 females) were included in the final analysis. Exercise intensity was based on the speed corresponding to the maximum oxygen consumption (v V ˙ O 2 max). Moderate intensity continuous training (MICT) involved 14 minutes at 70% of v V ˙ O 2 max; short interval high-intensity interval training (S-HIIT) consisted of 20 bouts of 30 seconds at 100% of V ˙O 2 max with 30 seconds passive recovery; long interval high-intensity training (L-HIIT) consisted of 5 bouts of 2 minutes at 100% of v V ˙ O 2 max with 2 minutes passive recovery. Training protocols were performed on a motorized treadmill two times per week for eight weeks. Glycated hemoglobin (Hb1Ac), total cholesterol, triglycerides, resting systolic blood pressure (SBP), resting diastolic blood pressure (DBP), resting heart rate (resting HR) and maximum oxygen consumption ( V ˙O 2 max) were measured before and after the exercise intervention. The study was registered on the Brazilian clinical trial records (ID: RBR45 4RJGC3)., Results: There was a significant difference between groups for changes on V ˙ O 2 max. Greater increases on V ˙ O 2 max were achieved for L-HIIT (p = 0.04) and S-HIIT (p = 0.01) in comparison to MICT group, with no significant difference between L-HIIT and S-HIIT (p = 0.9). Regarding comparison within groups, there were significant reductions on HbA1c and triglycerides levels only for L-HIIT (p< 0.05). V ˙ O 2 max significantly increased for both L-HIIT (MD = 3.2 ± 1.7 ml/kg/min, p< 0.001) and S-HIIT (MD = 3.4 ± 1.7, p< 0.001). There was a significant reduction on resting SBP for L-HIIT group (MD = -12.07 ± 15.3 mmHg, p< 0.01), but not for S-HIIT and MICT. There were no significant changes from pre- to post-training on fasting glycemia, total cholesterol, HDL, LDL, resting HR and resting DBP for any group (p > 0.05)., Conclusion: Low-volume HIIT promoted greater improvements in cardiorespiratory capacity in comparison with low-volume MICT, independent of the protocols used. There were no other differences between groups. All protocols improved at least one of the variables analyzed; however, the most evident benefits were after the high-intensity protocols, especially L-HIIT., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gentil, Silva, Antunes, Carneiro, de Lira, Batista, Oliveira, Cardoso, Souza and Rebelo.)

Published
2023
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48. Symmetry-based regularization in deep breast cancer screening.

Author

Castro E, Costa Pereira J, and Cardoso JS

Subjects
Female, Humans, Quality of Life, Early Detection of Cancer, Breast Neoplasms diagnostic imaging
Abstract

Breast cancer is the most common and lethal form of cancer in women. Recent efforts have focused on developing accurate neural network-based computer-aided diagnosis systems for screening to help anticipate this disease. The ultimate goal is to reduce mortality and improve quality of life after treatment. Due to the difficulty in collecting and annotating data in this domain, data scarcity is - and will continue to be - a limiting factor. In this work, we present a unified view of different regularization methods that incorporate domain-known symmetries in the model. Three general strategies were followed: (i) data augmentation, (ii) invariance promotion in the loss function, and (iii) the use of equivariant architectures. Each of these strategies encodes different priors on the functions learned by the model and can be readily introduced in most settings. Empirically we show that the proposed symmetry-based regularization procedures improve generalization to unseen examples. This advantage is verified in different scenarios, datasets and model architectures. We hope that both the principle of symmetry-based regularization and the concrete methods presented can guide development towards more data-efficient methods for breast cancer screening as well as other medical imaging domains., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Eduardo Castro reports financial support was provided by Foundation for Science and Technology., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Published
2023
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49. Computer-aided diagnosis through medical image retrieval in radiology.

Author

Silva W, Gonçalves T, Härmä K, Schröder E, Obmann VC, Barroso MC, Poellinger A, Reyes M, and Cardoso JS

Subjects
Humans, Radiography, Radiologists, Diagnosis, Computer-Assisted, Computers, Radiology
Abstract

Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and consequently, to undesired diagnosis mistakes. Decision support systems can be used to prioritize and help radiologists making quicker decisions. In this sense, medical content-based image retrieval systems can be of extreme utility by providing well-curated similar examples. Nonetheless, most medical content-based image retrieval systems work by finding the most similar image, which is not equivalent to finding the most similar image in terms of disease and its severity. Here, we propose an interpretability-driven and an attention-driven medical image retrieval system. We conducted experiments in a large and publicly available dataset of chest radiographs with structured labels derived from free-text radiology reports (MIMIC-CXR-JPG). We evaluated the methods on two common conditions: pleural effusion and (potential) pneumonia. As ground-truth to perform the evaluation, query/test and catalogue images were classified and ordered by an experienced board-certified radiologist. For a profound and complete evaluation, additional radiologists also provided their rankings, which allowed us to infer inter-rater variability, and yield qualitative performance levels. Based on our ground-truth ranking, we also quantitatively evaluated the proposed approaches by computing the normalized Discounted Cumulative Gain (nDCG). We found that the Interpretability-guided approach outperforms the other state-of-the-art approaches and shows the best agreement with the most experienced radiologist. Furthermore, its performance lies within the observed inter-rater variability., (© 2022. The Author(s).)

Published
2022
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50. Electrocardiogram lead conversion from single-lead blindly-segmented signals.

Author

Beco SC, Pinto JR, and Cardoso JS

Subjects
Humans, Electrocardiography, Electrodes, Databases, Factual, Wearable Electronic Devices, Heart Diseases
Abstract

Background: The standard configuration's set of twelve electrocardiogram (ECG) leads is optimal for the medical diagnosis of diverse cardiac conditions. However, it requires ten electrodes on the patient's limbs and chest, which is uncomfortable and cumbersome. Interlead conversion methods can reconstruct missing leads and enable more comfortable acquisitions, including in wearable devices, while still allowing for adequate diagnoses. Currently, methodologies for interlead ECG conversion either require multiple reference (input) leads and/or require input signals to be temporally aligned considering the ECG landmarks., Methods: Unlike the methods in the literature, this paper studies the possibility of converting ECG signals into all twelve standard configuration leads using signal segments from only one reference lead, without temporal alignment (blindly-segmented). The proposed methodology is based on a deep learning encoder-decoder U-Net architecture, which is compared with adaptations based on convolutional autoencoders and label refinement networks. Moreover, the method is explored for conversion with one single shared encoder or multiple individual encoders for each lead., Results: Despite the more challenging settings, the proposed methodology was able to attain state-of-the-art level performance in multiple target leads, and both lead I and lead II seem especially suitable to convert certain sets of leads. In cross-database tests, the methodology offered promising results despite acquisition setup differences. Furthermore, results show that the presence of medical conditions does not have a considerable effect on the method's performance., Conclusions: This study shows the feasibility of converting ECG signals using single-lead blindly-segmented inputs. Although the results are promising, further efforts should be devoted towards the improvement of the methodologies, especially the robustness to diverse acquisition setups, in order to be applicable to cardiac health monitoring in wearable devices and less obtrusive clinical scenarios., (© 2022. The Author(s).)

Published
2022
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