Atrial secondary tricuspid regurgitation: pathophysiology, definition, diagnosis, and treatment.

Atrial secondary tricuspid regurgitation (A-STR) is a distinct phenotype of secondary tricuspid regurgitation with predominant dilation of the right atrium and normal right and left ventricular function. Atrial secondary tricuspid regurgitation occurs most commonly in elderly women with atrial fibrillation and in heart failure with preserved ejection fraction in sinus rhythm. In A-STR, the main mechanism of leaflet malcoaptation is related to the presence of a significant dilation of the tricuspid annulus secondary to right atrial enlargement. In addition, there is an insufficient adaptive growth of tricuspid valve leaflets that become unable to cover the enlarged annular area. As opposed to the ventricular phenotype, in A-STR, the tricuspid valve leaflet tethering is typically trivial. The A-STR phenotype accounts for 10%-15% of clinically relevant tricuspid regurgitation and has better outcomes compared with the more prevalent ventricular phenotype. Recent data suggest that patients with A-STR may benefit from more aggressive rhythm control and timely valve interventions. However, little is mentioned in current guidelines on how to identify, evaluate, and manage these patients due to the lack of consistent evidence and variable definitions of this entity in recent investigations. This interdisciplinary expert opinion document focusing on A-STR is intended to help physicians understand this complex and rapidly evolving topic by reviewing its distinct pathophysiology, diagnosis, and multi-modality imaging characteristics. It first defines A-STR by proposing specific quantitative criteria for defining the atrial phenotype and for discriminating it from the ventricular phenotype, in order to facilitate standardization and consistency in research.

Racial Differences in Age-Related Changes in Left Ventricular Structural and Functional Echocardiographic Measurements Among Healthy Japanese and American Participants　- A Subanalysis of the World Alliance Society of Echocardiography Normal Values Study.

BACKGROUND: Age-related changes in left ventricular (LV) structure and function lower the threshold for the onset of heart failure with preserved ejection fraction (HFpEF). LV parameters change also with race; however, the racial differences in age-related changes in LV parameters with and without adjustment for body mass index (BMI), heart rate (HR), and blood pressure (BP) remain unclear.Methods and Results: We performed a subanalysis of the World Alliance Society of Echocardiography Normal Values Study, an international cross-sectional study that examined normal echocardiographic values in 15 countries. The age-related changes in 2-dimensional echocardiographic derived parameters including LV size, systolic function, and mass, were compared between healthy Japanese (n=227) and healthy White (n=98) and Black (n=69) American participants. In men, age-related changes in all parameters did not differ significantly among races. However, compared with Japanese women, White American women had a smaller body surface area (BSA)-indexed LV volume, BSA-indexed LV internal dimension at end-systole, BSA-indexed LV stroke volume, and LV mass index to BSA, and a larger LV ejection fraction with age, even after adjusting for BMI, HR, and BP. CONCLUSIONS: Age-related changes in LV structure and function, which are important for the pathophysiology of HFpEF, may differ by race. Therefore, future studies examining echocardiographic reference values for each age group in each race are needed.

Reverse Remodeling Effects of Sacubitril-Valsartan: Structural and Functional Optimization in Stage C Heart Failure.

Sacubitril-valsartan, an angiotensin receptor-neprilysin inhibitor, reduces all-cause mortality and the rate of heart failure hospitalizations in patients with heart failure with reduced ejection fraction. This study aimed to elucidate the benefits of initiating sacubitril-valsartan on ventricular remodeling in patients previously optimized on guideline-directed medical therapy. In this prospective, single-arm longitudinal study, 40 patients with heart failure with reduced ejection fraction who were optimized on guideline-directed medical therapy were transitioned to sacubitril-valsartan. The primary end point was the change in left ventricular (LV) volume at 1 year as assessed by 3-dimensional transthoracic echocardiography. Other echocardiographic end points included change in LV-function and change in right ventricular (RV) size and function. The mean age was 55 ± 12 years, and 63% were male. At 1 year, LV end-diastolic volume decreased from 242 ± 71 to 157 ± 57 ml (p <0.001) with a corresponding increase in LV ejection fraction from 32 ± 7% to 44 ± 9% (p <0.001). RV end-diastolic volume decreased from 151 ± 51 to 105 ±45 ml (p <0.001). Although RV ejection fraction did not change (51 ± 8 vs 51 ± 10; p = 0.35), RV global longitudinal strain improved from -14.9 ± 3.4 % to -19.3 ± 4.3% (p <0.001). When added to standard medical therapy for heart failure, sacubitril-valsartan induces significant remodeling of both the right and left ventricles as assessed by 3-dimensional echocardiography.

Defining echocardiographic predictors of outcome in cardiac amyloidosis by subtype.

BACKGROUND: Current echocardiographic risk factors for prognosis in cardiac amyloidosis (CA) do not distinguish between the two main subtypes: transthyretin cardiomyopathy (TTR) and immunoglobulin light chain cardiomyopathy (AL), each of which require distinct diagnostic and therapeutic approaches. Additionally, only traditional parameters have been studied with little data on advanced techniques. Accordingly, we sought to determine whether differences exist in 2D transthoracic echocardiography (2DE) predictors of survival between the CA subtypes using a comprehensive approach. METHODS: 220 patients (72±12 years) with confirmed CA (AL=89, TTR=131) who underwent 2DE at the time of CA diagnosis were enrolled. Left ventricular (LV) dimensions, indexed mass (LVMi), global longitudinal strain (LVGLS), apical-sparing ratio (LVASR), diastology, right ventricular (RV) size and function indices including tricuspid annular systolic excursion (TAPSE), RV free-wall (RVFWS) and global (RVGLS) strain, indexed left (LA) and right atrial volumes (LAVi and RAVi), LA strain (reservoir and booster) and RV systolic pressure (RVSP) were measured. A propensity-score weighted stepwise variable selection Cox proportional hazards model derived from NYHA class and renal impairment status at diagnosis was used to determine the associations between 2DE parameters and mortality specific to CA subtype over a median follow-up of 36-months. RESULTS: After adjusting for age, atrial fibrillation and treatment, parameters associated with survival were RVFWS (p=0.003, HR 1.15, 95% CI[1.053,1.245]) and RVSP (p=0.03, HR 1.03, 95% CI[1.004,1.063]) in AL and LVASR (p=0.007, HR 6.68, 95% CI[1.75,25.492]) and RAVi (p=0.049, HR 1.03, 95% CI[1.000,1.052]) in TTR. CONCLUSIONS: Echocardiographic prognosticators for survival are specific to cardiac amyloid subtype. These results potentially provide information critical for clinical decision-making and follow-up in these patients.

Multimodality Cardiac Imaging and the Imaging Workforce in the United States: Diversity, Disparities, and Future Directions.

Innovations in cardiac imaging have fundamentally advanced the understanding and treatment of cardiovascular disease. These advances in noninvasive cardiac imaging have also expanded the role of the cardiac imager and dramatically increased the demand for imagers who are cross-trained in multiple modalities. However, we hypothesize that there is significant variation in the availability of cardiac imaging expertise and a disparity in the adoption of advanced imaging technologies across the United States. To evaluate this, we have brought together the leaders of cardiovascular imaging societies, imaging trainees, as well as collaborated with national imaging accreditation commissions and imaging certification boards to assess the state of cardiac imaging and the diversity of the imaging workforce in the United States. Aggregate data confirm the presence of critical gaps, such as limited access to imaging and imaging expertise in rural communities, as well as disparities in the imaging workforce, notably among women and underrepresented minorities. Based on these results, we have proposed solutions to promote and maintain a robust and diverse community of cardiac imagers and improve equity and accessibility for cardiac imaging technologies.

Echocardiographic Detection of Regional Wall Motion Abnormalities Using Artificial Intelligence Compared to Human Readers.

BACKGROUND: Although regional wall motion abnormality (RWMA) detection is foundational to transthoracic echocardiography, current methods are prone to interobserver variability. We aimed to develop a deep learning (DL) model for RWMA assessment and compare it to expert and novice readers. METHODS: We used 15,746 transthoracic echocardiography studies-including 25,529 apical videos-which were split into training, validation, and test datasets. A convolutional neural network was trained and validated using apical 2-, 3-, and 4-chamber videos to predict the presence of RWMA in 7 regions defined by coronary perfusion territories, using the ground truth derived from clinical transthoracic echocardiography reports. Within the test cohort, DL model accuracy was compared to 6 expert and 3 novice readers using F1 score evaluation, with the ground truth of RWMA defined by expert readers. Significance between the DL model and novices was assessed using the permutation test. RESULTS: Within the test cohort, the DL model accurately identified any RWMA with an area under the curve of 0.96 (0.92-0.98). The mean F1 scores of the experts and the DL model were numerically similar for 6 of 7 regions: anterior (86 vs 84), anterolateral (80 vs 74), inferolateral (83 vs 87), inferoseptal (86 vs 86), apical (88 vs 87), inferior (79 vs 81), and any RWMA (90 vs 94), respectively, while in the anteroseptal region, the F1 score of the DL model was lower than the experts (75 vs 89). Using F1 scores, the DL model outperformed both novices 1 (P = .002) and 2 (P = .02) for the detection of any RWMA. CONCLUSIONS: Deep learning provides accurate detection of RWMA, which was comparable to experts and outperformed a majority of novices. Deep learning may improve the efficiency of RWMA assessment and serve as a teaching tool for novices.

Limitations of apical sparing pattern in cardiac amyloidosis: a multicentre echocardiographic study.

AIMS: Although impaired left ventricular (LV) global longitudinal strain (GLS) with apical sparing is a feature of cardiac amyloidosis (CA), its diagnostic accuracy has varied across studies. We aimed to determine the ability of apical sparing ratio (ASR) and most common echocardiographic parameters to differentiate patients with confirmed CA from those with clinical and/or echocardiographic suspicion of CA but with this diagnosis ruled out. METHODS AND RESULTS: We identified 544 patients with confirmed CA and 200 controls (CTRLs) as defined above (CTRL patients). Measurements from transthoracic echocardiograms were performed using artificial intelligence software (Us2.AI, Singapore) and audited by an experienced echocardiographer. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance and optimal cut-offs for the differentiation of CA patients from CTRL patients. Additionally, a group of 174 healthy subjects (healthy CTRL) was included to provide insight on how patients and healthy CTRLs differed echocardiographically. LV GLS was more impaired (-13.9 ± 4.6% vs. -15.9 ± 2.7%, P < 0.0005), and ASR was higher (2.4 ± 1.2 vs. 1.7 ± 0.9, P < 0.0005) in the CA group vs. CTRL patients. Relative wall thickness and ASR were the most accurate parameters for differentiating CA from CTRL patients [area under the curve (AUC): 0.77 and 0.74, respectively]. However, even with the optimal cut-off of 1.67, ASR was only 72% sensitive and 66% specific for CA, indicating the presence of apical sparing in 32% of CTRL patients and even in 6% healthy subjects. CONCLUSION: Apical sparing did not prove to be a CA-specific biomarker for accurate identification of CA, when compared with clinically similar CTRLs with no CA.

Added value of 3D echocardiography in the diagnosis and prognostication of patients with right ventricular dysfunction.

Recent inroads into percutaneous-based options for the treatment of tricuspid valve disease has brought to light how little we know about the behavior of the right ventricle in both health and disease and how incomplete our assessment of right ventricular (RV) physiology and function is using current non-invasive technology, in particular echocardiography. The purpose of this review is to provide an overview of what three-dimensional echocardiography (3DE) can offer currently to enhance RV evaluation and what the future may hold if we continue to improve the 3D evaluation of the right heart.

Optimización de la interpretación de ecocardiogramas con la utilización de Machine Learning en el estudio WASE / Optimizing Echocardiograms Interpretation with the Application of Machine Learning in the WASE Study

RESUMEN Introducción: El número creciente de estudios ecocardiográficos y la necesidad de cumplir rigurosamente con las recomendaciones de guías internacionales de cuantificación, ha llevado a que los cardiólogos deban realizar tareas sumamente extensas y repetitivas, como parte de la interpretación y análisis de cantidades de información cada vez más abrumadoras. Novedosas técnicas de machine learning (ML), diseñadas para reconocer imágenes y realizar mediciones en las vistas adecuadas, están siendo cada vez más utilizadas para responder a esta necesidad evidente de automatización de procesos. Objetivos: Nuestro objetivo fue evaluar un modelo alternativo de interpretación y análisis de estudios ecocardiográficos, basado fundamentalmente en la utilización de software de ML, capaz de identificar y clasificar vistas y realizar mediciones estandarizadas de forma automática. Material y métodos: Se utilizaron imágenes obtenidas en 2000 sujetos normales, libres de enfermedad, de los cuales 1800 fueron utilizados para desarrollar los algoritmos de ML y 200 para su validación posterior. Primero, una red neuronal convolucional fue desarrollada para reconocer 18 vistas ecocardiográficas estándar y clasificarlas de acuerdo con 8 grupos (stacks) temáticos. Los resultados de la identificación automática fueron comparados con la clasificación realizada por expertos. Luego, algoritmos de ML fueron desarrollados para medir automáticamente 16 parámetros de eco Doppler de evaluación clínica habitual, los cuales fueron comparados con las mediciones realizadas por un lector experto. Finalmente, comparamos el tiempo necesario para completar el análisis de un estudio ecocardiográfico con la utilización de métodos manuales convencionales, con el tiempo necesario con el empleo del modelo que incorpora ML en la clasificación de imágenes y mediciones ecocardiográficas iniciales. La variabilidad inter e intraobservador también fue analizada. Resultados: La clasificación automática de vistas fue posible en menos de 1 segundo por estudio, con una precisión de 90 % en imágenes 2D y de 94 % en imágenes Doppler. La agrupación de imágenes en stacks tuvo una precisión de 91 %, y fue posible completar dichos grupos con las imágenes necesarias en 99% de los casos. La concordancia con expertos fue excelente, con diferencias similares a las observadas entre dos lectores humanos. La incorporación de ML en la clasificación y medición de imágenes ecocardiográficas redujo un 41 % el tiempo de análisis y demostró menor variabilidad que la metodología de interpretación convencional. Conclusión: La incorporación de técnicas de ML puede mejorar significativamente la reproducibilidad y eficiencia de las interpretaciones y mediciones ecocardiográficas. La implementación de este tipo de tecnologías en la práctica clínica podría resultar en reducción de costos y aumento en la satisfacción del personal médico.

ABSTRACT Background: The growing number of echocardiographic tests and the need for strict adherence to international quantification guidelines have forced cardiologists to perform highly extended and repetitive tasks when interpreting and analyzing increasingly overwhelming amounts of data. Novel machine learning (ML) techniques, designed to identify images and perform measurements at relevant visits, are becoming more common to meet this obvious need for process automation. Objectives: Our objective was to evaluate an alternative model for the interpretation and analysis of echocardiographic tests mostly based on the use of ML software in order to identify and classify views and perform standardized measurements automatically. Methods: Images came from 2000 healthy subjects, 1800 of whom were used to develop ML algorithms and 200 for subsequent validation. First, a convolutional neural network was developed in order to identify 18 standard echocardiographic views and classify them based on 8 thematic groups (stacks). The results of automatic identification were compared to classification by experts. Later, ML algorithms were developed to automatically measure 16 Doppler scan parameters for regular clinical evaluation, which were compared to measurements by an expert reader. Finally, we compared the time required to complete the analysis of an echocardiographic test using conventional manual methods with the time needed when using the ML model to classify images and perform initial echocardiographic measurements. Inter- and intra-observer variability was also analyzed. Results: Automatic view classification was possible in less than 1 second per test, with a 90% accuracy for 2D images and a 94% accuracy for Doppler scan images. Stacking images had a 91% accuracy, and it was possible to complete the groups with any necessary images in 99% of cases. Expert agreement was outstanding, with discrepancies similar to those found between two human readers. Applying ML to echocardiographic imaging classification and measurement reduced time of analysis by 41% and showed lower variability than conventional reading methods. Conclusion: Application of ML techniques may significantly improve reproducibility and efficiency of echocardiographic interpretations and measurements. Using this type of technologies in clinical practice may lead to reduced costs and increased medical staff satisfaction.

Regresión de amiloidosis cardiaca tras un trasplante autólogo de células progenitoras en pacientes con hallazgos atípicos en la resonancia magnética / Regression of cardiac amyloidosis following autologous stem cell transplant in patients with atypical magnetic resonance imaging findings

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Deformação miocárdica pelo speckle tracking na cardiomiopatia dilatada grave / Myocardial deformation by speckle tracking in severe dilated cardiomyopathy

FUNDAMENTO: A alta e crescente prevalência de Cardiomiopatia Dilatada (CMD) representa sério problema de saúde pública. Novas tecnologias vêm sendo utilizadas objetivando diagnósticos mais sofisticados, que melhorem a abordagem terapêutica. Nesse cenário, o Speckle Tracking (STE) utiliza marcadores miocárdicos naturais para analisar a deformação sistólica do Ventrículo Esquerdo (VE). OBJETIVO: Mensurar o strain transmural longitudinal global (SG) do VE através do STE em pacientes com CMD grave, comparando os resultados com indivíduos normais e com parâmetros ecocardiográficos consagrados para análise da função sistólica do VE, validando o método nessa população. MÉTODOS: Foram estudados 71 pacientes com CMD grave, (53 ± 12a, 72% homens) e 20 controles (30 ± 8a, 45% homens). Foram obtidos os volumes e a FEVE pela ecocardiografia bi e tridimensional, parâmetros do Doppler, Doppler tecidual e o SG pelo STE. RESULTADOS: Comparados ao grupo controle, os volumes do VE foram maiores no grupo CMD; entretanto, a FEVE e velocidade de pico da onda E foram menores neste último. O índice de performance miocárdica foi maior entre os pacientes. As velocidades do miocárdio pelo Doppler tecidual (S', e', a') foram consideravelmente menores e a relação E/e' foi maior no grupo CMD. O SG apresentou-se diminuído no grupo CMD (-5,5% ± 2,3%), em relação aos controles (-14,0% ± 1,8%). CONCLUSÃO: No presente estudo, o SG foi significativamente menor nos pacientes com CMD grave, abrindo novas perspectivas para abordagens terapêuticas nessa população específica.

BACKGROUND: The high and increasing prevalence of Dilated Cardiomyopathy (DCM) represents a serious public health problem. New technologies are being used aiming at more accurate diagnoses in order to improve therapeutic approach. In this scenario, speckle tracking echocardiography (STE) uses natural myocardial markers to analyze the systolic deformation of the left ventricle (LV). OBJECTIVE: To measure the longitudinal transmural global strain (GS) of the LV through STE in patients with severe DCM, comparing the results with normal individuals and with echocardiographic parameters established for the analysis of LV systolic function, validating the method in this population. METHODS: We studied 71 patients with severe DCM (53 ± 12 years, 72% men) and 20 controls (30 ± 8 years, 45% men). We obtained LV volumes and ejection fraction by two and three-dimensional echocardiography, Doppler parameters, tissue Doppler and GS was obtained by STE. RESULTS: Compared to controls, LV volumes were higher in the DCM group; however, LVEF and peak velocity of E wave were lower in the latter. The myocardial performance index was higher among patients. Myocardial velocities at the tissue Doppler (S', e', a') were significantly lower and E/e' ratio was higher in the DCM group. The GS was decreased in the DCM group (-5.5% ± 2.3%) when compared to controls (-14.0% ± 1.8%). CONCLUSION: In this study, GS was significantly lower in patients with severe DCM, bringing new perspectives for therapeutic approaches in this specific population.

Calcificación caseosa del anillo mitral, un raro hallazgo ecocardiográfico / Caseous calcification of the mitral annulus, a rare echocardiographic finding

La calcificación caseosa del anillo mitral, también conocida como «caseoma», es un raro hallazgo ecocardiográfico. Debido a su curso asintomático en la mayoría de los casos, el diagnóstico se produce de forma casual. Su origen se encuentra en la degeneración caseosa del material interior de la calcificación del anillo mitral. El aspecto ecocardiográfico es el de una masa redondeada con un área central ecolucente localizada preferentemente en la base del velo posterior, que se puede confundir con tumores o abscesos cardiacos. Presentamos 3 casos en los que se constató dicha afección; en uno de ellos se precisó extracción quirúrgica de la masa para alcanzar el diagnóstico final (AU)

Caseous calcification of mitral annulus, also known as caseoma, is a rare echocardiographic finding. Because it often presents asymptomatically, the diagnosis is usually incidental. The condition arises due to caseous degeneration in the internal material of the mitral annulus calcification. On echocardiography, the calcification is a round mass with a central echolucent area, typically located at the base of the posterior leaflet, and can be mistaken for cardiac tumors or abscesses. We describe 3 patients with the condition, one of whom required surgical removal of the mass to obtain a definitive diagnosis (AU)

Ecocardiografía tridimensional en la evaluación de las válvulas protésicas / No disponible

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