Left Atrial Chamber and Appendage Function After Internal Atrial Defibrillation: A Prospective and Serial Transesophageal Echocardiographic Study

AbstractObjectives. The purpose of this prospective study was to assess left atrial chamber and appendage function after internal atrial defibrillation of atrial fibrillation and to evaluate the time course of recovery.Background. External cardioversion of atrial fibrillation may result in left atrial appendage dysfunction (“stunning”) and may promote thrombus formation. In contrast to external cardioversion, internal atrial defibrillation utilizes lower energies; however, it is unknown whether the use of lower energies may avoid stunning of the left atrial appendage.Methods. Transesophageal and transthoracic echocardiography were performed in 20 patients 24 h before and 1 and 7 days after internal atrial defibrillation to assess both left atrial chamber and appendage function. Transthoracic echocardiography was again performed 28 days after internal atrial defibrillation to assess left atrial function. The incidence and degree of spontaneous echo contrast accumulation (range 1+ to 4+) was noted, and peak emptying velocities of the left atrial appendage were measured before and after internal atrial defibrillation. To determine left atrial mechanical function, peak A wave velocities were obtained from transmitral flow velocity profiles.Results. Sinus rhythm was restored in all patients. The mean ± SD peak A wave velocities increased gradually after cardioversion, from 0.47 ± 0.16 m/s at 24 h to 0.61 ± 0.13 m/s after 7 days (p < 0.05) and 0.63 ± 0.13 m/s after 4 weeks. Peak emptying velocities of the left atrial appendage were 0.37 ± 0.16 m/s before internal atrial defibrillation, decreased significantly after internal atrial defibrillation to 0.23 ± 0.1 m/s at 24 h (p < 0.01) and then recovered to 0.49 ± 0.23 m/s (p < 0.01) after 7 days. The corresponding values for the degree of spontaneous echo contrast were 1.2 ± 1.2 before internal atrial defibrillation versus 2.0 ± 1.0 (p < 0.01) and 1.1 ± 1.3 (p < 0.01) 1 and 7 days after cardioversion, respectively. One patient developed a new thrombus in the left atrial appendage, and another had a thromboembolic event after internal atrial defibrillation.Conclusions. Internal atrial defibrillation causes depressed left atrial chamber and appendage function and may result in the subacute accumulation of spontaneous echo contrast and development of new thrombi after cardioversion. These findings have important clinical implications for anticoagulation therapy before and after low energy internal atrial defibrillation in patients with atrial fibrillation.(J Am Coll Cardiol 1997;29:131–8)

Analysis of chronic aortic regurgitation by 2D and 3D echocardiography and cardiac MRI

Purpose: The study compares the feasibility of the quantitative volumetric and semi-quantitative approach for quantification of chronic aortic regurgitation (AR) using different imaging modalities. Methods: Left ventricular (LV) volumes, regurgitant volumes (RVol) and regurgitant fractions (RF) were assessed retrospectively by 2D, 3D echocardiography and cMRI in 55 chronic AR patients. Semi-quantitative parameters were assessed by 2D echocardiography. Results: 22 (40%) patients had mild, 25 (46%) moderate and 8 (14%) severe AR. The quantitative volumetric approach was feasible using 2D, 3D echocardiography and cMRI, whereas the feasibility of semi-quantitative parameters varied considerably. LV volume (LVEDV, LVESV, SVtot) analyses showed good correlations between the different imaging modalities, although significantly increased LV volumes were assessed by cMRI. RVol was significantly different between 2D/3D echocardiography and 2D echocardiography/cMRI but was not significantly different between 3D echocardiography/cMRI. RF was not statistically different between 2D echocardiography/cMRI and 3D echocardiography/cMRI showing poor correlations (r < 0.5) between the different imaging modalities. For AR grading by RF, moderate agreement was observed between 2D/3D echocardiography and 2D echocardiography/cMRI and good agreement was observed between 3D echocardiography/cMRI. Conclusion: Semi-quantitative parameters are difficult to determine by 2D echocardiography in clinical routine. The quantitative volumetric RF assessment seems to be feasible and can be discussed as an alternative approach in chronic AR. However, RVol and RF did not correlate well between the different imaging modalities. The best agreement for grading of AR severity by RF was observed between 3D echocardiography and cMRI. LV volumes can be verified by different approaches and different imaging modalities

Myocardial Work Assessment for the Prediction of Prognosis in Advanced Heart Failure

Objectives: The aim of this study was to investigate whether echocardiographic assessment of myocardial work is a predictor of outcome in advanced heart failure. Background: Global work index (GWI) and global constructive work (GCW) are calculated bymeans of speckle tracking, blood pressuremeasurement, and a normalized reference curve. Their prognostic value in advanced heart failure is unknown. Methods: Cardiopulmonary exercise testing and echocardiography with assessment of GWI and GCW was performed in patients with advanced heart failure caused by ischemic heart disease or dilated cardiomyopathy (n = 105). They were then followed up repeatedly. The combined endpoint was all-cause death, implantation of a left ventricular assist device, or heart transplantation. Results: The median patient age was 54 years (interquartile range [IQR]: 48–59.9). The mean left ventricular ejection fraction was 27.8 ± 8.2%, the median NT-proBNP was 1,210 pg/ml (IQR: 435–3,696). The mean GWI was 603 ± 329 mmHg% and the mean GCW was 742 ± 363 mmHg%. The correlation between peak oxygen uptake and GWI as well as GCW was strongest in patients with ischemic cardiomyopathy (r = 0.56, p = 0.001 and r = 0.53, p = 0.001, respectively). The median follow-up was 16 months (IQR: 12–18.5). Thirty one patients met the combined endpoint: Four patients died, eight underwent transplantation, and 19 underwent implantation of a left ventricular assist device. In themultivariate Cox regression analysis, only NYHA class, NT-proBNP and GWI (hazard ratio [HR] for every 50 mmHg%: 0.85; 95% CI: 0.77–0.94; p = 0.002) as well as GCW (HR for every 50 mmHg%: 0.86; 95% CI: 0.79–0.94; p = 0.001) were identified as independent predictors of the endpoint. The cut-off value for predicting the outcome was 455 mmHg% for GWI (AUC: 0.80; p < 0.0001; sensitivity 77.4%; specificity 71.6%) and 530 mmHg% for GCW (AUC: 0.80; p < 0.0001; sensitivity 74.2%; specificity 78.4%). Conclusions: GWI and GCW are powerful predictors of outcome in patients with advanced heart failure

Triage 4.0:On Death Algorithms and Technological Selection. Is Today’s Data- Driven Medical System Still Compatible with the Constitution?

Health data bear great promises for a healthier and happier life, but they also make us vulnerable. Making use of millions or billions of data points, Machine Learning (ML) and Artificial Intelligence (AI) are now creating new benefits. For sure, harvesting Big Data can have great potentials for the health system, too. It can support accurate diagnoses, better treatments and greater cost effectiveness. However, it can also have undesirable implications, often in the sense of undesired side effects, which may in fact be terrible. Examples for this, as discussed in this article, are discrimination, the mechanisation of death, and genetic, social, behavioural or technological selection, which may imply eugenic effects or social Darwinism. As many unintended effects become visible only after years, we still lack sufficient criteria, long-term experience and advanced methods to reliably exclude that things may go terribly wrong. Handing over decision-making, responsibility or control to machines, could be dangerous and irresponsible. It would also be in serious conflict with human rights and our constitution

Myocardial Work Assessment for the Prediction of Prognosis in Advanced Heart Failure

Objectives: The aim of this study was to investigate whether echocardiographic assessment of myocardial work is a predictor of outcome in advanced heart failure.Background: Global work index (GWI) and global constructive work (GCW) are calculated by means of speckle tracking, blood pressure measurement, and a normalized reference curve. Their prognostic value in advanced heart failure is unknown.Methods: Cardiopulmonary exercise testing and echocardiography with assessment of GWI and GCW was performed in patients with advanced heart failure caused by ischemic heart disease or dilated cardiomyopathy (n = 105). They were then followed up repeatedly. The combined endpoint was all-cause death, implantation of a left ventricular assist device, or heart transplantation.Results: The median patient age was 54 years (interquartile range [IQR]: 48–59.9). The mean left ventricular ejection fraction was 27.8 ± 8.2%, the median NT-proBNP was 1,210 pg/ml (IQR: 435–3,696). The mean GWI was 603 ± 329 mmHg% and the mean GCW was 742 ± 363 mmHg%. The correlation between peak oxygen uptake and GWI as well as GCW was strongest in patients with ischemic cardiomyopathy (r = 0.56, p = 0.001 and r = 0.53, p = 0.001, respectively). The median follow-up was 16 months (IQR: 12–18.5). Thirty one patients met the combined endpoint: Four patients died, eight underwent transplantation, and 19 underwent implantation of a left ventricular assist device. In the multivariate Cox regression analysis, only NYHA class, NT-proBNP and GWI (hazard ratio [HR] for every 50 mmHg%: 0.85; 95% CI: 0.77–0.94; p = 0.002) as well as GCW (HR for every 50 mmHg%: 0.86; 95% CI: 0.79–0.94; p = 0.001) were identified as independent predictors of the endpoint. The cut-off value for predicting the outcome was 455 mmHg% for GWI (AUC: 0.80; p &lt; 0.0001; sensitivity 77.4%; specificity 71.6%) and 530 mmHg% for GCW (AUC: 0.80; p &lt; 0.0001; sensitivity 74.2%; specificity 78.4%).Conclusions: GWI and GCW are powerful predictors of outcome in patients with advanced heart failure

Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier

This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.</jats:p