Three-dimensional myocardial strain estimation from volumetric ultrasound: experimental validation in an animal model

Although real-time three-dimensional echocardiography has the potential to allow for more accurate assessment of global and regional ventricular dynamics compared to the more traditional two-dimensional ultrasound examinations, it still requires rigorous testing and validation against other accepted techniques should it breakthrough as a standard examination in routine clinical practice. Very few studies have looked at a validation of regional functional indices in an in-vivo context. The aim of the present study therefore was to validate a previously proposed 3D strain estimation-method based on elastic registration of subsequent volumes on a segmental level in an animal model. Volumetric images were acquired with a GE Vivid7 ultrasound system in five open-chest sheep instrumented with ultrasonic microcrystals. Radial (epsilon(RR)), longitudinal (epsilon(LL)) and circumferential strain (epsilon(CC)) were estimated during four stages: at rest, during esmolol and dobutamine infusion, and during acute ischemia. Moderate correlations for epsilon(LL) (r=0.63; p<0.01) and epsilon(CC) (r=0.60; p=0.01) were obtained, whereas no significant radial correlation was found. These findings are comparable to the performance of the current state-of-the-art commercial 3D speckle tracking methods

Elastic image registration versus speckle tracking for 2-D myocardial motion estimation: a direct comparison in vivo

Despite the availability of multiple solutions for assessing myocardial strain by ultrasound, little is currently known about the relative performance of the different methods. In this study, we sought to contrast two strain estimation techniques directly (speckle tracking and elastic registration) in an in vivo setting by comparing both to a gold standard reference measurement. In five open-chest sheep instrumented with ultrasonic microcrystals, 2-D images were acquired with a GE Vivid7 ultrasound system. Radial (epsilon(RR)) , longitudinal (epsilon(LL)) , and circumferential strain (epsilon(CC)) were estimated during four inotropic stages: at rest, during esmolol and dobutamine infusion, and during acute ischemia. The correlation of the end-systolic strain values of a well-validated speckle tracking approach and an elastic registration method against sonomicrometry were comparable for epsilon(LL) (r = 0.70 versus r = 0.61, respectively; p = 0.32) and epsilon(CC) (r = 0.73 versus r = 0.80 respectively; p = 0.31). However, the elastic registration method performed considerably better for epsilon(RR) (r = 0.64 versus r = 0.85 respectively; p = 0.09). Moreover, the bias and limits of agreement with respect to the reference strain estimates were statistically significantly smaller in this direction (p < 0.001). This could be related to regularization which is imposed during the motion estimation process as opposed to an a posteriori regularization step in the speckle tracking method. Whether one method outperforms the other in detecting dysfunctional regions remains the topic of future research

Automated quantification of mitral valve tenting volume in functional mitral regurgitation by three‐dimensional echocardiography

Background: Tenting of the mitral leaflets is a major pathophysiological factor contributing to functional mitral regurgitation (FMR). A novel software tool allows automated quantification of the tenting volume (TnV) by 3D transesophageal echocardiography (TEE). The aims of this study are to investigate the correlations of biometric patient characteristics with the TnV and whether a threshold value for the diagnosis of a moderate or severe FMR can be calculated for the TnV. Methods: This explorative and hypothesis-generating study analyzed the TnV of the mitral valve obtained by clinically indicated TEE. The mid-systolic, threefold calculated and averaged TnV from 80 patients with no or mild FMR and 27 patients with moderate or severe FMR was determined using the TomTec 4D MV Assessment tool. Results: The TnV correlated significantly with the body size (r = 0.341), the weight (r = 0.272), and the body surface area (r = 0.320). After the adjustment to the body size, a threshold value of 1.25 cm(3)/m was determined for the TnV by using a receiver-operating characteristic curve. This value distinguished moderate to severe from none to mild FMR with a sensitivity of 85% and a specificity of 71%. The intra-observer variability and inter-observer variability were determined to be 0.96 and 0.85, respectively. Conclusions: Automated assessment of TnV has the potential to support the diagnostic evaluation of FMR. Further studies are needed to validate this result, detect additional factors influencing the size of the TnV, and determine further thresholds for any degree of FMR

Fast left ventricle tracking using localized anatomical affine optical flow

Fast left ventricle tracking using localized anatomical affine optical flowIn daily clinical cardiology practice, left ventricle (LV) global and regional function assessment is crucial for disease diagnosis, therapy selection, and patient follow-up. Currently, this is still a time-consuming task, spending valuable human resources. In this work, a novel fast methodology for automatic LV tracking is proposed based on localized anatomically constrained affine optical flow. This novel method can be combined to previously proposed segmentation frameworks or manually delineated surfaces at an initial frame to obtain fully delineated datasets and, thus, assess both global and regional myocardial function. Its feasibility and accuracy were investigated in 3 distinct public databases, namely in realistically simulated 3D ultrasound, clinical 3D echocardiography, and clinical cine cardiac magnetic resonance images. The method showed accurate tracking results in all databases, proving its applicability and accuracy for myocardial function assessment. Moreover, when combined to previous state-of-the-art segmentation frameworks, it outperformed previous tracking strategies in both 3D ultrasound and cardiac magnetic resonance data, automatically computing relevant cardiac indices with smaller biases and narrower limits of agreement compared to reference indices. Simultaneously, the proposed localized tracking method showed to be suitable for online processing, even for 3D motion assessment. Importantly, although here evaluated for LV tracking only, this novel methodology is applicable for tracking of other target structures with minimal adaptations.The authors acknowledge funding support from FCT - Fundacao para a Ciência e a Tecnologia, Portugal, and the European Social Found, European Union, through the Programa Operacional Capital Humano (POCH) in the scope of the PhD grants SFRH/BD/93443/2013 (S. Queiros) and SFRH/BD/95438/2013 (P. Morais), and by the project ’PersonalizedNOS (01-0145-FEDER-000013)’ co-funded by Programa Operacional Regional do Norte (Norte2020) through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2)

The Global Parkinson's Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Defining the causes of sporadic Parkinson’s disease in the global Parkinson’s genetics program (GP2)

\ua9 2023, Springer Nature Limited. The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease

\ua9 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

Defining the causes of sporadic Parkinson’s disease in the global Parkinson’s genetics program (GP2)

The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

A Novel Echocardiographic Index for the Estimation of Left Ventricular Contractility

The goal of this Ph.D. project was to propose and clinically validate anon-invasive echocardiographic estimate of intrinsic left ventricular (LV) contractility. Indeed, such an index is currently missing in clinical routine, even though it could be very useful in multiple clinical settings, as for example in monitoring heart failure treatment, in selectingpatients with valvular heart disease for surgical treatment or in detecting early cardiac damage in patients with systemic disease. Before describing the studies that were undertaken to reach this goal, thebasic mechanisms of cardiac contraction, needed to understand the concept of intrinsic cardiac contractility, are described and a short overview of the main contractility parameters proposed so far is given in the first part of this thesis manuscript. Besides that, the general principles and possible clinical applications of the echocardiographic myocardialdeformation imaging methods that were employed in all the studies of this project are explained. In the second part of the thesis a novel non-invasive echocardiographic estimate of intrinsic left ventricular contractility is described. This index is based on the dependency of regional systolic LV strain (SS) on late diastolic stretch (preS), which was measured as a relative change of LV segmental length during atrial contraction from adapted myocardial deformation imaging curves. In analogy to the Frank Starling concept and to the experimental studies, the preS SS relationship got steeper with a pharmacologically induced increase of LV contractility, flattened after the exposure to a cardiotoxic drug and remained the same during the preload induced increase of LV systolic function. We have also found that in spite of increasing late diastolic stretch in the elderly, the slope of this relationship does not change with age. Further on, it was shown that the intraventricular distribution of passive late diastolic stretch is very consistent between individuals. As the flatter LV walls were stretched more than the more spherical walls, we have suggested that regional heterogeneity of passive LV deformation is related to the regional differences of wall stress. Our results have also shown that the spatial intraventricular distribution of regional systolic strain is very similar to that of passive diastolic stretch, which confirmed that a major part of intraventricular systolic strain variability could be explained by segmental differences in diastolic stretch as a direct consequence of the Frank Starling mechanism. Last but not least, we wanted to extend the stretch-strain methodology from TDI to a more widely used 2D speckle tracking (ST) and to the novel and very promising 3D strain estimation (SE) method. Hereto, a clinical validation of the chosen 3DSE was performed first. In this study 3DSE was compared to the widely clinically accepted 2DST method. After showing good agreement between global and fair agreement between regional strain values obtained with those two techniques, aseparate pilot study was performed to test the suitability of the 2D and3D myocardial deformation imaging approaches for the extraction of the stretch strain relationship. With both of those techniques individual longitudinal preS SS relationships obtained in healthy LVs at rest were similar to what we have previously observed with TDI. However, the slopes and intercepts of circumferential stretch strain relationships were widely spread. Only averaging the segmental deformation values improved the correlation between preS and SS and gave the same regression equation as observed with the longitudinal strain component. We attributed this to the high intramural gradient of the circumferential strain component, and concluded that differently from the longitudinal, the circumferential stretch strain relationship is not suitable for individual use. Of course, whether, 2D and 3D derived longitudinal stretch strain is capable to detect changes in global LV contractility still remains to be tested in the future. Besides that, an experimental validation study, which would also reveal the effect of afterload on this index remains to be performed in the future.Abstract ..........................................................................................................................15 Part I ..........................................................................................................................17 Chapter 1. General introduction and outline of the doctoral thesis...................................17 1.1. General introduction.........................................................................................17 1.2. Outline of the doctoral thesis ...........................................................................18 Chapter 2. The Heart and Its Contractility ........................................................................21 2.1. Introduction ......................................................................................................21 2.2. Cardiac structure and contraction.....................................................................21 2.2.1. Gross cardiac anatomy .....................................................................................21 2.2.2. Cardiomyocyte structure and cross-bridge cycle .............................................22 2.2.3. The cardiac cycle..............................................................................................24 2.3. Modulation of cardiac systolic performance....................................................27 2.3.1. Modulation of contractility...............................................................................27 2.3.2. Preload dependent increase in LV systolic function ........................................29 2.4. Quantification of LV systolic function and LV contractility ...........................31 2.4.1. Measuring LV systolic function.......................................................................32 2.4.2. Measuring LV contractility ..............................................................................34 2.5. Conclusion........................................................................................................37 Chapter 3. Myocardial deformation imaging with tissue Doppler imaging and 2D speckle tracking: technical details and clinical applications.............................................................39 3.1. Introduction ......................................................................................................39 3.2. Myocardial deformation...................................................................................40 3.2.1. The principles of myocardial deformation .......................................................40 3.2.2. Myocardial deformation imaging.....................................................................43 3.3. Clinical applications.........................................................................................49 3.3.1. Normal values ..................................................................................................49 10 3.3.2. Clinical applications.........................................................................................50 3.4. Conclusion........................................................................................................55 Chapter 4. Current state of 3D myocardial strain estimation using echocardiography .....57 4.1. Introduction ......................................................................................................57 4.2. 3D strain estimation approaches ......................................................................58 4.2.1. General workflow.............................................................................................58 4.2.2. Block-matching ................................................................................................58 4.2.3. Elastic registration ...........................................................................................62 4.2.4 Model-based approach .....................................................................................63 4.3. Validation.........................................................................................................65 4.3.1. Validation on simulated models.......................................................................65 4.3.2. Validation in an in-vitro experimental setting..................................................67 4.3.3. Validation in an in-vivo experimental setting ..................................................67 4.4. Comparison against other techniques and current applications of 3DSE in a clinical setting...................................................................................................................69 4.4.1. 3DSE for the estimation of global LV function ...............................................69 4.4.2. 3DSE for the estimation of regional LV function ............................................70 4.4.3. Radial strain estimates with 3DSE...................................................................71 4.4.4. Reproducibility of 3DSE measurements..........................................................71 4.5. Clinical potential of 3DSE ...............................................................................72 4.6. Current limitations of 3DSE.............................................................................72 4.6.1. Feasibility and image quality ...........................................................................74 4.6.2. Inter-vendor dependency of strain values ........................................................75 4.6.3. Temporal resolution .........................................................................................75 4.6.4. Strain rate estimation........................................................................................76 4.6.5. Spatial resolution..............................................................................................76 4.7. Future developments and conclusions..............................................................77 4.8. Appendix 1 - Radial strain estimation..............................................................78 Part II ..........................................................................................................................81 Chapter 5. The slope of the segmental stretch-strain relationship as a novel non-invasive index of LV contractility......................................................................................................81 5.1. Introduction ......................................................................................................81 5.2. Methods............................................................................................................82 5.2.1. Study population ..............................................................................................82 11 5.2.2. Study protocol ..................................................................................................82 5.2.3. Data analysis ....................................................................................................83 5.2.4. Statistical analysis............................................................................................85 5.3. Results..............................................................................................................86 5.4. Discussion ........................................................................................................89 5.4.1. Presence of regional stretch – strain relationship in the healthy LV................91 5.4.2. The slope of regional stretch – strain relationship as an estimate of myocardial contractile state .............................................................................................................91 5.4.3. Potential clinical application of the stretch –strain relationship ......................93 5.5. Limitations .......................................................................................................94 5.6. Conclusion........................................................................................................94 Chapter 6. Consistent Regional Heterogeneity of Passive Diastolic Stretch and Systolic Deformation in the Healthy Heart; Age Related Changes of LV contractility ....................97 6.1. Introduction ......................................................................................................97 6.2. Methods............................................................................................................98 6.2.1. Study population ..............................................................................................98 6.2.2. Study protocol ..................................................................................................98 6.2.3. Statistical analysis............................................................................................99 6.3. Results............................................................................................................100 6.4. Discussion ......................................................................................................103 6.4.1. Intra-ventricular differences of regional prestretch and strain values............105 6.4.2. Changes of segmental LV deformation with age ...........................................106 6.4.3. Changes of the stretch – strain relationship with age.....................................106 6.4.4. Reproducibility of the measurements.............................................................107 6.5. Limitations .....................................................................................................107 6.6. Conclusion......................................................................................................107 Chapter 7. Comparison of a new methodology for the assessment of 3D myocardial strain from volumetric ultrasound with 2D speckle tracking.......................................................109 7.1. Introduction ....................................................................................................109 7.2. Methods..........................................................................................................110 7.2.1. Study population ............................................................................................110 7.2.2. Data acquisition..............................................................................................110 7.2.3. Data processing ..............................................................................................111 7.2.4. Intra- and inter-observer variability ...............................................................113 7.2.5. Error analysis..................................................................................................113 12 7.2.6. Statistical analysis..........................................................................................113 7.3. Results............................................................................................................114 7.3.1. Segmental exclusions and variability of measurements.................................114 7.3.2. Global strain ...................................................................................................114 7.3.3. Segmental strain .............................................................................................116 7.3.4. Error analysis..................................................................................................120 7.4. Discussion ......................................................................................................120 7.5. Limitations .....................................................................................................123 7.6. Conclusion......................................................................................................123 Chapter 8. Stretch – strain relationship can be obtained by 2D and 3D myocardial deformation imaging techniques........................................................................................125 8.1. Introduction ....................................................................................................125 8.2. Methods..........................................................................................................125 8.2.1. Study population ............................................................................................125 8.2.2. Data acquisition and processing.....................................................................126 8.2.3. Statistical analysis..........................................................................................128 8.3. Results............................................................................................................128 8.3.1. Longitudinal and circumferential preS – SS relationships with 2DST..........128 8.3.2. Longitudinal and circumferential preS – SS relationships with 3DSE..........131 8.4. Discussion ......................................................................................................131 8.5. Limitations .....................................................................................................132 8.6. Conclusion......................................................................................................133 Chapter 9. Concluding discussion and future work.........................................................135 9.1. Intra-ventricular stretch – strain relationship in the healthy LV ....................135 9.1.1. Origin of the stretch and strain intra-ventricular heterogeneity .....................137 9.1.2 The slope of the regional stretch – strain relationship as an estimate of myocardial contractile state ........................................................................................138 9.1.3. Changes of the stretch – strain relationship with age....................................139 9.1.4. Meaning of the intercept of the stretch – strain relationship.........................140 9.2. The extension of the stretch – strain relationship to 2D and 3D myocardial deformation imaging methods........................................................................................140 9.2.1. Clinical validation of 3D myocardial deformation imaging ..........................141 9.2.2. The extension of the stretch – strain relationship to the 2D and 3D myocardial deformation imaging methods ....................................................................................141 9.3. Limitations .....................................................................................................142 13 9.4. Future work and perspectives.........................................................................143 9.5. Conclusion......................................................................................................143 Bibliography ........................................................................................................................145 List of Acronyms..................................................................................................................161 Curriculum vitae..................................................................................................................163 Publication and Abstract List.............................................................................................165 Summary ........................................................................................................................169 Nederlandse Samenvatting..................................................................................................171nrpages: 172status: publishe

Konventionelle Echokardiographie und Strain Imaging für die Vorhersage des kurzfristigen Verlaufs von idiopathischer dilatativer Kardiomyopathie

In the light of continuous prolongation of the waiting time on heart transplantation (HTx) lists, the need for reliable predictors of the time course of heart failure (HF) has become increasingly important. Thus, the aim of our study was to evaluate the prognostic value of echocardiography including 2D strain imaging in patients with end-stage idiopathic dilated cardiomyopathy (IDCM). The study population comprised 38 consecutive stable IDCM patients referred for HTx. At the baseline all of them underwent standard echocardiography, during which views for radial, circumferential and longitudinal strain and strain rate measurements were stored. Strain imaging was used to acquire radial, circumferential and longitudinal endsystolic strains (ESS), peak systolic strain rates (SSRmax), as well as early and late diastolic strain rate (DSRE and DSRA, respectively). Dyssynchrony indexes were also calculated from strain images. For quantification of systolic intraventricular dyssynchrony the coefficient of variance of time interval from the beginning of QRS complex to peak systolic strain at separate segments was used. Echocardiographic parameters were analyzed to find significant differences between the groups. Their value in predicting the time course of HF during the following 6 months was tested. During the first 6 months after inclusion in the study, 18 patients remained stable; the other 20 showed severe cardiac deterioration and finally 14 of them underwent ventricular assist implantation and 6 died, although initially there were no significant differences in peak oxygen consumption, left ventricular size, ejection fraction or other parameters describing systolic function. However, at the baseline, patients who remained stable had lower NT-proBNP values and less altered diastolic function in comparison to those with rapid HF progression. Thus, stable patients had significantly lower flow Doppler and strain imaging derived E/A ratios, longer transmitral E wave deceleration time and higher late diastolic strain rate (DSRA) values). They also had significantly lower systolic circumferential and longitudinal dyssynchrony indexes and higher longitudinal systolic strain and strain rate values. Diastolic parameters correlated strongly with NT-proBNP, which is known to be elevated in patients with HF, whereas systolic dyssynchrony indexes showed moderate correlations with this marker. No correlations between peak oxygen consumption and echocardiographic parameters were observed. At certain cut-off values flow Doppler and strain imaging derived diastolic parameters, as well as systolic dyssynchrony indexes showed high predictive values for cardiac stability over the next 6 months. Highest positive predictive values for rapid HF progression were found for transmitral E decelaration time (DT) <100ms, late diastolic strain rate (DSRA) 2 (88%, 89% and 85%, respectively). Thus in patients with advanced idiopathic DCM and similar LVEF (<30%), further cardiac stability appeared to be related to the severity of alterations in LV systolic synchronicity and diastolic function. Transmitral flow Doppler and strain imaging parameters can be recommended as short-term prognostic factors in these patients. Moreover, strain imaging is a highly reproducible, not time consuming and a very useful method, as it allows a better understanding of HF pathophysiology and throws light on the progress of this syndrome in individual patients. This extensive approach to the problem is essential while making decisions of vital importance, such as patient listing for high-urgency Tx.Der Mangel an Spenderorganen verlängert zunehmend die Wartezeiten auf den Transplantations-Listen. Der klinische Verlauf nach Herztransplantation (HTx) - Listung ist bei Patienten mit idiopathischer dilatativer Kardiomyopathie (IDCM) unterschiedlich und schwer vorhersehbar. Die frühzeitige Erkennung einer potentiell vitalen Gefährdung könnte das Outcome der Patienten wesentlich verbessern. Deswegen war unser Ziel echokardiographische Parameter mit prognostischem Wert für die kurzfristige Vorhersage des klinischen Verlaufs bei schwerer IDCM zu suchen. Für unsere Studie haben wir 38 stabile Patienten mit End-Stage IDCM, die für HTx gelistet sind, aufgenommen. Am Ausgang wurde für alle Patienten die konventionelle Echokardiographie und das 2D Strain Imaging durchgeführt. Von den 2D Strain Imaging Bildern haben wir radialen, zirkumferenziellen und longitudinalen endsystolischen Strain (ESS), maximalen systolischen Strain Rate (SSRmax), frühen und späten diastolischen Strain Rate (DSRE und DSRA,). Um die Dyssynchronizität des linken Ventrikels zu bewerten haben wir den Variationskoeffizient der Zeit zwischen QRS Beginn und maximalem Strain in den unterschiedlichen Segmenten berechnet. Prognostischer Wert von diesen Parametern wurde für die 6 Monate des Verlauf von IDCM überprüft. Nach 6 Monaten sind 18 von den Patienten klinisch stabil geblieben und 20 haben sich klinisch verschlechtert, obwohl es am Anfang der Studie keine signifikanten Unterschiede hinsichtlich maximalem Sauerstoffverbrauch, Dimensionen und Ejektions Fraktion des linken Ventrikels oder anderen konventionellen systolischen Parametern gab. Allerdings hatten die Patienten, die stabil geblieben sind, am Ausgang eine bessere diastolische Funktion. Sie haben ein kleineres E/A Verhältnis, längere E Dezelerationszeit, sowie größere spät diastolische Strain Rate (DSRA) als die Patienten, dessen Zustand sich später verschlechterte, gehabt. Außerdem war die zirkumferenzielle und longitudinale Dyssynchronizität mehr ausgeprägt in die Gruppe von instabilen Patienten. Wir haben starke Korrelationen zwischen diastolischen Parametern und NT-proBNP gefunden. Die Korrelationen zwischen dem Dyssynchronizitätsparameter und NT-proBNP waren mittelmäßig. Echokardiographische Parameter haben nicht mit maximalem Sauerstoffverbrauch korreliert. Diastolische sowie Dyssynchronizitätsparameter haben starken prädiktiven Werten für die kurzfristige Stabilität der Patienten mit IDCM. Die besten positiven prädiktiven Werte für klinische Verschlechterung haben E Dezelarationszeit (DT) <100ms, spät diastolische Strain Rate (DSRA) <0.3/s und von 2D Strain Imaging bewertetes E/A (DSRE/A) >2 (entsprechend 88%, 89% and 85%). Die Stabilität von IDCM Patienten scheint abhängig von diastolischer Funktion und Synchronizität des linken Ventrikels zu sein. Tranmitrales Fluss Doppler und 2D Strain Imaging sind wenig Zeit verbrauchende und gut reproduzierbare Methoden, die den kurzfristigen Verlauf von diesen Patienten vorhersagen können. Die beiden Methoden könnten durch frühzeitige Erkennung einer potentiell vitalen Gefährdung das Outcome der Patienten wesentlich verbessern