The saga of dyssynchrony imaging: Are we getting to the point.

Cardiac resynchronisation therapy (CRT) has an established role in the management of patients with heart failure, reduced left ventricular ejection fraction (LVEF < 35%) and widened QRS (>130 msec). Despite the complex pathophysiology of left ventricular (LV) dyssynchrony and the increasing evidence supporting the identification of specific electromechanical substrates that are associated with a higher probability of CRT response, the assessment of LVEF is the only imaging-derived parameter used for the selection of CRT candidates. This review aims to (1) provide an overview of the evolution of cardiac imaging for the assessment of LV dyssynchrony and its role in the selection of patients undergoing CRT; (2) highlight the main pitfalls and advantages of the application of cardiac imaging for the assessment of LV dyssynchrony; (3) provide some perspectives for clinical application and future research in this field. Conclusion: the road for a more individualized approach to resynchronization therapy delivery is open and imaging might provide important input beyond the assessment of LVEF.

Evolution of non-invasive myocardial work variables after transcatheter aortic valve implantation in patients with severe aortic stenosis.

INTRODUCTION: Guidelines recommend aortic valve replacement in patients with severe aortic stenosis who present with symptoms or left ventricular ejection fraction<50%, both conditions representing a late stage of the disease. Whereas global longitudinal strain is load dependent, but interesting for assessing prognosis, myocardial work has emerged. AIM: To evaluate acute changes in myocardial work occurring in patients undergoing transcatheter aortic valve implantation (TAVI). METHODS: Patients who underwent TAVI were evaluated before and after by echocardiography. Complete echocardiographies were considered. Myocardial work indices (global work index, global constructive work, global work efficiency, global wasted work) were calculated integrating mean transaortic pressure gradient and brachial cuff systolic pressure. RESULTS: One hundred and twenty-five patients underwent successful TAVI, with a significant decrease in mean transaortic gradient (from 52.5±16.1 to 12.2±5.0; P<0.0001). There was no significant change in left ventricular ejection fraction after TAVI. Myocardial work data after TAVI showed a significant reduction in global work index (1389±537 vs. 2014±714; P<0.0001), global constructive work (1693±543 vs. 2379±761; P<0.0001) and global work efficiency (85.0±7.06 vs. 87.1±5.98; P=0.0034). The decrease in global work index and global constructive work after TAVI was homogeneous among different subgroups, based on global longitudinal strain, left ventricular ejection fraction and New York Heart Association status before TAVI. We observed a significant association between global work index and global constructive work before TAVI, and global longitudinal strain degradation after TAVI. CONCLUSIONS: Myocardial work variables show promising potential in best understanding the left ventricular myocardial consequences of aortic stenosis and its correction. Given their ability to discriminate between New York Heart Association status and global longitudinal strain evolution, we can hypothesize about their clinical value.

Optimal selection of multipolar electrode configurations for nerve burst detection.

Nerve cuff electrodes are commonly used for neural stimulation and recording applications. Usually, these electrodes are composed of a limited set of metal rings, disposed around the nerve. Although widely used, this technology may be insufficient to record and stimulate in a more selective manner. Higher resolution electrodes, usually composed of a matrix of independent contact points, have been proposed in this sense. These electrodes allow for the exploration of a wide variety of bipolar or multipolar setups, for selective recording and stimulation. In this study, we propose a method to optimally select such multipolar setups and to quantitatively evaluate the performance of a multi-contact neural organic electrode (OE) in recording burst discharges from the rat's phrenic nerve. A 16-channel OE was wrapped around the phrenic nerve (studied electrode) and a suction electrode was applied to the cut-end of the same nerve (gold standard electrode). Analysis of all possible combinations of bipoles and tripoles from the OE were carried out to assess the improvement in the recording performance, measured as the signal-to-noise ratio, compared to the gold standard. The results showed that the bipolar and tripolar configuration significantly increased the overall recording performance. Such configurations are therefore essential to improve nerve burst detection.

How myocardial work could be relevant in patients with an aortic valve stenosis?

AIMS: Myocardial work (MW) calculation is an attractive method to assess left ventricular (LV) myocardial function. In case of aortic stenosis (AS), assessment of work indices is challenging because it requires an accurate evaluation of LV-pressure curves. We sought to evaluate the performances of two distinct methods and to provide a quantitative comparison with invasive data. METHODS AND RESULTS: Model-based and template-based methods were defined and applied for the evaluation of LV-pressures on 67 AS-patient. Global Constructive (GCW), Wasted (GWW), Positive (GPW), Negative (GNW) MW and Global Work Efficiency (GWE), and Index (GWI) parameters were calculated using the available software computing the indices using brachial blood-pressure and trans-aortic mean pressure gradient (MPG) for estimating the LV-pressures vs. using a model-based and homemade software. A complete comparison was performed with invasive measurements. Patients were characterized by MPG of 49.8 ± 14.8 mmHg, the global longitudinal strain (GLS) was -15.0 ± 4.04%, GCW was 2107 ± 800 mmHg.% (model-based) and 2483 ± 1068 mmHg.% (template-based). The root mean square error (RMSE) and correlation were calculated for each patient and pressure estimation methods. The mean RMSE are 33.9 mmHg and 40.4 mmHg and the mean correlation coefficients are 0.81 and 0.72 for the model-based and template-based methods, respectively. The two methods present correlation coefficient r2 >0.75 for all the indices. CONCLUSION: The two non-invasive methods of LV pressure estimation and work indices computation correlate with invasive measurements. Although the model-based approach requires less information and is associated with slightly better performances, the implementation of template-based method is easier and is appropriate for clinical practice.

Desynchronization Strain Patterns and Contractility in Left Bundle Branch Block through Computer Model Simulation.

Left bundle branch block (LBBB) is associated with specific septal-to-lateral wall activation patterns which are strongly influenced by the intrinsic left ventricular (LV) contractility and myocardial scar localization. The objective of this study was to propose a computational-model-based interpretation of the different patterns of LV contraction observed in the case of LBBB and preserved contractility or myocardial scarring. Two-dimensional transthoracic echocardiography was used to obtain LV volumes and deformation patterns in three patients with LBBB: (1) a patient with non-ischemic dilated cardiomyopathy, (2) a patient with antero-septal myocardial scar, and (3) a patient with lateral myocardial scar. Scar was confirmed by the distribution of late gadolinium enhancement with cardiac magnetic resonance imaging (cMRI). Model parameters were evaluated manually to reproduce patient-derived data such as strain curves obtained from echocardiographic apical views. The model was able to reproduce the specific strain patterns observed in patients. A typical septal flash with pre-ejection shortening, rebound stretch, and delayed lateral wall activation was observed in the case of non-ischemic cardiomyopathy. In the case of lateral scar, the contractility of the lateral wall was significantly impaired and septal flash was absent. In the case of septal scar, septal flash and rebound stretch were also present as previously described in the literature. Interestingly, the model was also able to simulate the specific contractile properties of the myocardium, providing an excellent localization of LV scar in ischemic patients. The model was able to simulate the electromechanical delay and specific contractility patterns observed in patients with LBBB of ischemic and non-ischemic etiology. With further improvement and validation, this technique might be a useful tool for the diagnosis and treatment planning of heart failure patients needing CRT.

Multivariate ensemble classification for the prediction of symptoms in patients with Brugada syndrome.

Identification of asymptomatic patients at higher risk for suffering cardiac events remains controversial and challenging in Brugada syndrome (BS). In this work, we proposed an ECG-based classifier to predict BS-related symptoms, by merging the most predictive electrophysiological features derived from the ventricular depolarization and repolarization periods, along with autonomic-related markers. The initial feature space included local and dynamic ECG markers, assessed during a physical exercise test performed in 110 BS patients (25 symptomatic). Morphological, temporal and spatial properties quantifying the ECG dynamic response to exercise and recovery were considered. Our model was obtained by proposing a two-stage feature selection process that combined a resampled-based regularization approach with a wrapper model assessment for balancing, simplicity and performance. For the classification step, an ensemble was constructed by several logistic regression base classifiers, whose outputs were fused using a performance-based weighted average. The most relevant predictors corresponded to the repolarization interval, followed by two autonomic markers and two other makers of depolarization dynamics. Our classifier allowed for the identification of novel symptom-related markers from autonomic and dynamic ECG responses during exercise testing, suggesting the need for multifactorial risk stratification approaches in order to predict future cardiac events in asymptomatic BS patients. Graphical abstract Pipeline for feature selection and predictive modeling of symptoms in Brugada syndrome.

Modeling Patient-Specific Desaturation Patterns in Sleep Apnea.

OBJECTIVE: The physiological mechanisms involved in cardio-respiratory responses to sleep apnea events are not yet fully elucidated. A model-based approach is proposed to analyse the acute desaturation response to obstructive apneas. METHODS: An integrated model of cardio-respiratory interactions was proposed and parameters were identified, using an evolutionary algorithm, on a database composed of 107 obstructive apneas acquired from 10 patients (HYPNOS clinical study). Unsupervised clustering was applied to the identified parameters in order to characterize the phenotype of each response to obstructive apneas. RESULTS: A close match was observed between simulated oxygen saturation ( SaO2) and experimental SaO2 in all identifications (median RMSE = 1.3892 %). Two clusters of parameters, associated with different dynamics related to sleep apnea and periodic breathing were obtained. CONCLUSION AND SIGNIFICANCE: The proposed patient and event-specific model-based analysis provides understanding on specific desaturation patterns, consequent to apnea events, with potential applications for personalized diagnosis and treatment.

Left atrial strain is a predictor of left ventricular systolic and diastolic reverse remodelling in CRT candidates.

AIMS: The left atrium (LA) has a pivotal role in cardiac performance and LA deformation is a well-known prognostic predictor in several clinical conditions including heart failure with reduced ejection fraction. The aim of this study is to investigate the effect of cardiac resynchronization therapy (CRT) on both LA morphology and function and to assess the impact of LA reservoir strain (LARS) on left ventricular (LV) systolic and diastolic remodelling after CRT. METHODS AND RESULTS: Two hundred and twenty-one CRT-candidates were prospectively included in the study in four tertiary centres and underwent echocardiography before CRT-implantation and at 6-month follow-up (FU). CRT-response was defined by a 15% reduction in LV end-systolic volume. LV systolic and diastolic remodelling were defined as the percent reduction in LV end-systolic and end-diastolic volume at FU. Indexed LA volume (LAVI) and LV-global longitudinal (GLS) strain were the main parameters correlated with LARS, with LV-GLS being the strongest determinant of LARS (r = -0.59, P < 0.0001). CRT induced a significant improvement in LAVI and LARS in responders (both P < 0.0001). LARS was an independent predictor of both LV systolic and diastolic remodelling at follow-up (r = -0.14, P = 0.049 and r = -0.17, P = 0.002, respectively). CONCLUSION: CRT induces a significant improvement in LAVI and LARS in responders. In CRT candidates, the evaluation of LARS before CRT delivery is an independent predictor of LV systolic and diastolic remodelling at FU.

Sensitivity Analysis of a Cardio-respiratory Model in Preterm Newborns for the Study of Patent Ductus Arteriosus.

This paper proposes an integrated model of cardio-respiratory interactions in preterm newborns, focused on the study of the patent ductus arteriosus (PDA). A formal model parameter sensitivity analysis on blood flow through the PDA is performed. Results show that the proposed model is capable of simulating hemodynamics in right-to-left and left-to-right shunts. For both configurations, the most significant parameters are associated with mechanical ventricular properties and circulatory parameters related to left ventricle loading conditions. These results highlight important physiological mechanisms involved in PDA and provide key information towards the definition of patient-specific parameters.

Parametric Analysis of an Integrated Model of Cardio-respiratory Interactions in Adults in the Context of Obstructive Sleep Apnea.

An original integrated model of cardio-respiratory interactions is presented in this paper with the objective of studying the acute physiological responses evoked by obstructive sleep apnea events in adults. A comprehensive sensitivity analysis of the model is proposed during the simulation of a 20 s obstructive apnea episode using the Morris' screening method and local sensitivity analysis. The more relevant parameters are related to the following mechanisms of the physiology: (i) the fraction of oxygen in inspired air, (ii) metabolic rates (oxygen consumption rate, CO2 production rate); (iii) chemoreflex (gains and time constants) (iv) respiratory mechanics (lung compliance and unstressed volume of air in the alveoli). These results highlight significant physiological variables that may be particularly useful for the development of novel diagnostic and therapeutic strategies, integrating a virtual patient approach.

Assessment of the Use of Multi-Channel Organic Electrodes to Record ENG on Small Nerves: Application to Phrenic Nerve Burst Detection.

Effective closed-loop neuromodulation relies on the acquisition of appropriate physiological control variables and the delivery of an appropriate stimulation signal. In particular, electroneurogram (ENG) data acquired from a set of electrodes applied at the surface of the nerve may be used as a potential control variable in this field. Improved electrode technologies and data processing methods are clearly needed in this context. In this work, we evaluated a new electrode technology based on multichannel organic electrodes (OE) and applied a signal processing chain in order to detect respiratory-related bursts from the phrenic nerve. Phrenic ENG (pENG) were acquired from nine Long Evans rats in situ preparations. For each preparation, a 16-channel OE was applied around the phrenic nerve's surface and a suction electrode was applied to the cut end of the same nerve. The former electrode provided input multivariate pENG signals while the latter electrode provided the gold standard for data analysis. Correlations between OE signals and that from the gold standard were estimated. Signal to noise ratio (SNR) and ROC curves were built to quantify phrenic bursts detection performance. Correlation score showed the ability of the OE to record high-quality pENG. Our methods allowed good phrenic bursts detection. However, we failed to demonstrate a spatial selectivity from the multiple pENG recorded with our OE matrix. Altogether, our results suggest that highly flexible and biocompatible multi-channel electrode may represent an interesting alternative to metallic cuff electrodes to perform nerve bursts detection and/or closed-loop neuromodulation.

Echocardiographic view and feature selection for the estimation of the response to CRT.

Cardiac resynchronization therapy (CRT) is an implant-based therapy applied to patients with a specific heart failure (HF) profile. The identification of patients that may benefit from CRT is a challenging task and the application of current guidelines still induce a non-responder rate of about 30%. Several studies have shown that the assessment of left ventricular (LV) mechanics by speckle tracking echocardiography can provide useful information for CRT patient selection. A comprehensive evaluation of LV mechanics is normally performed using three different echocardioraphic views: 4, 3 or 2-chamber views. The aim of this study is to estimate the relative importance of strain-based features extracted from these three views, for the estimation of CRT response. Several features were extracted from the longitudinal strain curves of 130 patients and different methods of feature selection (out-of-bag random forest, wrapping and filtering) have been applied. Results show that more than 50% of the 20 most important features are calculated from the 4-chamber view. Although features from the 2- and 3-chamber views are less represented in the most important features, some of the former have been identified to provide complementary information. A thorough analysis and interpretation of the most informative features is also provided, as a first step towards the construction of a machine-learning chain for an improved selection of CRT candidates.

Characterization of Responder Profiles for Cardiac Resynchronization Therapy through Unsupervised Clustering of Clinical and Strain Data.

BACKGROUND: The mechanisms of improvement of left ventricular (LV) function with cardiac resynchronization therapy (CRT) are not yet elucidated. The aim of this study was to characterize CRT responder profiles through clustering analysis, on the basis of clinical and echocardiographic preimplantation data, integrating automatic quantification of longitudinal strain signals. METHODS: This was a multicenter observational study of 250 patients with chronic heart failure evaluated before CRT device implantation and followed up to 4 years. Clinical, electrocardiographic, and echocardiographic data were collected. Regional longitudinal strain signals were also analyzed with custom-made algorithms in addition to existing approaches, including myocardial work indices. Response was defined as a decrease of ≥15% in LV end-systolic volume. Death and hospitalization for heart failure at 4 years were considered adverse events. Seventy features were analyzed using a clustering approach (k-means clustering). RESULTS: Five clusters were identified, with response rates between 50% in cluster 1 and 92.7% in cluster 5. These five clusters differed mainly by the characteristics of LV mechanics, evaluated using strain integrals. There was a significant difference in event-free survival at 4 years between cluster 1 and the other clusters. The quantitative analysis of strain curves, especially in the lateral wall, was more discriminative than apical rocking, septal flash, or myocardial work in most phenogroups. CONCLUSIONS: Five clusters are described, defining groups of below-average to excellent responders to CRT. These clusters demonstrate the complexity of LV mechanics and prediction of response to CRT. Automatic quantitative analysis of longitudinal strain curves appears to be a promising tool to improve the understanding of LV mechanics, patient characterization, and selection for CRT.

Importance of Systematic Right Ventricular Assessment in Cardiac Resynchronization Therapy Candidates: A Machine Learning Approach.

BACKGROUND: Despite all having systolic heart failure and broad QRS intervals, patients screened for cardiac resynchronization therapy (CRT) are highly heterogeneous, and it remains extremely challenging to predict the impact of CRT devices on left ventricular function and outcomes. The aim of this study was to evaluate the relative impact of clinical, electrocardiographic, and echocardiographic data on the left ventricular remodeling and prognosis of CRT candidates by the application of machine learning approaches. METHODS: One hundred ninety-three patients with systolic heart failure receiving CRT according to current recommendations were prospectively included in this multicenter study. A combination of the Boruta algorithm and random forest methods was used to identify features predicting both CRT volumetric response and prognosis. Model performance was tested using the area under the receiver operating characteristic curve. The k-medoid method was also applied to identify clusters of phenotypically similar patients. RESULTS: From 28 clinical, electrocardiographic, and echocardiographic variables, 16 features were predictive of CRT response, and 11 features were predictive of prognosis. Among the predictors of CRT response, eight variables (50%) pertained to right ventricular size or function. Tricuspid annular plane systolic excursion was the main feature associated with prognosis. The selected features were associated with particularly good prediction of both CRT response (area under the curve, 0.81; 95% CI, 0.74-0.87) and outcomes (area under the curve, 0.84; 95% CI, 0.75-0.93). An unsupervised machine learning approach allowed the identification of two phenogroups of patients who differed significantly in clinical variables and parameters of biventricular size and right ventricular function. The two phenogroups had significantly different prognosis (hazard ratio, 4.70; 95% CI, 2.1-10.0; P < .0001; log-rank P < .0001). CONCLUSIONS: Machine learning can reliably identify clinical and echocardiographic features associated with CRT response and prognosis. The evaluation of both right ventricular size and functional parameters has pivotal importance for the risk stratification of CRT candidates and should be systematically performed in patients undergoing CRT.

Prediction of response to cardiac resynchronization therapy using a multi-feature learning method.

We hypothesized that a multiparametric evaluation, based on the combination of electrocardiographic and echocardiographic parameters, could enhance the appraisal of the likelihood of reverse remodeling and prognosis of favorable clinical evolution to improve the response of cardiac resynchronization therapy (CRT). Three hundred and twenty-three heart failure patients were retrospectively included in this multicenter study. 221 patients (68%) were responders, defined by a decrease in left ventricle end-systolic volume ≥15% at the 6-month follow-up. In addition, strain data coming from echocardiography were analyzed with custom-made signal processing methods. Integrals of regional longitudinal strain signals from the beginning of the cardiac cycle to strain peak and to the instant of aortic valve closure were analyzed. QRS duration, septal flash and different other features manually extracted were also included in the analysis. The random forest (RF) method was applied to analyze the relative feature importance, to select the most significant features and to build an ensemble classifier with the objective of predicting response to CRT. The set of most significant features was composed of Septal Flash, E, E/A, E/EA, QRS, left ventricular end-diastolic volume and eight features extracted from strain curves. A Monte Carlo cross-validation method with 100 runs was applied, using, in each run, different random sets of 80% of patients for training and 20% for testing. Results show a mean area under the curve (AUC) of 0.809 with a standard deviation of 0.05. A multiparametric approach using a combination of echo-based parameters of left ventricular dyssynchrony and QRS duration helped to improve the prediction of the response to cardiac resynchronization therapy.

New expectations for diastolic function assessment in transthoracic echocardiography based on a semi-automated computing of strain-volume loops.

AIMS: Early diagnosis of heart failure with preserved ejection fraction (HFpEF) by determination of diastolic dysfunction is challenging. Strain-volume loop (SVL) is a new tool to analyse left ventricular function. We propose a new semi-automated method to calculate SVL area and explore the added value of this index for diastolic function assessment. METHOD AND RESULTS: Fifty patients (25 amyloidosis, 25 HFpEF) were included in the study and compared with 25 healthy control subjects. Left ventricular ejection fraction was preserved and similar between groups. Classical indices of diastolic function were pathological in HFpEF and amyloidosis groups with greater left atrial volume index, greater mitral average E/e' ratio, faster tricuspid regurgitation (P < 0.0001 compared with controls). SVL analysis demonstrated a significant difference of the global area between groups, with the smaller area in amyloidosis group, the greater in controls and a mid-range value in HFpEF group (37 vs. 120 vs. 72 mL.%, respectively, P < 0.0001). Applying a linear discriminant analysis (LDA) classifier, results show a mean area under the curve of 0.91 for the comparison between HFpEF and amyloidosis groups. CONCLUSION: SVLs area is efficient to identify patients with a diastolic dysfunction. This new semi-automated tool is very promising for future development of automated diagnosis with machine-learning algorithms.

Model-based estimation of left ventricular pressure and myocardial work in aortic stenosis.

This paper proposes a model-based estimation of left ventricular (LV) pressure for the evaluation of constructive and wasted myocardial work of patients with aortic stenosis (AS). A model of the cardiovascular system is proposed, including descriptions of i) cardiac electrical activity, ii) elastance-based cardiac cavities, iii) systemic and pulmonary circulations and iv) heart valves. After a sensitivity analysis of model parameters, an identification strategy was implemented using a Monte-Carlo cross-validation approach. Parameter identification procedure consists in two steps for the estimation of LV pressures: step 1) from invasive, intraventricular measurements and step 2) from non-invasive data. The proposed approach was validated on data obtained from 12 patients with AS. The total relative errors between estimated and measured pressures were on average 11.9% and 12.27% and mean R2 were equal to 0.96 and 0.91, respectively for steps 1 and 2 of parameter identification strategy. Using LV pressures obtained from non-invasive measurements (step 2) and patient-specific simulations, Global Constructive (GCW), Wasted (GWW) myocardial Work and Global Work Efficiency (GWE) parameters were calculated. Correlations between measures and model-based estimations were 0.88, 0.80, 0.91 respectively for GCW, GWW and GWE. The main contributions concern the proposal of the parameter identification procedure, applied on an integrated cardiovascular model, able to reproduce LV pressure specifically to each AS patient, by non-invasive procedures, as well as a new method for the non-invasive estimation of constructive, wasted myocardial work and work efficiency in AS.