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.Conclusionthe road for a more individualized approach to resynchronization therapy delivery is open and imaging might provide important input beyond the assessment of LVEF

Characterization of cardiac resynchronization therapy response through machine learning and personalized models

International audienceIntroduction: The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a novel hybrid approach, integrating machine-learning and personalized models, to identify explainable phenogroups of HF patients and predict their CRT response.Methods: The paper proposes the creation of a complete personalized model population based on preoperative CRT patient strain curves. Based on the parameters and features extracted from these personalized models, phenotypes of patients are identified thanks to a clustering algorithm and a random forest classification is provided.Results: A close match was observed between the 162 experimental and simulated myocardial strain curves, with a mean RMSE of 4.48% (±1.08) for the 162 patients. Five phenogroups of personalized models were identified from the clustering, with response rates ranging from 52% to 94%. The classification results show a mean area under the curves (AUC) of 0.86 ± 0.06 and provided a feature importance analysis with 22 features selected. Results show both regional myocardial contractility (from 22.5% to 33.0%), tissue viability and electrical activation delays importance on CRT response for each HF patient (from 55.8 ms to 88.4 ms).Discussion: The patient-specific model parameters' analysis provides an explainable interpretation of HF patient phenogroups in relation to physiological mechanisms that seem predictive of the CRT response. These novel combined approaches appear as promising tools to improve understanding of LV mechanical dyssynchrony for HF patient characterization and CRT selection

Could echocardiographic left atrial characterization have additive value for detecting risks of atrial arrhythmias and stroke in patients with hypertrophic cardiomyopathy?

International audienceAims Atrial arrhythmia (AA) is considered a turning point for prognosis in patients with hypertrophic cardiomyopathy (HCM). We sought to assess whether the occurrence of AA and stroke could be estimated by an echocardiographic evaluation. Methods and results A total of 216 patients with HCM (52 +/- 16 years old) were analysed. All patients underwent transthoracic echocardiography for the evaluation of left atrial volume (LAV), peak left atrial strain (PLAS), and peak atrial contraction strain. The patients were followed for 2.9 years for the occurrence of a composite endpoint including AA and/or stroke and peripheral embolism. Among the 216 patients, 78 (36%) met the composite endpoint. These patients were older (57.1 +/- 14.4 vs. 50.3 +/- 16.7 years; P = 0.0035), had a higher prevalence of arterial hypertension (62.3 vs. 42.3%; P = 0.005), and had higher NT-proBNP. The LAV (47 +/- 20 vs. 37.2 +/- 15.7 mL/m(2); P = 0.0001) was significantly higher in patients who met the composite endpoint, whereas PLAS was significantly impaired (19.3 +/- 9.54 vs. 26.6 +/- 9.12%; P < 0.0001). After adjustment, PLAS was independently associated with events with an odds ratio of 0.42 (95% confidence interval 0.29-0.61; P < 0.0001). Stroke occurred in 67% of the patients without any clinical AA. The PLAS with a cut-off of under 15.5% provided event prediction with 91% specificity. Using a 15% cut-off, PLAS also demonstrated a predictive value for new-onset of AA. Conclusion The decrease in PLAS was strongly associated with the risk of stroke, even in patients without any documented AA. Its value for guiding the management of patients with HCM requires further investigation