Model-based analysis of myocardial strains in left bundle branch block

International audienceIntroduction: Although observational studies of patients with left bundle branch block (LBBB) have shown a relation between strain morphologies and responses to cardiac resynchronization therapy (CRT), the evaluation of left ventricle (LV) dyssynchrony from echocardiography remains difficult. The objective of this article is to propose a patient-specific model-based approach to improve the analysis and interpretation of myocardial strain signals. Methods: A system-level model of the cardiovascular system is proposed, integrating: (i) the cardiac electrical system, (ii) right and left atria, (iii) a multi-segment representation of the RVs and LVs, and (iv) the systemic and pulmonary circulations. After a sensitivity analysis step, model parameters were identified specifically for each patient. The proposed approach was evaluated on data obtained from 10 healthy subjects and 20 patients with LBBB with underlying ischemic (n = 10) and non-ischemic (n = 10) cardiomyopathies. Results: A close match was observed between estimated and observed strain signals, with mean RMSE respectively equal to 5.04 ± 1.02% and 3.90 ± 1.40% in healthy and LBBB cases. The analysis of patient-specific identified parameters, based on bull's-eye representation, shows that strain morphologies are related to both electrical conduction delay, and heterogeneity of contractile levels within the myocardium. Discussion: The model-based approach improve the interpretability echocardiography data by bringing additional information on the regional electrical and mechanical function of the LV. The analysis of model parameters show that septal motion and global strain morphologies are not only explained by electrical conduction delay but also by the heterogeneity of contractile levels within the myocardium. The proposed approach represents a step forward in the development of personalized LV models for the evaluation of LV dyssynchrony in the field of CRT. Copyright © 2022 Taconné, Owashi, Galli, Duchenne, Hubert, Donal, Hernàndez and Le Rolle

Sensitivity Analysis and Parameter Identification of a Cardiovascular Model in Aortic Stenosis

International audienceThe objective of this study is to propose a model-based method, adapted to patients with severe aortic stenosis (AS), in order to reproduce left ventricle (LV) pressure and volume from patient specific data. A formal sensitivity analysis is proposed, focused on left ventricle volume and pressure. The most influent parameters of this analysis are then selected to be identified in a parameter identification strategy and provide a patient specific pressure curve. This was implemented on 3 AS patients and a close match was observed between experimental and simulated pressure and volume curves. The global root mean square error (RMSE) for pressure and volume curves are respectively 21.8 (pm 1.8) mmHg and 14.8 (pm 9.4)ml,. The model-based approach proposed shows promising results to generate accurate LV pressure and volume in AS case. © 2021 Creative Commons

Model-based and Unsupervised Machine-learning Approaches for the Characterization of Responder Profiles for Cardiac Resynchronization Therapy

International audienceThe objective of this study is to improve the inter-pretability of a previous unsupervised clustering analysis of the CRT response through a physiological model-based approach. The developed clustering approach was applied on 250 CRT candidates based on clinical, original and classical echocardiographic features. Patient-specific computational models were proposed for patients associated of each cluster barycenter in order to provide an ex-plainable analysis in relation with physiological mecha-nisms. Five phenogroups were identified from the clustering approach with response rates ranging from 50% to 92.7%. Concerning the model-based approach, a match was observed between the 16 experimental and simulated myocardial strain curves pattern with a mean RMSE of 3.97%(± 1.74) on the five patients. Moreover, the identified model parameters provide us information about the mecano-electrical coupling and tissue properties. The gain of information provides by the parameters model identification, added to the clinical and classical echocar-diographic features is promising for an understanding of LV mechanical dyssynchrony and the identification of patients suitable for CRT. © 2022 Creative Commons

Improved fisheries management could offset many negative effects of climate change.

The world's oceans supply food and livelihood to billions of people, yet species' shifting geographic ranges and changes in productivity arising from climate change are expected to profoundly affect these benefits. We ask how improvements in fishery management can offset the negative consequences of climate change; we find that the answer hinges on the current status of stocks. The poor current status of many stocks combined with potentially maladaptive responses to range shifts could reduce future global fisheries yields and profits even more severely than previous estimates have suggested. However, reforming fisheries in ways that jointly fix current inefficiencies, adapt to fisheries productivity changes, and proactively create effective transboundary institutions could lead to a future with higher profits and yields compared to what is produced today

Characterisation of responder profiles for cardiac resynchronisation therapy based on explainable machine-learning and virtual patient approaches

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