Critical care is provided to patients who require intensive monitoring and often the support of failing organs. Cardiovascular and circulatory diseases and dysfunctions are extremely common in this group of patients. However, cardiac disease states are highly patient-specific and every patient has a unique expression of the disease or underlying dysfunction. Clinical staff must consider many combinations of different disease scenarios based on frequently conflicting or confusing measured data on a patient’s condition. Successful diagnosis and treatment therefore often rely on the experience and intuition of clinical staff, increasing the likelihood for clinical errors. A cardiovascular (CVS) computerized model that uniquely represents the patient and underlying dysfunction or disease is developed. The CVS model is extended to account for the known physiologic mechanisms during spontaneous breathing and mechanical ventilation, thus increasing the model’s accuracy of representing a critically ill patient in the intensive care unit (ICU). The extended CVS model is validated by correctly simulating several well known circulatory mechanisms and interactions. An integral-based system parameter identification method is refined and extended to account for much smaller subsets of available input data, as usually seen in critical care units. For example, instead of requiring the continuous ventricle pressure and volume waveforms, only the end-systolic (ESV) and end-diastolic (EDV) volume values are needed, which can be even further reduced to only using the global end-diastolic volume (GEDV) and estimating the ventricle volumes. These changes make the CVS model and its application to monitoring more pplicable to a clinical environment. The CVS model and integral-based parameter identification approach are validated on data from porcine experiments of pulmonary embolism (PE), positive end-expiratory pressure (PEEP) titrations at different volemic levels, and 2 different studies of induced endotoxic (septic) shock. They are also validated on 3 adrenaline dosing data sets obtained from published studies in humans. Overall, these studies are used to show how the model and realistic clinical measurements may be used to provide a clear clinical picture in real-time. A wide range of clinically measured hemodynamics were successfully captured over time. The integral-based method identified all model parameters, typically with less than 10% error versus clinically measured pressure and volume signals. Moreover, patient-specific parameter relationships were formulated allowing the forward prediction of the patient’s response towards clinical interventions, such as administering a fluid bolus or changing the dose of an inotrope. Hence, the model and methods are able to provide diagnostic information and therapeutic decision support. In particular, tracking the model parameter changes over time can assist clinical staff in finding the right diagnosis, for example an increase in pulmonary vascular resistance indicates a developing constriction in the pulmonary artery caused by an embolus. Furthermore, using the predictive ability of the model and developed methods, different treatment choices and their effect on the patient can be simulated. Thus, the best individual treatment for each patient can be developed and chosen, and unnecessary or even harmful interventions avoided. This research thus increases confidence in the clinical applicability and validity of this overall diagnostic monitoring and therapy guidance approach. It accomplishes this goal using a novel physiological model of the heart and circulation. The integral-based parameter identification methods take dense, numerical data from diverse measurements and aggregate them into a clearer physiological picture of CVS status. Hence, the broader accomplishment of this thesis is the transformation, using computation and models, of diverse and often confusing measured data into a patient-specific physiological picture - a new model-based therapeutic.

L'estimation des solutions du problème inverse en Électrocardiographie (ECG) représente un intérêt majeur dans le diagnostic et la thérapie d'arythmies cardiaques par cathéter. Ce dernier consiste à fournir des images 3D de la distribution spatiale de l'activité électrique du cœur de manière non-invasive à partir des données anatomiques et électrocardiographiques. D'une part ce problème est rendu difficile à cause de son caractère mal-posé. D'autre part, la validation des méthodes proposées sur données cliniques reste très limitée. Une alternative consiste à évaluer ces méthodes sur des données simulées par un modèle électrique cardiaque. Pour cette application, les modèles existants sont soit trop complexes, soit ne produisent pas un schéma de propagation cardiaque réaliste. Dans un premier temps, nous avons conçu un modèle cœur-torse basse-résolution qui génère des cartographies cardiaques et des ECGs réalistes dans des cas sains et pathologiques. Ce modèle est bâti sur une géométrie coeur-torse simplifiée et implémente le formalisme monodomaine en utilisant la Méthode des Éléments Finis (MEF). Les paramètres ont été identifiés par une approche évolutionnaire et leur influence a été analysée par une méthode de criblage. Dans un second temps, une nouvelle approche pour résoudre le problème inverse a été proposée et comparée aux méthodes classiques dans les cas sains et pathologiques. Cette méthode utilise un a priori spatio-temporel sur l'activité électrique cardiaque ainsi que le principe de contradiction afin de trouver un paramètre de régularisation adéquat. / The estimation of solutions of the inverse problem of Electrocardiography (ECG) represents a major interest in the diagnosis and catheter-based therapy of cardiac arrhythmia. The latter consists in non-invasively providing 3D images of the spatial distribution of cardiac electrical activity based on anatomical and electrocardiographic data. On the one hand, this problem is challenging due to its ill-posed nature. On the other hand, validation of proposed methods on clinical data remains very limited. Another way to proceed is by evaluating these methods performance on data simulated by a cardiac electrical model. For this application, existing models are either too complex or do not produce realistic cardiac patterns. As a first step, we designed a low-resolution heart-torso model that generates realistic cardiac mappings and ECGs in healthy and pathological cases. This model is built upon a simplified heart torso geometry and implements the monodomain formalism by using the Finite Element Method (FEM). Parameters were identified using an evolutionary approach and their influence were analyzed by a screening method. In a second step, a new approach for solving the inverse problem was proposed and compared to classical methods in healthy and pathological cases. This method uses a spatio-temporal a priori on the cardiac electrical activity and the discrepancy principle for finding an adequate regularization parameter.

Drug- and cell-based strategies for treating heart disease, including myocardial infarction, face significant roadblocks on the path to the clinic, a primary obstacle being the lack of information-rich in vitro human model systems. Conventional model systems are hampered by at least one of three fundamental limitations which include a) the lack of an in vivo-like microenvironment specifically engineered for the input cell population, b) a relatively low-throughput assays, and c) the low-content nature of output parameters. We describe an integrated computational, design, and experimental strategy for the rational design of a microfabricated high-content screening platform which we term the Cardiac MicroWire (CMW) system. Within this system, we recapitulate the basic microenvironment found in the heart, one which integrates cardiomyocytes, non-myocytes, the extracellular matrix, and dynamic electromechanical forces. Our results highlight the CMW system’s potential as a powerful discovery tool for screening small molecules and transplantable cells toward heart regeneration therapies.

Drug- and cell-based strategies for treating heart disease, including myocardial infarction, face significant roadblocks on the path to the clinic, a primary obstacle being the lack of information-rich in vitro human model systems. Conventional model systems are hampered by at least one of three fundamental limitations which include a) the lack of an in vivo-like microenvironment specifically engineered for the input cell population, b) a relatively low-throughput assays, and c) the low-content nature of output parameters. We describe an integrated computational, design, and experimental strategy for the rational design of a microfabricated high-content screening platform which we term the Cardiac MicroWire (CMW) system. Within this system, we recapitulate the basic microenvironment found in the heart, one which integrates cardiomyocytes, non-myocytes, the extracellular matrix, and dynamic electromechanical forces. Our results highlight the CMW system’s potential as a powerful discovery tool for screening small molecules and transplantable cells toward heart regeneration therapies.