An object-oriented computational model to study cardiopulmonary hemodynamic interactions in humans

Comparing physiological impacts of positive pressure ventilation versus self-breathing via a versatile cardiopulmonary model incorporating a novel alveoli opening mechanism

Mathematical models can be used to generate high-fidelity simulations of the cardiopulmonary system. Such models, when applied to real patients, can provide valuable insights into underlying physiological processes that are hard for clinicians to observe directly. In this work, we propose a novel modelling strategy capable of generating scenario-specific cardiopulmonary simulations to replicate the vital physiological signals clinicians use to determine the state of a patient. This model is composed of a tree-like pulmonary system that features a novel, non-linear alveoli opening strategy, based on the dynamics of balloon inflation, that interacts with the cardiovascular system via the thorax. A baseline simulation of the model is performed to measure the response of the system during spontaneous breathing which is subsequently compared to the same system under mechanical ventilation. To test the new lung opening mechanics and systematic recruitment of alveolar units, a positive end-expiratory pressure (PEEP) test is performed and its results are then compared to simulations of a deep spontaneous breath. The system displays a marked decrease in tidal volume as PEEP increases, replicating a sigmoidal curve relationship between volume and pressure. At high PEEP, cardiovascular function is shown to be visibly impaired, in contrast to the deep breath test where normal function is maintained.

A mathematical model to assess the effects of COVID-19 on the cardiocirculatory system

Impaired cardiac function has been described as a frequent complication of COVID-19-related pneumonia. To investigate possible underlying mechanisms, we represented the cardiovascular system by means of a lumped-parameter 0D mathematical model. The model was calibrated using clinical data, recorded in 58 patients hospitalized for COVID-19-related pneumonia, to make it patient-specific and to compute model outputs of clinical interest related to the cardiocirculatory system. We assessed, for each patient with a successful calibration, the statistical reliability of model outputs estimating the uncertainty intervals. Then, we performed a statistical analysis to compare healthy ranges and mean values (over patients) of reliable model outputs to determine which were significantly altered in COVID-19-related pneumonia. Our results showed significant increases in right ventricular systolic pressure, diastolic and mean pulmonary arterial pressure, and capillary wedge pressure. Instead, physical quantities related to the systemic circulation were not significantly altered. Remarkably, statistical analyses made on raw clinical data, without the support of a mathematical model, were unable to detect the effects of COVID-19-related pneumonia in pulmonary circulation, thus suggesting that the use of a calibrated 0D mathematical model to describe the cardiocirculatory system is an effective tool to investigate the impairments of the cardiocirculatory system associated with COVID-19.

Modeling the cardiac response to hemodynamic changes associated with COVID-19: a computational study

Emerging studies address how COVID-19 infection can impact the human cardiovascular system. This relates particularly to the development of myocardial injury, acute coronary syndrome, myocarditis, arrhythmia, and heart failure. Prospective treatment approach is advised for these patients. To study the interplay between local changes (reduced contractility), global variables (peripheral resistances, heart rate) and the cardiac function, we considered a lumped parameters computational model of the cardiovascular system and a three-dimensional multiphysics model of cardiac electromechanics. Our mathematical model allows to simulate the systemic and pulmonary circulations, the four cardiac valves and the four heart chambers, through equations describing the underlying physical processes. By the assessment of conventionally relevant parameters of cardiac function obtained through our numerical simulations, we propose a computational model to effectively reveal the interactions between the cardiac and pulmonary functions in virtual subjects with normal and impaired cardiac function at baseline affected by mild or severe COVID-19.

Echocardiography-guided percutaneous left ventricular intracavitary injection as a cell delivery approach in infarcted mice

In the field of cell therapy for heart disease, a new paradigm of repeated dosing of cells has recently emerged. However, the lack of a repeatable cell delivery method in preclinical studies in rodents is a major obstacle to investigating this paradigm. We have established and standardized a method of echocardiography-guided percutaneous left ventricular intracavitary injection (echo-guided LV injection) as a cell delivery approach in infarcted mice. Here, we describe the method in detail and address several important issues regarding it. First, by integrating anatomical and echocardiographic considerations, we have established strategies to determine a safe anatomical window for injection in infarcted mice. Second, we summarize our experience with this method (734 injections). The overall survival rate was 91.4%. Third, we examined the efficacy of this cell delivery approach. Compared with vehicle treatment, cardiac mesenchymal cells (CMCs) delivered via this method improved cardiac function assessed both echocardiographically and hemodynamically. Furthermore, repeated injections of CMCs via this method yielded greater cardiac function improvement than single-dose administration. Echo-guided LV injection is a feasible, reproducible, relatively less invasive and effective delivery method for cell therapy in murine models of heart disease. It is an important approach that could move the field of cell therapy forward, especially with regard to repeated cell administrations.

Object-oriented modeling of thoracic fluid balance to study cardiogenic pulmonary congestion in humans

BACKGROUND AND OBJECTIVE

We hypothesized that a biophysical computational model implemented in an object-oriented modeling language (OOML) would provide physiological information and simulative data to study the development and treatment of cardiogenic pulmonary congestion.

METHODS

This work is based on the object-oriented cardiopulmonary interaction introduced in [1]. This paper describes the novel model components required to study cardiogenic pulmonary congestion: i) interstitial fluid exchange related to the Starling equation, ii) the lymphatic pump, and iii) the interconnection of these elements with the original cardiopulmonary model. The presented model succeeds in i) describing lymphatic flow at the capillary artery and venous end, ii) activation of the lymphatic pump at elevated pulmonary pressures, and iii) the simulation of the different safety factors related to lung tissue, osmotic gradient, and the lymphatic system during the development of lung congestion.

RESULTS

Simulations show a qualitative correlation between model behavior and physiological data from literature. The model also demonstrates the beneficial effect of continuous positive airway pressure therapy on fluid clearance and respiratory mechanics.

CONCLUSION

This study demonstrates the successful use of OOML to describe the development of cardiogenic congestion by introducing a model of the lymphatic system and the thoracic fluid balance system, as well as connecting them to the existing cardiopulmonary model.

A novel intelligent object-oriented three-dimensional simulation system for physics experimentation

Despite the fact that there being a large literature on simulation, there is as yet no generic paradigm or architecture to develop a three-dimensional (3-D) simulator which depends on autonomous intelligent objects. This has motivated us to introduce a 3-D simulation system based on intelligent objects for Physics Experimentation. We formulated the system’s components as an object-orientation model. So, the entities in every experiment’s work cell are modeled by characterizing their properties and functions into classes and objects of the system hierarchy. Intelligent objects are realized by developing a knowledge base (KB) that captures a set of rules/algorithms that operate on 3-D objects. Rules fall into two categories: action and property rules. In the simulation layer, the student is allowed, by using the virtual system, to stroll throughout the Physics laboratory in light of a walking model. Student gets to a simulation region to do an experiment through the detection of mathematical collision. From software engineering perspective, the proposed system facilitates the Physics experiment through making the specification of its applicable parts more modular and reusable. Moreover, a major pedagogical objective is achieved by permitting the student tuning parameters, fixing component of a device then visualizing outputs. This provides student well interpretation by viewing how distinct parameters affect the outcomes of the experiment. With the objective of student performance measuring, we utilized an exploratory group relying upon pre- and post-testing. The application results demonstrate that the simulator contributes positively to student performance in regard to practical Physics.