Computational models for the study of heart-lung interactions in mammals

Cardiovascular effects of exercise induced dynamic hyperinflation in COPD patients-Dynamically hyperinflated and non-hyperinflated subgroups

INTRODUCTION

An increase in respiratory rate and expiratory flow limitation can facilitate dynamic hyperinflation (DH), which may cause an element of the intrathoracic pressure in connection with the worsening of venous return, with negative effect on stroke volume (SV) and cardiac output (CO). It has been unclassified, whether poor circulatory adaptation to exercise can be attributed to DH or poor cardio-vascular performance itself in COPD. Only a subset of COPD patients exhibit dynamic hyperinflation during exercise.

PATIENTS AND METHODS

We designed a study to show how lung mechanical and cardiovascular parameters change in hyperinflated and non-hyperinflated COPD patients during exercise with a new experimental set-up. Thirty-three COPD patients with similar severity of COPD and left ventricular performance (20 men, 13 women, mean±SD age: 65,36±6,95 years) participated. We measured the cardiovascular parameters with a non-invasive device (Finometer-pro) including the left ventricular ejection time index (LVETi) and estimated the change of DH with inspiratory capacity (IC) manoeuvres during exercise.

RESULTS

Twenty-one subjects exhibited DH (DH group) and 12 did not (non-DH group). The measurement results were given in mean ± SD and difference between the values measured during maximal load and rest also (ΔX = Xmax.load-Xrest). ΔSV and ΔCO were significantly higher in non-DH vs. DH patients (ΔSV: non-DH 9,7 ± 13,22 ml vs. DH -3,6 ± 14,34 ml, p = 0.0142; ΔCO: non-DH 2,26 ± 1,46 l/min vs. DH 0,88 ± 1,35 l/min, p = 0.0024). LVETi was not different between the two groups. Calculated oxygen delivery (DO2) during maximal load was significantly higher in non-DH group.

CONCLUSION

We concluded that worse cardiovascular adaptation to exercise of COPD patients can be associated with exercise-induced DH in a similar cardiovascular aged COPD group.

Heartbeats entrain breathing via baroreceptor-mediated modulation of expiratory activity

NEW FINDINGS

  Cardio-ventilatory coupling refers to the onset of inspiration occurring at a preferential latency following the last heartbeat (HB) in expiration. According to the cardiac-trigger hypothesis, the pulse pressure initiates an inspiration via baroreceptor activation. However, the central neural substrate mediating this coupling remains undefined. Using a combination of animal data, human data and mathematical modelling, this study tests the hypothesis that the HB, by way of pulsatile baroreflex activation, controls the initiation of inspiration that occurs through a rapid neural activation loop from the carotid baroreceptors to Bötzinger complex expiratory neurons.

ABSTRACT

Cardio-ventilatory coupling refers to a heartbeat (HB) occurring at a preferred latency prior to the next breath. We hypothesized that the pressure pulse generated by a HB activates baroreceptors that modulate brainstem expiratory neuronal activity and delay the initiation of inspiration. In supine male subjects, we recorded ventilation, electrocardiogram and blood pressure during 20-min epochs of baseline, slow-deep breathing and recovery. In in situ rodent preparations, we recorded brainstem activity in response to pulses of perfusion pressure. We applied a well-established respiratory network model to interpret these data. In humans, the latency between a HB and onset of inspiration was consistent across different breathing patterns. In in situ preparations, a transient pressure pulse during expiration activated a subpopulation of expiratory neurons normally active during post-inspiration, thus delaying the next inspiration. In the model, baroreceptor input to post-inspiratory neurons accounted for the effect. These studies are consistent with baroreflex activation modulating respiration through a pauci-synaptic circuit from baroreceptors to onset of inspiration.

Integrative Computational Models of Lung Structure-Function Interactions

Anatomically based integrative models of the lung and their interaction with other key components of the respiratory system provide unique capabilities for investigating both normal and abnormal lung function. There is substantial regional variability in both structure and function within the normal lung, yet it remains capable of relatively efficient gas exchange by providing close matching of air delivery (ventilation) and blood delivery (perfusion) to regions of gas exchange tissue from the scale of the whole organ to the smallest continuous gas exchange units. This is despite remarkably different mechanisms of air and blood delivery, different fluid properties, and unique scale-dependent anatomical structures through which the blood and air are transported. This inherent heterogeneity can be exacerbated in the presence of disease or when the body is under stress. Current computational power and data availability allow for the construction of sophisticated data-driven integrative models that can mimic respiratory system structure, function, and response to intervention. Computational models do not have the same technical and ethical issues that can limit experimental studies and biomedical imaging, and if they are solidly grounded in physiology and physics they facilitate investigation of the underlying interaction between mechanisms that determine respiratory function and dysfunction, and to estimate otherwise difficult-to-access measures. © 2021 American Physiological Society. Compr Physiol 11:1501-1530, 2021.

Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation

Mechanical ventilation (MV) is a core therapy in the intensive care unit (ICU). Some patients rely on MV to support breathing. However, it is a difficult therapy to optimise, where inter- and intra- patient variability leads to significantly increased risk of lung damage. Excessive volume and/or pressure can cause volutrauma or barotrauma, resulting in increased length of time on ventilation, length of stay, cost and mortality. Virtual patient modelling has changed care in other areas of ICU medicine, enabling more personalized and optimal care, and have emerged for volume-controlled MV. This research extends this MV virtual patient model into the increasingly more commonly used pressure-controlled MV mode. The simulation methods are extended to use pressure, instead of both volume and flow, as the known input, increasing the output variables to be predicted (flow and its integral, volume). The model and methods are validated using data from N = 14 pressure-control ventilated patients during recruitment maneuvers, with n = 558 prediction tests over changes of PEEP ranging from 2 to 16 cmH₂O. Prediction errors for peak inspiratory volume for an increase of 16 cmH₂O were 80 [30 - 140] mL (15.9 [8.4 - 31.0]%), with RMS fitting errors of 0.05 [0.03 - 0.12] L. These results show very good prediction accuracy able to guide personalised MV care.

Biomedical engineer's guide to the clinical aspects of intensive care mechanical ventilation

Background

Mechanical ventilation is an essential therapy to support critically ill respiratory failure patients. Current standards of care consist of generalised approaches, such as the use of positive end expiratory pressure to inspired oxygen fraction (PEEP–FiO₂) tables, which fail to account for the inter- and intra-patient variability between and within patients. The benefits of higher or lower tidal volume, PEEP, and other settings are highly debated and no consensus has been reached. Moreover, clinicians implicitly account for patient-specific factors such as disease condition and progression as they manually titrate ventilator settings. Hence, care is highly variable and potentially often non-optimal. These conditions create a situation that could benefit greatly from an engineered approach. The overall goal is a review of ventilation that is accessible to both clinicians and engineers, to bridge the divide between the two fields and enable collaboration to improve patient care and outcomes. This review does not take the form of a typical systematic review. Instead, it defines the standard terminology and introduces key clinical and biomedical measurements before introducing the key clinical studies and their influence in clinical practice which in turn flows into the needs and requirements around how biomedical engineering research can play a role in improving care. Given the significant clinical research to date and its impact on this complex area of care, this review thus provides a tutorial introduction around the review of the state of the art relevant to a biomedical engineering perspective.

Discussion

This review presents the significant clinical aspects and variables of ventilation management, the potential risks associated with suboptimal ventilation management, and a review of the major recent attempts to improve ventilation in the context of these variables. The unique aspect of this review is a focus on these key elements relevant to engineering new approaches. In particular, the need for ventilation strategies which consider, and directly account for, the significant differences in patient condition, disease etiology, and progression within patients is demonstrated with the subsequent requirement for optimal ventilation strategies to titrate for patient- and time-specific conditions.

Conclusion

Engineered, protective lung strategies that can directly account for and manage inter- and intra-patient variability thus offer great potential to improve both individual care, as well as cohort clinical outcomes.

Cardiorespiratory interactions in humans and animals: rhythms for life

The cardiorespiratory system exhibits oscillations from a range of sources. One of the most studied oscillations is heart rate variability, which is thought to be beneficial and can serve as an index of a healthy cardiovascular system. Heart rate variability is dampened in many diseases including depression, autoimmune diseases, hypertension, and heart failure. Thus, understanding the interactions that lead to heart rate variability, and its physiological role, could help with prevention, diagnosis, and treatment of cardiovascular diseases. In this review, we consider three types of cardiorespiratory interactions: respiratory sinus arrhythmia (variability in heart rate at the frequency of breathing), cardioventilatory coupling (synchronization between the heart beat and the onset of inspiration), and respiratory stroke volume synchronization (the constant phase difference between the right and the left stroke volumes over one respiratory cycle). While the exact physiological role of these oscillations continues to be debated, the redundancies in the mechanisms responsible for its generation and its strong evolutionary conservation point to the importance of cardiorespiratory interactions. The putative mechanisms driving cardiorespiratory oscillations as well as the physiological significance of these oscillations will be reviewed. We suggest that cardiorespiratory interactions have the capacity to both dampen the variability in systemic blood flow as well as improve the efficiency of work done by the heart while maintaining physiological levels of arterial CO2. Given that reduction in variability is a prognostic indicator of disease, we argue that restoration of this variability via pharmaceutical or device-based approaches may be beneficial in prolonging life.

Central regulation of heart rate and the appearance of respiratory sinus arrhythmia: new insights from mathematical modeling

A minimal model for the neural control of heart rate (HR) has been developed with the aim of better understanding respiratory sinus arrhythmia (RSA)--a modulation of HR at the frequency of breathing. This model consists of two differential equations and is integrated into a previously-published model of gas exchange. The heart period is assumed to be affected primarily by the parasympathetic signal, with the sympathetic signal taken as a parameter in the model. We include the baroreflex, mechanical stretch-receptor feedback from the lungs, and central modulation of the cardiac vagal tone by the respiratory drive. Our model mimics a range of experimental observations and provides several new insights. Most notably, the model mimics the growth in the amplitude of RSA with decreasing respiratory frequency up to 7 breaths per minute (for humans). Our model then mimics the decrease in the amplitude of RSA at frequencies below 7 breaths per minute and predicts that this decrease is due to the baroreflex (we show this both numerically and analytically with a linear baroreflex). Another new prediction of the model is that the gating of the baroreflex leads to the dependency of RSA on mean vagal tone. The new model was also used to test two previously-suggested hypotheses regarding the physiological function of RSA and supports the hypothesis that RSA minimizes the work done by the heart while maintaining physiological levels of arterial CO2. These and other new insights the model provides extend our understanding of the integrative nature of vagal control of the heart.

Physiological and pathophysiological interactions between the respiratory central pattern generator and the sympathetic nervous system

Respiratory modulation seen in the sympathetic nerve activity (SNA) implies that the respiratory and sympathetic networks interact. During hypertension elicited by chronic intermittent hypoxia (CIH), the SNA displays an enhanced respiratory modulation reflecting strengthened interactions between the networks. In this chapter, we review a series of experimental and modeling studies that help elucidate possible mechanisms of sympatho-respiratory coupling. We conclude that this coupling significantly contributes to both the sympathetic baroreflex and the augmented sympathetic activity after exposure to CIH. This conclusion is based on the following findings. (1) Baroreceptor activation results in perturbation of the respiratory pattern via transient activation of postinspiratory neurons in the Bötzinger complex (BötC). The same BötC neurons are involved in the respiratory modulation of SNA, and hence provide an additional pathway for the sympathetic baroreflex. (2) Under hypercapnia, phasic activation of abdominal motor nerves (AbN) is accompanied by synchronous discharges in SNA due to the common source of this rhythmic activity in the retrotrapezoid nucleus (RTN). CIH conditioning increases the CO2 sensitivity of central chemoreceptors in the RTN which results in the emergence of AbN and SNA discharges under normocapnic conditions similar to those observed during hypercapnia in naïve animals. Thus, respiratory-sympathetic interactions play an important role in defining sympathetic output and significantly contribute to the sympathetic activity and hypertension under certain physiological or pathophysiological conditions, and the theoretical framework presented may be instrumental in understanding of malfunctioning control of sympathetic activity in a variety of disease states.

Integrative approaches for modeling regulation and function of the respiratory system

Mathematical models have been central to understanding the interaction between neural control and breathing. Models of the entire respiratory system-which comprises the lungs and the neural circuitry that controls their ventilation-have been derived using simplifying assumptions to compartmentalize each component of the system and to define the interactions between components. These full system models often rely-through necessity-on empirically derived relationships or parameters, in addition to physiological values. In parallel with the development of whole respiratory system models are mathematical models that focus on furthering a detailed understanding of the neural control network, or of the several functions that contribute to gas exchange within the lung. These models are biophysically based, and rely on physiological parameters. They include single-unit models for a breathing lung or neural circuit, through to spatially distributed models of ventilation and perfusion, or multicircuit models for neural control. The challenge is to bring together these more recent advances in models of neural control with models of lung function, into a full simulation for the respiratory system that builds upon the more detailed models but remains computationally tractable. This requires first understanding the mathematical models that have been developed for the respiratory system at different levels, and which could be used to study how physiological levels of O2 and CO2 in the blood are maintained.