Variable Ventilation as a Diagnostic Tool for the Injured Lung

Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review

Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion in the chest and abdomen can lead to uncertainty in the shape, volume, and location of the tumor, making treatment of the chest and abdomen difficult. Therefore, compensation for respiratory motion is very important in clinical treatment. The purpose of this review was to discuss the research and development of respiratory movement monitoring and prediction in thoracic and abdominal surgery, as well as introduce the current research status. The integration of modern respiratory motion compensation technology with advanced sensor detection technology, medical-image-guided therapy, and artificial intelligence technology is discussed and analyzed. The future research direction of intraoperative thoracic and abdominal respiratory motion compensation should be non-invasive, non-contact, use a low dose, and involve intelligent development. The complexity of the surgical environment, the constraints on the accuracy of existing image guidance devices, and the latency of data transmission are all present technical challenges.

An approach to study recruitment/derecruitment dynamics in a patient-specific computational model of an injured human lung

We present a new approach for physics-based computational modeling of diseased human lungs. Our main object is the development of a model that takes the novel step of incorporating the dynamics of airway recruitment/derecruitment into an anatomically accurate, spatially resolved model of respiratory system mechanics, and the relation of these dynamics to airway dimensions and the biophysical properties of the lining fluid. The importance of our approach is that it potentially allows for more accurate predictions of where mechanical stress foci arise in the lungs, since it is at these locations that injury is thought to arise and propagate from. We match the model to data from a patient with acute respiratory distress syndrome (ARDS) to demonstrate the potential of the model for revealing the underlying derangements in ARDS in a patient-specific manner. To achieve this, the specific geometry of the lung and its heterogeneous pattern of injury are extracted from medical CT images. The mechanical behavior of the model is tailored to the patient's respiratory mechanics using measured ventilation data. In retrospective simulations of various clinically performed, pressure-driven ventilation profiles, the model adequately reproduces clinical quantities measured in the patient such as tidal volume and change in pleural pressure. The model also exhibits physiologically reasonable lung recruitment dynamics and has the spatial resolution to allow the study of local mechanical quantities such as alveolar strains. This modeling approach advances our ability to perform patient-specific studies in silico, opening the way to personalized therapies that will optimize patient outcomes.

Tracking respiratory mechanics around natural breathing rates via variable ventilation

Measuring respiratory resistance and elastance as a function of time, tidal volume, respiratory rate, and positive end-expiratory pressure can guide mechanical ventilation. However, current measurement techniques are limited since they are assessed intermittently at non-physiological frequencies or involve specialized equipment. To this end, we introduce ZVV, a practical approach to continuously track resistance and elastance during Variable Ventilation (VV), in which frequency and tidal volume vary from breath-to-breath. ZVV segments airway pressure and flow recordings into individual breaths, calculates resistance and elastance for each breath, bins them according to frequency or tidal volume and plots the results against bin means. ZVV's feasibility was assessed clinically in five human patients with acute lung injury, experimentally in five mice ventilated before and after lavage injury, and computationally using a viscoelastic respiratory model. ZVV provided continuous measurements in both settings, while the computational study revealed <2% estimation errors. Our findings support ZVV as a feasible technique to assess respiratory mechanics under physiological conditions. Additionally, in humans, ZVV detected a decrease in resistance and elastance with time by 12.8% and 6.2%, respectively, suggesting that VV can improve lung recruitment in some patients and can therefore potentially serve both as a dual diagnostic and therapeutic tool.

Alveolar Micromechanics in Bleomycin-induced Lung Injury

Lung injury results in intratidal alveolar recruitment and derecruitment and alveolar collapse, creating stress concentrators that increase strain and aggravate injury. In this work, we sought to describe alveolar micromechanics during mechanical ventilation in bleomycin-induced lung injury and surfactant replacement therapy. Structure and function were assessed in rats 1 day and 3 days after intratracheal bleomycin instillation and after surfactant replacement therapy. Pulmonary system mechanics were measured during ventilation with positive end-expiratory pressures (PEEPs) between 1 and 10 cm H2O, followed by perfusion fixation at end-expiratory pressure at airway opening (Pao) values of 1, 5, 10, and 20 cm H2O for quantitative analyses of lung structure. Lung structure and function were used to parameterize a physiologically based, multicompartment computational model of alveolar micromechanics. In healthy controls, the numbers of open alveoli remained stable in a range of Pao = 1-20 cm H2O, whereas bleomycin-challenged lungs demonstrated progressive alveolar derecruitment with Pao < 10 cm H2O. At Day 3, ∼40% of the alveoli remained closed at high Pao, and alveolar size heterogeneity increased. Simulations of injured lungs predicted that alveolar recruitment pressures were much greater than the derecruitment pressures, so that minimal intratidal recruitment and derecruitment occurred during mechanical ventilation with a tidal volume of 10 ml/kg body weight over a range of PEEPs. However, the simulations also predicted a dramatic increase in alveolar strain with injury that we attribute to alveolar interdependence. These findings suggest that in progressive lung injury, alveolar collapse with increased distension of patent (open) alveoli dominates alveolar micromechanics. PEEP and surfactant substitution reduce alveolar collapse and dynamic strain but increase static strain.

Ventilator-induced lung injury and lung mechanics

Mechanical ventilation applies physical stresses to the tissues of the lung and thus may give rise to ventilator-induced lung injury (VILI), particular in patients with acute respiratory distress syndrome (ARDS). The most dire consequences of VILI result from injury to the blood-gas barrier. This allows plasma-derived fluid and proteins to leak into the airspaces where they flood some alveolar regions, while interfering with the functioning of pulmonary surfactant in those regions that remain open. These effects are reflected in commensurately increased values of dynamic lung elastance (EL ), a quantity that in principle is readily measured at the bedside. Recent mathematical/computational modeling studies have shown that the way in which EL varies as a function of both time and positive end-expiratory pressure (PEEP) reflects the nature and degree of lung injury, and can even be used to infer the separate contributions of volutrauma and atelectrauma to VILI. Interrogating such models for minimally injurious regimens of mechanical ventilation that apply to a particular lung may thus lead to personalized approaches to the ventilatory management of ARDS.

Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel “virtual EIT” module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling.

NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results obtained in an acute respiratory distress syndrome patient show the potential of this approach for personalized computationally guided optimization of mechanical ventilation in future.

Predicting the response of the injured lung to the mechanical breath profile

Mechanical ventilation is a crucial component of the supportive care provided to patients with acute respiratory distress syndrome. Current practice stipulates the use of a low tidal volume (VT) of 6 ml/kg ideal body weight, the presumptive notion being that this limits overdistension of the tissues and thus reduces volutrauma. We have recently found, however, that airway pressure release ventilation (APRV) is efficacious at preventing ventilator-induced lung injury, yet APRV has a very different mechanical breath profile compared with conventional low-VT ventilation. To gain insight into the relative merits of these two ventilation modes, we measured lung mechanics and derecruitability in rats before and following Tween lavage. We fit to these lung mechanics measurements a computational model of the lung that accounts for both the degree of tissue distension of the open lung and the amount of lung derecruitment that takes place as a function of time. Using this model, we predicted how tissue distension, open lung fraction, and intratidal recruitment vary as a function of ventilator settings both for conventional low-VT ventilation and for APRV. Our predictions indicate that APRV is more effective at recruiting the lung than low-VT ventilation, but without causing more overdistension of the tissues. On the other hand, low-VT ventilation generally produces less intratidal recruitment than APRV. Predictions such as these may be useful for deciding on the relative benefits of different ventilation modes and thus may serve as a means for determining how to ventilate a given lung in the least injurious fashion.