Regional Lung Perfusion Analysis in Experimental ARDS by Electrical Impedance and Computed Tomography

A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis

Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, is characterized by chronic pulmonary inflammation and progressive fibrosis, potentially leading to respiratory and/or heart failure. Workers exposed to dust, such as coal miners, foundry workers, and construction workers, are at risk of pneumoconiosis. This review synthesizes the international and national classifications, epidemiological characteristics, strategies for prevention, clinical manifestations, diagnosis, pathogenesis, and treatment of pneumoconiosis. Current research on the pathogenesis of pneumoconiosis focuses on the influence of autophagy, apoptosis, and pyroptosis on the progression of the disease. In addition, factors such as lipopolysaccharide and nicotine have been found to play crucial roles in the development of pneumoconiosis. This review provides a comprehensive summary of the most fundamental achievements in the treatment of pneumoconiosis with the purpose of indicating the future direction of its treatment and control. New technologies of integrative omics, artificial intelligence, systemic administration of mesenchymal stromal cells have proved useful in solving the conundrum of pneumoconiosis. These directional studies will provide novel therapeutic targets for the treatment of pneumoconiosis.

Absolute values of regional ventilation-perfusion mismatch in patients with ARDS monitored by electrical impedance tomography and the role of dead space and shunt compensation

BACKGROUND

Assessment of regional ventilation/perfusion (V'/Q) mismatch using electrical impedance tomography (EIT) represents a promising advancement for personalized management of the acute respiratory distress syndrome (ARDS). However, accuracy is still hindered by the need for invasive monitoring to calibrate ventilation and perfusion. Here, we propose a non-invasive correction that uses only EIT data and characterized patients with more pronounced compensation of V'/Q mismatch.

METHODS

We enrolled twenty-one ARDS patients on controlled mechanical ventilation. Cardiac output was measured invasively, and ventilation and perfusion were assessed by EIT. Relative V'/Q maps by EIT were calibrated to absolute values using the minute ventilation to invasive cardiac output (MV/CO) ratio (V'/Q-ABS), left unadjusted (V'/Q-REL), or corrected by MV/CO ratio derived from EIT data (V'/Q-CORR). The ratio between ventilation to dependent regions and perfusion reaching shunted units ( V D ' /QSHUNT) was calculated as an index of more effective hypoxic pulmonary vasoconstriction. The ratio between perfusion to non-dependent regions and ventilation to dead space units (QND/ V DS ' ) was calculated as an index of hypocapnic pneumoconstriction.

RESULTS

Our calibration factor correlated with invasive MV/CO (r = 0.65, p < 0.001), showed good accuracy and no apparent bias. Compared to V'/Q-ABS, V'/Q-REL maps overestimated ventilation (p = 0.013) and perfusion (p = 0.002) to low V'/Q units and underestimated ventilation (p = 0.011) and perfusion (p = 0.008) to high V'/Q units. The heterogeneity of ventilation and perfusion reaching different V'/Q compartments was underestimated. V'/Q-CORR maps eliminated all these differences with V'/Q-ABS (p > 0.05). HigherV D ' / Q SHUNT correlated with higher PaO2/FiO2 (r = 0.49, p = 0.025) and lower shunt fraction (ρ =  - 0.59, p = 0.005). HigherQ ND / V DS ' correlated with lower PEEP (ρ =  - 0.62, p = 0.003) and plateau pressure (ρ =  - 0.59, p = 0.005). Lower values of both indexes were associated with less ventilator-free days (p = 0.05 and p = 0.03, respectively).

CONCLUSIONS

Regional V'/Q maps calibrated with a non-invasive EIT-only method closely approximate the ones obtained with invasive monitoring. Higher efficiency of shunt compensation improves oxygenation while compensation of dead space is less needed at lower airway pressure. Patients with more effective compensation mechanisms could have better outcomes.

Technical Principles and Clinical Applications of Electrical Impedance Tomography in Pulmonary Monitoring

Pulmonary monitoring is crucial for the diagnosis and management of respiratory conditions, especially after the epidemic of coronavirus disease. Electrical impedance tomography (EIT) is an alternative non-radioactive tomographic imaging tool for monitoring pulmonary conditions. This review proffers the current EIT technical principles and applications on pulmonary monitoring, which gives a comprehensive summary of EIT applied on the chest and encourages its extensive usage to clinical physicians. The technical principles involving EIT instrumentations and image reconstruction algorithms are explained in detail, and the conditional selection is recommended based on clinical application scenarios. For applications, specifically, the monitoring of ventilation/perfusion (V/Q) is one of the most developed EIT applications. The matching correlation of V/Q could indicate many pulmonary diseases, e.g., the acute respiratory distress syndrome, pneumothorax, pulmonary embolism, and pulmonary edema. Several recently emerging applications like lung transplantation are also briefly introduced as supplementary applications that have potential and are about to be developed in the future. In addition, the limitations, disadvantages, and developing trends of EIT are discussed, indicating that EIT will still be in a long-term development stage before large-scale clinical applications.

Time-domain and 3D methods for lung perfusion data clustering in electrical impedance tomography

The paper discusses the use of clustering methods to segment electrical impedance tomography images when assessing pulmonary perfusion through hypertonic saline injection. The clustering method assumes hybrid pixels (with both heart and lung partial volume effects) and lung pixels (solely due to lung perfusion). The study used data from 51 perfusions in 8 healthy and injured mechanically ventilated swine to generate ground truth masks and evaluate the clustering performance. Among the compared methods, k-means method with the correlation metric, was found to be the most effective and balanced performance. Achieving a median sensitivity and specificity of 84% while aiming to minimize false negative cases. This is important for avoiding the attribution of high lung perfusion values to voxels with mixed heart and lung effects.

Imaging the pulmonary vasculature in acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is characterized by a redistribution of regional lung perfusion that impairs gas exchange. While speculative, experimental evidence suggests that perfusion redistribution may contribute to regional inflammation and modify disease progression. Unfortunately, tools to visualize and quantify lung perfusion in patients with ARDS are lacking. This review explores recent advances in perfusion imaging techniques that aim to understand the pulmonary circulation in ARDS. Dynamic contrast-enhanced computed tomography captures first-pass kinetics of intravenously injected dye during continuous scan acquisitions. Different contrast characteristics and kinetic modeling have improved its topographic measurement of pulmonary perfusion with high spatial and temporal resolution. Dual-energy computed tomography can map the pulmonary blood volume of the whole lung with limited radiation exposure, enabling its application in clinical research. Electrical impedance tomography can obtain serial topographic assessments of perfusion at the bedside in response to treatments such as inhaled nitric oxide and prone position. Ongoing technological improvements and emerging techniques will enhance lung perfusion imaging and aid its incorporation into the care of patients with ARDS.

Electrical Impedance Tomography Identifies Evolution of Regional Perfusion in a Porcine Model of Acute Respiratory Distress Syndrome

BACKGROUND

Bedside electrical impedance tomography could be useful to visualize evolving pulmonary perfusion distributions when acute respiratory distress syndrome worsens or in response to ventilatory and positional therapies. In experimental acute respiratory distress syndrome, this study evaluated the agreement of electrical impedance tomography and dynamic contrast-enhanced computed tomography perfusion distributions at two injury time points and in response to increased positive end-expiratory pressure (PEEP) and prone position.

METHODS

Eleven mechanically ventilated (VT 8 ml · kg-1) Yorkshire pigs (five male, six female) received bronchial hydrochloric acid (3.5 ml · kg-1) to invoke lung injury. Electrical impedance tomography and computed tomography perfusion images were obtained at 2 h (early injury) and 24 h (late injury) after injury in supine position with PEEP 5 and 10 cm H2O. In eight animals, electrical impedance tomography and computed tomography perfusion imaging were also conducted in the prone position. Electrical impedance tomography perfusion (QEIT) and computed tomography perfusion (QCT) values (as percentages of image total) were compared in eight vertical regions across injury stages, levels of PEEP, and body positions using mixed-effects linear regression. The primary outcome was agreement between QEIT and QCT, defined using limits of agreement and Pearson correlation coefficient.

RESULTS

Pao2/Fio2 decreased over the course of the experiment (healthy to early injury, -253 [95% CI, -317 to -189]; early to late injury, -88 [95% CI, -151 to -24]). The limits of agreement between QEIT and QCT were -4.66% and 4.73% for the middle 50% quantile of average regional perfusion, and the correlation coefficient was 0.88 (95% CI, 0.86 to 0.90]; P < 0.001). Electrical impedance tomography and computed tomography showed similar perfusion redistributions over injury stages and in response to increased PEEP. QEIT redistributions after positional therapy underestimated QCT in ventral regions and overestimated QCT in dorsal regions.

CONCLUSIONS

Electrical impedance tomography closely approximated computed tomography perfusion measures in experimental acute respiratory distress syndrome, in the supine position, over injury progression and with increased PEEP. Further validation is needed to determine the accuracy of electrical impedance tomography in measuring perfusion redistributions after positional changes.

EDITOR’S PERSPECTIVE

Regional lung perfusion using different indicators in electrical impedance tomography

Management of acute respiratory distress syndrome (ARDS) is classically guided by protecting the injured lung and mitigating damage from mechanical ventilation. Yet the natural history of ARDS is also dictated by disruption in lung perfusion. Unfortunately, diagnosis and treatment are hampered by the lack of bedside perfusion monitoring. Electrical impedance tomography is a portable imaging technique that can estimate regional lung perfusion in experimental settings from the kinetic analysis of a bolus of an indicator with high conductivity. Hypertonic sodium chloride has been the standard indicator. However, hypertonic sodium chloride is often inaccessible in the hospital, limiting practical adoption. We investigated whether regional lung perfusion measured using electrical impedance tomography is comparable between indicators. Using a swine lung injury model, we determined regional lung perfusion (% of total perfusion) in five pigs, comparing 12% sodium chloride to 8.4% sodium bicarbonate across stages of lung injury and experimental conditions (body position, positive end-expiratory pressure). Regional lung perfusion for four lung regions was determined from maximum slope analysis of the indicator-based impedance signal. Estimates of regional lung perfusion between indicators were compared in the lung overall and within four lung regions. Regional lung perfusion estimated with a sodium bicarbonate indicator agreed with a hypertonic sodium chloride indicator overall (mean bias 0%, limits of agreement -8.43%, 8.43%) and within lung quadrants. The difference in regional lung perfusion between indicators did not change across experimental conditions. Sodium bicarbonate may be a comparable indicator to estimate regional lung perfusion using electrical impedance tomography.NEW & NOTEWORTHY Electrical impedance tomography is an emerging tool to measure regional lung perfusion using kinetic analysis of a conductive indicator. Hypertonic sodium chloride is the standard agent used. We measured regional lung perfusion using another indicator, comparing hypertonic sodium chloride to sodium bicarbonate in an experimental swine lung injury model. We found strong agreement between the two indicators. Sodium bicarbonate may be a comparable indicator to measure regional lung perfusion with electrical impedance tomography.

Evaluation of Different Contrast Agents for Regional Lung Perfusion Measurement Using Electrical Impedance Tomography: An Experimental Pilot Study

Monitoring regional blood flow distribution in the lungs appears to be useful for individually optimizing ventilation therapy. Electrical impedance tomography (EIT) can be used at the bedside for indicator-based regional lung perfusion measurement. Hypertonic saline is widely used as a contrast agent but could be problematic for clinical use due to potential side effects. In five ventilated healthy pigs, we investigated the suitability of five different injectable and clinically approved solutions as contrast agents for EIT-based lung perfusion measurement. Signal extraction success rate, signal strength, and image quality were analyzed after repeated 10 mL bolus injections during temporary apnea. The best results were obtained using NaCl 5.85% and sodium-bicarbonate 8.4% with optimal success rates (100%, each), the highest signal strengths (100 ± 25% and 64 ± 17%), and image qualities (r = 0.98 ± 0.02 and 0.95 ± 0.07). Iomeprol 400 mg/mL (non-ionic iodinated X-ray contrast medium) and Glucose 5% (non-ionic glucose solution) resulted in mostly well usable signals with above average success rates (87% and 89%), acceptable signal strength (32 ± 8% and 16 + 3%), and sufficient image qualities (r = 0.80 ± 0.19 and 0.72 ± 0.21). Isotonic balanced crystalloid solution failed due to a poor success rate (42%), low signal strength (10 ± 4%), and image quality (r = 0.43 ± 0.28). While Iomeprol might enable simultaneous EIT and X-ray measurements, glucose might help to avoid sodium and chloride overload. Further research should address optimal doses to balance reliability and potential side effects.

Pathophysiology and Clinical Meaning of Ventilation-Perfusion Mismatch in the Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) remains an important clinical challenge with a mortality rate of 35-45%. It is being increasingly demonstrated that the improvement of outcomes requires a tailored, individualized approach to therapy, guided by a detailed understanding of each patient's pathophysiology. In patients with ARDS, disturbances in the physiological matching of alveolar ventilation (V) and pulmonary perfusion (Q) (V/Q mismatch) are a hallmark derangement. The perfusion of collapsed or consolidated lung units gives rise to intrapulmonary shunting and arterial hypoxemia, whereas the ventilation of non-perfused lung zones increases physiological dead-space, which potentially necessitates increased ventilation to avoid hypercapnia. Beyond its impact on gas exchange, V/Q mismatch is a predictor of adverse outcomes in patients with ARDS; more recently, its role in ventilation-induced lung injury and worsening lung edema has been described. Innovations in bedside imaging technologies such as electrical impedance tomography readily allow clinicians to determine the regional distributions of V and Q, as well as the adequacy of their matching, providing new insights into the phenotyping, prognostication, and clinical management of patients with ARDS. The purpose of this review is to discuss the pathophysiology, identification, consequences, and treatment of V/Q mismatch in the setting of ARDS, employing experimental data from clinical and preclinical studies as support.

Deep feature-domain matching for cardiac-related component separation from a chest electrical impedance tomography image series: proof-of-concept study

Objectives.The cardiac-related component in chest electrical impedance tomography (EIT) measurement is of potential value to pulmonary perfusion monitoring and cardiac function measurement. In a spontaneous breathing case, cardiac-related signals experience serious interference from ventilation-related signals. Traditional cardiac-related signal-separation methods are usually based on certain features of signals. To further improve the separation accuracy, more comprehensive features of the signals should be exploited.Approach.We propose an unsupervised deep-learning method called deep feature-domain matching (DFDM), which exploits the feature-domain similarity of the desired signals and the breath-holding signals. This method is characterized by two sub-steps. In the first step, a novel Siamese network is designed and trained to learn common features of breath-holding signals; in the second step, the Siamese network is used as a feature-matching constraint between the separated signals and the breath-holding signals.Main results.The method is first tested using synthetic data, and the results show satisfactory separation accuracy. The method is then tested using the data of three patients with pulmonary embolism, and the consistency between the separated images and the radionuclide perfusion scanning images is checked qualitatively.Significance.The method uses a lightweight convolutional neural network for fast network training and inference. It is a potential method for dynamic cardiac-related signal separation in clinical settings.

Suppressing interferences of EIT on synchronous recording EEG based on comb filter for seizure detection

Background and objective

The purpose of this study was to eliminate the interferences of electrical impedance tomography (EIT) on synchronous recording electroencephalography (EEG) for seizure detection.

Methods

The simulated EIT signal generated by COMSOL Multiphysics was superimposed on the clinical EEG signal obtained from the CHB-MIT Scalp EEG Database, and then the spectrum features of superimposed mixed signals were analyzed. According to the spectrum analysis, in addition to high-frequency interference at 51.2 kHz related to the drive current, there was also low-frequency interference caused by switching of electrode pairs, which were used to inject drive current. A low pass filter and a comb filter were used to suppress the high-frequency interference and low-frequency interference, respectively. Simulation results suggested the low-pass filter and comb filter working together effectively filtered out the interference of EIT on EEG in the process of synchronous monitoring.

Results

As a result, the normal EEG and epileptic EEG could be recognized effectively. Pearson correlation analysis further confirmed the interference of EIT on EEG was effectively suppressed.

Conclusions

This study provides a simple and effective interference suppression method for the synchronous monitoring of EIT and EEG, which could be served as a reference for the synchronous monitoring of EEG and other medical electromagnetic devices.

Hybrid method for improving Tikhonov-based reconstruction quality in electrical impedance tomography

Purpose

Electrical impedance tomography (EIT) has shown its potential in the field of medical imaging. Physiological or pathological variation would cause the change of conductivity. EIT is favorable in reconstructing conductivity distribution inside the detected area. However, due to its ill-posed and nonlinear characteristics, reconstructed images suffer from low spatial resolution.

Approach

Tikhonov regularization method is a popular and effective approach for image reconstruction in EIT. Nevertheless, excessive smoothness is observed when reconstruction is conducted based on Tikhonov method. To improve Tikhonov-based reconstruction quality in EIT, an innovative hybrid iterative optimization method is proposed. An efficient alternating minimization algorithm is introduced to solve the optimization problem.

Results

To verify image reconstruction performance and anti-noise robustness of the proposed method, a series of simulation work and phantom experiments is carried out. Meanwhile, comparison is made with reconstruction results based on Landweber, Newton-Raphson, and Tikhonov methods. The reconstruction performance is also verified by quantitative comparison of blur radius and structural similarity values which further demonstrates the excellent performance of the proposed method.

Conclusions

In contrast to Landweber, Newton-Raphson, and Tikhonov methods, it is found that images reconstructed by the proposed method are more accurate. Even under the impact of noise, the proposed method outperforms comparison methods.

Adaptively Regularized Bases-Expansion Subspace Optimization Methods for Electrical Impedance Tomography

OBJECTIVE

In this work, to deal with the difficulties in choosing regularization weighting parameters and low spatial resolution problems in difference electrical impedance tomography (EIT), we propose two adaptively regularized bases-expansion subspace optimization methods (AR-BE-SOMs).

METHODS

Firstly, an adaptive L1-norm based total variation (TV) regularization is introduced under the framework of BE-SOM. Secondly, besides the additive regularization, an adaptive weighted L2-norm multiplicative regularization is further proposed. The regularized objective functions are optimized by conjugate gradient method, where the unknowns in both methods are update alternatively between induced contrast current (ICC) and conductivity domain.

CONCLUSION

Both numerical and experimental tests are conducted to validate the proposed methods, where AR-BE-SOMs show better edge-preserving, anti-noise performance, lower relative errors, and higher structure similarity indexes than BE-SOM.

SIGNIFICANCE

Unlike the common regularization techniques in EIT, the proposed regularization factors can be obtained adaptively during the optimization process. More importantly, ARBE-SOMs perform well in reconstructions of some challenging geometry with sharp corners such as the heart and lung phantoms, deformation phantoms, triangles and even rectangles. It is expected that the proposed AR-BE-SOMs will find their applications for high-quality lung health monitoring and other clinical applications.

An efficient and fast multi-band focused bioimpedance solution with EIT-based reconstruction for pulmonary embolism assessment: a simulation study from massive to segmental blockage

OBJECTIVE

Pulmonary embolism (PE) is an acute condition that blocks the perfusion to the lungs and is a common complication of Covid-19. However, PE is often not diagnosed in time, especially in the pandemic time due to complicated diagnosis protocol. In this study, a non-invasive, fast and efficient bioimpedance method with the EIT-based reconstruction approach is proposed to assess the lung perfusion reliably.

APPROACH

Some proposals are presented to improve the sensitivity and accuracy for the bioimpedance method: (1) a new electrode configuration and focused pattern to help study deep changes caused by PE within each lung field separately, (2) a measurement strategy to compensate the effect of different boundary shapes and varied respiratory conditions on the perfusion signals and (3) an estimator to predict the lung perfusion capacity, from which the severity of PE can be assessed. The proposals were tested on the first-time simulation of PE events at different locations and degrees from segmental blockages to massive blockages. Different object boundary shapes and varied respiratory conditions were included in the simulation to represent for different populations in real measurements.

RESULTS

The correlation between the estimator and the perfusion was very promising (R = 0.91, errors < 6%). The measurement strategy with the proposed configuration and pattern has helped stabilize the estimator to non-perfusion factors such as the boundary shapes and varied respiration conditions (3-5% errors).

SIGNIFICANCE

This promising preliminary result has demonstrated the proposed bioimpedance method's capability and feasibility, and might start a new direction for this application.

Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia

The Kingdom of Saudi Arabia has suffered from COVID-19 disease as part of the global pandemic due to severe acute respiratory syndrome coronavirus 2. The economy of Saudi Arabia also suffered a heavy impact. Several measures were taken to help mitigate its impact and stimulate the economy. In this context, we present a safe and secure WiFi-sensing-based COVID-19 monitoring system exploiting commercially available low-cost wireless devices that can be deployed in different indoor settings within Saudi Arabia. We extracted different activities of daily living and respiratory rates from ubiquitous WiFi signals in terms of channel state information (CSI) and secured them from unauthorized access through permutation and diffusion with multiple substitution boxes using chaos theory. The experiments were performed on healthy participants. We used the variances of the amplitude information of the CSI data and evaluated their security using several security parameters such as the correlation coefficient, mean-squared error (MSE), peak-signal-to-noise ratio (PSNR), entropy, number of pixel change rate (NPCR), and unified average change intensity (UACI). These security metrics, for example, lower correlation and higher entropy, indicate stronger security of the proposed encryption method. Moreover, the NPCR and UACI values were higher than 99% and 30, respectively, which also confirmed the security strength of the encrypted information.

Lung Perfusion Assessment by Bedside Electrical Impedance Tomography in Critically Ill Patients

As a portable, radiation-free imaging modality, electrical impedance tomography (EIT) technology has shown promise in the bedside visual assessment of lung perfusion distribution in critically ill patients. The two main methods of EIT for assessing lung perfusion are the pulsatility and conductivity contrast (saline) bolus method. Increasing attention is being paid to the saline bolus EIT method in the evaluation of regional pulmonary perfusion in clinical practice. This study seeks to provide an overview of experimental and clinical studies with the aim of clarifying the progress made in the use of the saline bolus EIT method. Animal studies revealed that the saline bolus EIT method presented good consistency with single-photon emission CT (SPECT) in the evaluation of lung regional perfusion changes in various pathological conditions. Moreover, the saline bolus EIT method has been applied to assess the lung perfusion in a pulmonary embolism and the effect of positive end-expiratory pressure (PEEP) on regional ventilation/perfusion ratio (V/Q) and acute respiratory distress syndrome (ARDS) in several clinical studies. The implementation of saline boluses, data analyses, precision, and cutoff values varied among different studies, and a consensus must be reached regarding the clinical application of the saline bolus EIT method. Further study is required to validate the impact of the described saline bolus EIT method on decision-making, therapeutic management, and outcomes in critically ill patients.

A model-based source separation algorithm for lung perfusion imaging using electrical impedance tomography

Objective. Electrical impedance tomography (EIT) for lung perfusion imaging is attracting considerable interest in intensive care, as it might open up entirely new ways to adjust ventilation therapy. A promising technique is bolus injection of a conductive indicator to the central venous catheter, which yields the indicator-based signal (IBS). Lung perfusion images are then typically obtained from the IBS using the maximum slope technique. However, the low spatial resolution of EIT results in a partial volume effect (PVE), which requires further processing to avoid regional bias.Approach. In this work, we repose the extraction of lung perfusion images from the IBS as a source separation problem to account for the PVE. We then propose a model-based algorithm, called gamma decomposition (GD), to derive an efficient solution. The GD algorithm uses a signal model to transform the IBS into a parameter space where the source signals of heart and lung are separable by clustering in space and time. Subsequently, it reconstructs lung model signals from which lung perfusion images are unambiguously extracted.Main results. We evaluate the GD algorithm on EIT data of a prospective animal trial with eight pigs. The results show that it enables lung perfusion imaging using EIT at different stages of regional impairment. Furthermore, parameters of the source signals seem to represent physiological properties of the cardio-pulmonary system.Significance. This work represents an important advance in IBS processing that will likely reduce bias of EIT perfusion images and thus eventually enable imaging of regional ventilation/perfusion (V/Q) ratio.

Unmatched ventilation and perfusion measured by electrical impedance tomography predicts the outcome of ARDS

Background

In acute respiratory distress syndrome (ARDS), non-ventilated perfused regions coexist with non-perfused ventilated regions within lungs. The number of unmatched regions might reflect ARDS severity and affect the risk of ventilation-induced lung injury. Despite pathophysiological relevance, unmatched ventilation and perfusion are not routinely assessed at the bedside. The aims of this study were to quantify unmatched ventilation and perfusion at the bedside by electrical impedance tomography (EIT) investigating their association with mortality in patients with ARDS and to explore the effects of positive end-expiratory pressure (PEEP) on unmatched ventilation and perfusion in subgroups of patients with different ARDS severity based on PaO₂/FiO₂ and compliance.

Methods

Prospective observational study in 50 patients with mild (36%), moderate (46%), and severe (18%) ARDS under clinical ventilation settings. EIT was applied to measure the regional distribution of ventilation and perfusion using central venous bolus of saline 5% during end-inspiratory pause. We defined unmatched units as the percentage of only ventilated units plus the percentage of only perfused units.

Results

Percentage of unmatched units was significantly higher in non-survivors compared to survivors (32[27–47]% vs. 21[17–27]%, p < 0.001). Percentage of unmatched units was an independent predictor of mortality (OR 1.22, 95% CI 1.07–1.39, p = 0.004) with an area under the ROC curve of 0.88 (95% CI 0.79–0.97, p < 0.001). The percentage of ventilation to the ventral region of the lung was higher than the percentage of ventilation to the dorsal region (32 [27–38]% vs. 18 [13–21]%, p < 0.001), while the opposite was true for perfusion (28 [22–38]% vs. 36 [32–44]%, p < 0.001).

Higher percentage of only perfused units was correlated with lower dorsal ventilation (r =  − 0.486, p < 0.001) and with lower PaO₂/FiO₂ ratio (r =  − 0.293, p = 0.039).

Conclusions

EIT allows bedside assessment of unmatched ventilation and perfusion in mechanically ventilated patients with ARDS. Measurement of unmatched units could identify patients at higher risk of death and could guide personalized treatment.