Evaluation of Regional Pulmonary Ventilation in Spontaneously Breathing Patients with Idiopathic Pulmonary Fibrosis (IPF) Employing Electrical Impedance Tomography (EIT): A Pilot Study from the European IPF Registry (eurIPFreg)

Electrical impedance tomography as a bedside assessment tool for COPD treatment during hospitalization

For patients with chronic obstructive pulmonary disease (COPD), the assessment of the treatment efficacy during hospitalization is of importance to the optimization of clinical treatments. Conventional spirometry might not be sensitive enough to capture the regional lung function development. The study aimed to evaluate the feasibility of using electrical impedance tomography (EIT) as an objective bedside evaluation tool for the treatment of acute exacerbation of COPD (AECOPD). Consecutive patients who required hospitalization due to AECOPD were included prospectively. EIT measurements were conducted at the time of admission and before the discharge simultaneously when a forced vital capacity maneuver was conducted. EIT-based heterogeneity measures of regional lung function were calculated based on the impedance changes over time. Surveys for attending doctors and patients were designed to evaluate the ease of use, feasibility, and overall satisfaction level to understand the acceptability of EIT measurements. Patient-reported outcome assessments were conducted. User's acceptance of EIT technology was investigated with a five-dimension survey. A total of 32 patients were included, and 8 patients were excluded due to the FVC maneuver not meeting the ATS criteria. Spirometry-based lung function was improved during hospitalization but not significantly different (FEV1 %pred.: 35.8% ± 6.7% vs. 45.3% ± 8.8% at admission vs. discharge; p = 0.11. FVC %pred.: 67.8% ± 0.4% vs. 82.6% ± 5.0%; p = 0.15. FEV1/FVC: 0.41 ± 0.09 vs. 0.42 ± 0.07, p = 0.71). The symptoms of COPD were significantly improved, but the correlations between the improvement of symptoms and spirometry FEV1 and FEV1/FVC were low (R = 0.1 and -0.01, respectively). The differences in blood gasses and blood tests were insignificant. All but one EIT-based regional lung function parameter were significantly improved after hospitalization. The results highly correlated with the patient-reported outcome assessment (R > 0.6, p < 0.001). The overall acceptability score of EIT measurement for both attending physicians and patients was high (4.1 ± 0.8 for physicians, 4.5 ± 0.5 for patients out of 5). These results demonstrated that it was feasible and acceptable to use EIT as an objective bedside evaluation tool for COPD treatment efficacy.

Systemic Determinants of Exercise Intolerance in Patients With Fibrotic Interstitial Lung Disease and Severely Impaired DLCO

BACKGROUND

The precise mechanisms driving poor exercise tolerance in patients with fibrotic interstitial lung diseases (fibrotic ILDs) showing a severe impairment in single-breath lung diffusing capacity for carbon monoxide (DLCO < 40% predicted) are not fully understood. Rather than only reflecting impaired O2 transfer, a severely impaired DLCO may signal deranged integrative physiologic adjustments to exercise that jointly increase the burden of exertional symptoms in fibrotic ILD.

METHODS

Sixty-seven subjects (46 with idiopathic pulmonary fibrosis, 24 showing DLCO < 40%) and 22 controls underwent pulmonary function tests and an incremental cardiopulmonary exercise test with serial measurements of operating lung volumes and 0-10 Borg dyspnea and leg discomfort scores.

RESULTS

Subjects from the DLCO < 40% group showed lower spirometric values, more severe restriction, and lower alveolar volume and transfer coefficient compared to controls and participants with less impaired DLCO (P < .05). Peak work rate was ∼45% (vs controls) and ∼20% (vs DLCO > 40%) lower in the former group, being associated with lower (and flatter) O2 pulse, an earlier lactate (anaerobic) threshold, heightened submaximal ventilation, and lower SpO2 . Moreover, critically high inspiratory constrains were reached at lower exercise intensities in the DLCO < 40% group (P < .05). In association with the greatest leg discomfort scores, they reported the highest dyspnea scores at a given work rate. Between-group differences lessened or disappeared when dyspnea intensity was related to indexes of increased demand-capacity imbalance, that is, decreasing submaximal, dynamic ventilatory reserve, and inspiratory reserve volume/total lung capacity (P > .05).

CONCLUSIONS

A severely reduced DLCO in fibrotic ILD signals multiple interconnected derangements (cardiovascular impairment, an early shift to anaerobic metabolism, excess ventilation, inspiratory constraints, and hypoxemia) that ultimately lead to limiting respiratory (dyspnea) and peripheral (leg discomfort) symptoms. DLCO < 40%, therefore, might help in clinical decision-making to indicate the patient with fibrotic ILD who might derive particular benefit from pharmacologic and non-pharmacologic interventions aimed at lessening these systemic abnormalities.

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.

Classification of Electrical Impedance Tomography Data Using Machine Learning

Patients suffering from pulmonary diseases typically exhibit pathological lung ventilation in terms of homogeneity. Electrical Impedance Tomography (EIT) is a non- invasive imaging method that allows to analyze and quantify the distribution of ventilation in the lungs. In this article, we present a new approach to promote the use of EIT data and the implementation of new clinical applications for differential diagnosis, with the development of several machine learning models to discriminate between EIT data from healthy and nonhealthy subjects. EIT data from 16 subjects were acquired: 5 healthy and 11 non-healthy subjects (with multiple pulmonary conditions). Preliminary results have shown accuracy percentages of 66% in challenging evaluation scenarios. The results suggest that the pairing of EIT feature engineering methods with machine learning methods could be further explored and applied in the diagnostic and monitoring of patients suffering from lung diseases. Also, we introduce the use of a new feature in the context of EIT data analysis (Impedance Curve Correlation).

First Attempt at Using Electrical Impedance Tomography to Predict High Flow Nasal Cannula Therapy Outcomes at an Early Phase

Objective: Spatial and temporal ventilation distributions in patients with acute respiratory failure during high flow nasal cannula (HFNC) therapy were previously studied with electrical impedance tomography (EIT). The aim of the study was to explore the possibility of predicting HFNC failure based on various EIT-derived parameters.

Methods: High flow nasal cannula failure was defined reintubation within 48 h after HFNC. EIT was performed with the patients spontaneously breathing in the supine position at the start of HFNC. EIT-based indices (comprising the global inhomogeneity index, center of ventilation, ventilation delay, rapid shallow breathing index, minute volume, and inspiration to expiration time) were explored and evaluated at three time points (prior to HFNC, T1; 30 min after HFNC started, T2; and 1 h after, T3).

Results: A total of 46 subjects were included in the final analysis. Eleven subjects had failed HFNC. The time to failure was 27.8 ± 12.4 h. The ROX index (defined as SpO₂/FiO₂/respiratory rate) for HFNC success patients was 8.3 ± 2.7 and for HFNC failure patients, 6.2 ± 1.8 (p = 0.23). None of the investigated EIT-based parameters showed significant differences between subjects with HFNC failure and success. Further subgroup analysis indicated that a significant difference in ventilation inhomogeneity was found between ARDS and non-ARDS [0.54 (0.37) vs. 0.46 (0.28) as evaluated with GI, p < 0.01]. Ventilation homogeneity significantly improved in ARDS after 60-min HFNC treatment [0.59 (0.20) vs 0.57 (0.19), T1 vs. T3, p < 0.05].

Conclusion: Spatial and temporal ventilation distributions were slightly but insignificantly different between the HFNC success and failure groups. HFNC failure could not be predicted by changes in EIT temporal and spatial indexes of ventilation distribution within the first hour. Further studies are required to predict the outcomes of HFNC.