Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data’s variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70–0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.

Oscillometry of the Respiratory System. A Translational Opportunity Not to be Missed

Airway oscillometry has become the de facto standard for quality assessment of lung physiology in laboratory animals and has demonstrated its usefulness in understanding diseases of small airways. Nowadays, it is seeing extensive use in daily clinical practice and research, however, a question that remains unanswered is how well physiological findings in animals and humans correlate? Methodological and device differences are obvious between animal and human studies. However, all devices deliver an oscillated airflow test signal and output respiratory impedance. In addition, despite analysis differences, there are ways to interpret animal and human oscillometry data to allow suitable comparisons. The potential with oscillometry is its ability to reveal universal features of the respiratory system across species, making translational extrapolation likely to be predictive. This means that oscillometry can thus help determine if an animal model displays the same physiological characteristics as the human disease. Perhaps more importantly, it can also be useful to determine whether an intervention is effective as well as to understand if it affects the desired region of the respiratory system e.g., the periphery of the lung. Finally, findings in humans can also inform preclinical scientists and give indications as to what type of physiological changes should be observed in animal models to make them relevant as models of human disease. The present article will attempt to demonstrate the potential of oscillometry in respiratory research, an area where the development of novel therapies is plagued with a failure rate higher than in other disease areas.

Shotgun Proteomics of Isolated Urinary Extracellular Vesicles for Investigating Respiratory Impedance in Healthy Preschoolers

Urine proteomic applications in children suggested their potential in discriminating between healthy subjects from those with respiratory diseases. The aim of the current study was to combine protein fractionation, by urinary extracellular vesicle isolation, and proteomics analysis in order to establish whether different patterns of respiratory impedance in healthy preschoolers can be characterized from a protein fingerprint. Twenty-one 3–5-yr-old healthy children, representative of 66 recruited subjects, were selected: 12 late preterm (LP) and 9 full-term (T) born. Children underwent measurement of respiratory impedance through Forced Oscillation Technique (FOT) and no significant differences between LP and T were found. Unbiased clustering, based on proteomic signatures, stratified three groups of children (A, B, C) with significantly different patterns of respiratory impedance, which was slightly worse in group A than in groups B and C. Six proteins (Tripeptidyl peptidase I (TPP1), Cubilin (CUBN), SerpinA4, SerpinF1, Thy-1 membrane glycoprotein (THY1) and Angiopoietin-related protein 2 (ANGPTL2)) were identified in order to type the membership of subjects to the three groups. The differential levels of the six proteins in groups A, B and C suggest that proteomic-based profiles of urinary fractionated exosomes could represent a link between respiratory impedance and underlying biological profiles in healthy preschool children.

Respiratory Impedance Measured using Impulse Oscillometry in a Healthy Urban Population

This study derives normative prediction equations for respiratory impedance in a healthy asymptomatic urban population using an impulse oscillation system (IOS). In addition, this study uses body mass index (BMI) in the equations to describe the effect of obesity on respiratory impedance.Data from an urban population comprising 472 healthy asymptomatic subjects that resided or worked in lower Manhattan, New York City were retrospectively analysed. This population was the control group from a previously completed case-control study of the health effects of exposure to World Trade Center dust. Since all subjects underwent spirometry and oscillometry, these previously collected data allowed a unique opportunity to derive normative prediction equations for oscillometry in an urban, lifetime non-smoking, asymptomatic population without underlying respiratory disease.Normative prediction equations for men and women were successfully developed for a broad range of respiratory oscillometry variables with narrow confidence bands. Models that used BMI as an independent predictor of oscillometry variables (in addition to age and height) demonstrated equivalent or better fit when compared with models that used weight. With increasing BMI, resistance and reactance increased compatible with lung and airway compression from mass loading.This study represents the largest cohort of healthy urban subjects assessed with an IOS device. Normative prediction equations were derived that should facilitate application of IOS in the clinical setting. In addition, the data suggest that modelling of lung function may be best performed using height and BMI as independent variables rather than the traditional approach of using height and weight.

Reference values of respiratory impedance with impulse oscillometry in healthy Chinese adults

Background: Impulse oscillometry (IOS) is a non-invasive pulmonary function test for measuring respiratory impedance. Available reference equations of IOS indices for adults are limited. The aim of this study was to develop reference equations of IOS indices for Chinese adults.Methods: In a multicentral, cross-sessional study of impulse oscillometry in Chinese adults, IOS data from healthy subjects were collected from 19 general hospitals across China was between 2016 and 2018. IOS measurements were conducted in accordance with recommendations of the European Respiratory Society. Multiple linear regression was performed to develop sex-specific reference equations of IOS indices.Results: IOS measurements were performed in 1318 subjects, of which 567 subjects were defined as healthy normal individuals with acceptable IOS data and were included in the final analysis. Reference equations and limits of normal (LLN/ULN) of IOS indices were developed separately for males and females. Height but not age was shown to be the most influential contributor to IOS indices. The reference equations currently used in lung function laboratories predicted higher R5 and X5. Normal ranges of R5 and X5 recommended by the equipment manufacturer were clearly different from the ULN/LLN derived from the reference equations.Conclusions: Reference equations of IOS indices for Chinese adults from a wide region were provided in this study. It is necessary to update new IOS reference equations and adopt ULN/LLN as normal ranges of IOS indices.Clinical Trial Registration: This study was registered at www.clinicaltrials.gov as part of a larger study NCT03467880.