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Background: Acute respiratory distress syndrome (ARDS) poses challenges in early identification. Exhaled breath contains metabolites reflective of pulmonary inflammation. Aim: To evaluate the diagnostic accuracy of breath metabolites for ARDS in invasively ventilated intensive care unit (ICU) patients. Methods: This two-center observational study included critically ill patients receiving invasive ventilation. Gas chromatography and mass spectrometry (GC–MS) was used to quantify the exhaled metabolites. The Berlin definition of ARDS was assessed by three experts to categorize all patients into "certain ARDS", "certain no ARDS" and "uncertain ARDS" groups. The patients with "certain" labels from one hospital formed the derivation cohort used to train a classifier built based on the five most significant breath metabolites. The diagnostic accuracy of the classifier was assessed in all patients from the second hospital and combined with the lung injury prediction score (LIPS). Results: A total of 499 patients were included in this study. Three hundred fifty-seven patients were included in the derivation cohort (60 with certain ARDS; 17%), and 142 patients in the validation cohort (47 with certain ARDS; 33%). The metabolites 1-methylpyrrole, 1,3,5-trifluorobenzene, methoxyacetic acid, 2-methylfuran and 2-methyl-1-propanol were included in the classifier. The classifier had an area under the receiver operating characteristics curve (AUROCC) of 0.71 (CI 0.63–0.78) in the derivation cohort and 0.63 (CI 0.52–0.74) in the validation cohort. Combining the breath test with the LIPS does not significantly enhance the diagnostic performance. Conclusion: An exhaled breath metabolomics-based classifier has moderate diagnostic accuracy for ARDS but was not sufficiently accurate for clinical use, even after combination with a clinical prediction score. [ABSTRACT FROM AUTHOR]

Background: Exhaled volatile organic compounds (VOCs), particularly hydrocarbons from oxidative stress-induced lipid peroxidation, are associated with hyperoxia exposure. However, important heterogeneity amongst identified VOCs and concerns about their precise pathophysiological origins warrant translational studies assessing their validity as a marker of hyperoxia-induced oxidative stress. Therefore, this study sought to examine changes in VOCs previously associated with the oxidative stress response in hyperoxia-exposed lung epithelial cells. Methods: A549 alveolar epithelial cells were exposed to hyperoxia for 24 h, or to room air as normoxia controls, or hydrogen peroxide as oxidative-stress positive controls. VOCs were sampled from the headspace, analysed by gas chromatography coupled with mass spectrometry and compared by targeted and untargeted analyses. A secondary analysis of breath samples from a large cohort of critically ill adult patients assessed the association of identified VOCs with clinical oxygen exposure. Results: Following cellular hyperoxia exposure, none of the targeted VOCs, previously proposed as breath markers of oxidative stress, were increased, and decane was significantly decreased. Untargeted analysis did not reveal novel identifiable hyperoxia-associated VOCs. Within the clinical cohort, three previously proposed breath markers of oxidative stress, hexane, octane, and decane had no real diagnostic value in discriminating patients exposed to hyperoxia. Conclusions: Hyperoxia exposure of alveolar epithelial cells did not result in an increase in identifiable VOCs, whilst VOCs previously linked to oxidative stress were not associated with oxygen exposure in a cohort of critically ill patients. These findings suggest that the pathophysiological origin of previously proposed breath markers of oxidative stress is more complex than just oxidative stress from hyperoxia at the lung epithelial cellular level. [ABSTRACT FROM AUTHOR]

Background Early and accurate recognition of respiratory pathogens is crucial to prevent increased risk of mortality in critically ill patients. Microbial-derived volatile organic compounds (mVOCs) in exhaled breath could be used as noninvasive biomarkers of infection to support clinical diagnosis. Methods In this study, we investigated the diagnostic potential of in vitro – confirmed mVOCs in the exhaled breath of patients under mechanical ventilation from the BreathDx study. Samples were analyzed by thermal desorption–gas chromatography–mass spectrometry. Results Pathogens from bronchoalveolar lavage (BAL) cultures were identified in 45 of 89 patients and Staphylococcus aureus was the most commonly identified pathogen (n = 15). Of 19 mVOCs detected in the in vitro culture headspace of 4 common respiratory pathogens (S. aureus , Pseudomonas aeruginosa , Klebsiella pneumoniae , and Escherichia coli), 14 were found in exhaled breath samples. Higher concentrations of 2 mVOCs were found in the exhaled breath of patients infected with S. aureus compared to those without (3-methylbutanal: P <.01, area under the receiver operating characteristic curve [AUROC] = 0.81–0.87; and 3-methylbutanoic acid: P =.01, AUROC = 0.79–0.80). In addition, bacteria identified from BAL cultures that are known to metabolize tryptophan (E. coli , Klebsiella oxytoca , and Haemophilus influenzae) were grouped and found to produce higher concentrations of indole compared to breath samples with culture-negative (P =.034) and other pathogen-positive (P =.049) samples. Conclusions This study demonstrates the capability of using mVOCs to detect the presence of specific pathogen groups with potential to support clinical diagnosis. Although not all mVOCs were found in patient samples within this small pilot study, further targeted and qualitative investigation is warranted using multicenter clinical studies. [ABSTRACT FROM AUTHOR]

This paper presents a first step towards reliable detection of nocturnal epileptic seizures based on 3-D accelerometry (ACM) recordings. The main goal is to distinguish between data with and without subtle nocturnal motor activity, thus reducing the amount of data that needs further (more complex) analysis for seizure detection. From 15 ACM signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space, a linear threshold function is used for classification. For training and testing, the algorithm ACM data along with video data is used from nocturnal registrations in seven mentally retarded patients with severe epilepsy. Per patient, the algorithm detected 100% of the periods of motor activity that are marked in video recordings and the ACM signals by experts. From all the detections, 43%-89% was correct (mean =65%). We were able to reduce the amount of data that need to be analyzed considerably. The results show that our approach can be used for detection of subtle nocturnal motor activity. Furthermore, our results indicate that our algorithm is robust for fluctuations across patients. Consequently, there is no need for training the algorithm for each new patient. Index Terms--Epilepsy seizure detection, motor activity, supervised learning, 3-D accelerometry (ACM).

One of the most widely available technologies to clean the air in homes of particulate matter of less than 2.5 µm in diameter (PM2.5), known to have negative health impacts, are portable home air purifiers (HAPs). This paper presents research which (1) explored the effectiveness of HAPs in real-world conditions in 57 homes in three European cities; (2) examined if HAPs affect users' perceptions of the indoor air quality (IAQ) at home; and (3) considered the motivations for occupants' operation of HAPs. Results from this study found that PM2.5 concentrations in bedrooms were reduced by 45% to 69%; perceptions of IAQ were not correlated with measured high PM2.5 levels; occupants reported the HAPs to have a 'cooling' effect, which may explain why the predominant driver of HAP use was thermal comfort, rather than IAQ, in all three cities. The latter finding was supported by a statistically significant increase in the probability of HAP use with increasing indoor temperatures. If the operation of HAPs can be managed, or fully automated, to reflect indoor air pollution levels rather than thermal conditions, better pollutant reduction would be feasible and their use to reduce PM2.5 may help mitigate the negative health effects of exposure whilst at home. [ABSTRACT FROM AUTHOR]

Patients suspected of ventilator-associated lower respiratory tract infections (VA-LRTIs) commonly receive broad-spectrum antimicrobial therapy unnecessarily. We tested whether exhaled breath analysis can discriminate between patients suspected of VA-LRTI with confirmed infection, from patients with negative cultures. Breath from 108 patients suspected of VA-LRTI was analysed by gas chromatography-mass spectrometry. The breath test had a sensitivity of 98% at a specificity of 49%, confirmed with a second analytical method. The breath test had a negative predictive value of 96% and excluded pneumonia in half of the patients with negative cultures. Trial registration number: UKCRN ID number 19086, registered May 2015. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of ventilator-associated pneumonia (VAP) remains time-consuming and costly, the clinical tools lack specificity and a bedside test to exclude infection in suspected patients is unavailable. Breath contains hundreds to thousands of volatile organic compounds (VOCs) that result from host and microbial metabolism as well as the environment. The present study aims to use breath VOC analysis to develop a model that can discriminate between patients who have positive cultures and who have negative cultures with a high sensitivity.Methods/design: The Molecular Analysis of Exhaled Breath as Diagnostic Test for Ventilator-Associated Pneumonia (BreathDx) study is a multicentre observational study. Breath and bronchial lavage samples will be collected from 100 and 53 intubated and ventilated patients suspected of VAP. Breath will be analysed using Thermal Desorption - Gas Chromatography - Mass Spectrometry (TD-GC-MS). The primary endpoint is the accuracy of cross-validated prediction for positive respiratory cultures in patients that are suspected of VAP, with a sensitivity of at least 99% (high negative predictive value).Discussion: To our knowledge, BreathDx is the first study powered to investigate whether molecular analysis of breath can be used to classify suspected VAP patients with and without positive microbiological cultures with 99% sensitivity.Trial Registration: UKCRN ID number 19086, registered May 2015; as well as registration at www.trialregister.nl under the acronym 'BreathDx' with trial ID number NTR 6114 (retrospectively registered on 28 October 2016). [ABSTRACT FROM AUTHOR]

Four time-frequency and time-scale methods are studied for their ability of detecting myoclonic seizures from accelerometric data. Methods that are used are: the short-time Fourier transform (STFT), the Wigner distribution (WD), the continuous wavelet transform (CWT) using a Daubechies wavelet, and a newly introduced model-based matched wavelet transform (MOD). Real patient data are analyzed using these four time-frequency and time-scale methods. To obtain quantitative results, all four methods are evaluated in a linear classification setup. Data from 15 patients are used for training and data from 21 patients for testing. Using features based on the CWT and MOD, the success rate of the classifier was 80%. Using STFT or WD-based features, the classification success is reduced. Analysis of the false positives revealed that they were either clonic seizures, the onset of tonic seizures, or sharp peaks in “normal” movements indicating that the patient was making a jerky movement. All these movements are considered clinically important to detect. Thus, the results show that both CWT and MOD are useful for the detection of myoclonic seizures. On top of that, MOD has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiologically meaningful. Furthermore, in future work, the model can also be useful for the detection of other motor seizure types. [ABSTRACT FROM PUBLISHER]

Introduction: Infections such as ventilator-associated pneumonia (VAP) can be caused by one or more pathogens. Current methods for identifying these pathogenic microbes often require invasive sampling, and can be time consuming, due to the requirement for prolonged cultural enrichment along with selective and differential plating steps. This results in delays in diagnosis which in such critically ill patients can have potentially life-threatening consequences. Therefore, a non-invasive and timely diagnostic method is required. Detection of microbial volatile organic compounds (VOCs) in exhaled breath is proposed as an alternative method for identifying these pathogens and may distinguish between mono- and poly-microbial infections.Objectives: To investigate volatile metabolites that discriminate between bacterial mono- and co-cultures.Methods: VAP-associated pathogens Enterobacter cloacae and Pseudomonas aeruginosa were cultured individually and together in artificial sputum medium for 24 h and their headspace was analysed for potential discriminatory VOCs by thermal desorption gas chromatography-mass spectrometry.Results: Of the 70 VOCs putatively identified, 23 were found to significantly increase during bacterial culture (i.e. likely to be released during metabolism) and 13 decreased (i.e. likely consumed during metabolism). The other VOCs showed no transformation (similar concentrations observed as in the medium). Bacteria-specific VOCs including 2-methyl-1-propanol, 2-phenylethanol, and 3-methyl-1-butanol were observed in the headspace of axenic cultures of E. cloacae, and methyl 2-ethylhexanoate in the headspace of P. aeruginosa cultures which is novel to this investigation. Previously reported VOCs 1-undecene and pyrrole were also detected. The metabolites 2-methylbutyl acetate and methyl 2-methylbutyrate, which are reported to exhibit antimicrobial activity, were elevated in co-culture only.Conclusion: The observed VOCs were able to differentiate axenic and co-cultures. Validation of these markers in exhaled breath specimens could prove useful for timely pathogen identification and infection type diagnosis. [ABSTRACT FROM AUTHOR]

Background: Acute respiratory distress syndrome (ARDS) is currently diagnosed by the Berlin Definition. Diagnosis is subjective and often late. Untargeted metabolomics analysis of exhaled breath with gas chromatography and mass spectrometry (GC-MS) showed that the breath concentration of octane has a high diagnostic accuracy for ARDS. To facilitate rapid bedside measurement of this biomarker, a point-of-care (POC) breath test was developed. A prototype already showed good reproducibility and repeatability for the detection of octane. In this study we aim to measure octane in exhaled breath of invasively ventilated intensive care unit (ICU) patients and validate the diagnostic accuracy of the breath test for the early diagnosis of ARDS., Methods: This is a multicentre observational cohort study in patients admitted to the ICU receiving invasive ventilation for at least 24 hours. At least 500 patients in two academic hospitals in The Netherlands will be included. ARDS patients will be compared to patients without ARDS. ARDS diagnosis will be based on the Berlin Definition. Two diagnostic assessments will be performed during the first 72 hours of invasive ventilation, including breath sampling, arterial blood gas analysis and lung ultrasound (LUS). In patients fulfilling the criteria for ARDS, three additional breath samples will be taken to assess resolution. The primary endpoint is the diagnostic accuracy for ARDS, defined by the area under the receiver operating characteristics curve (AUROCC) of octane concentration in exhaled breath. Secondary endpoints are the association between exhaled breath octane and ARDS adjusted for confounders, and the added diagnostic accuracy of the breath test on top of the Lung Injury Prediction Score (LIPS)., Discussion: This is the first study that validates a metabolic biomarker of ARDS in an adequate sample size. The major novelty is the use of a POC breath test that has been specifically developed for the purpose of diagnosing ARDS. Strengths are; assessment in the early phase, in patients at risk for ARDS, longitudinal sampling and an expert panel to reliably diagnose ARDS. This study will provide a decisive answer on the question if exhaled breath metabolomics can be used to diagnose ARDS., Trial Registration: The trial is registered at trialregister.nl (ID: NL8226) with the tag "DARTS"., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/atm-21-1384). ARMV, TMEN, and IG are employed by company Philips. RMS received grant for the research project, Lung fund, The Netherlands. LDJB received grants from Health Holland, Dutch Lung Foundation (longfonds), Innovative medicine initiative and Amsterdam UMC. The other authors have no conflicts of interest to declare., (2021 Annals of Translational Medicine. All rights reserved.)

Introduction: Asthma is a heterogeneous condition, characterised by chronic inflammation of the airways, typically managed with inhaled bronchodilators and corticosteroids. In the case of uncontrolled asthma, oral corticosteroids (OCSs) are often prescribed. Good adherence and inhalation technique are associated with improved outcomes; however, it is difficult to monitor appropriate drug intake and effectiveness in individual patients. Exhaled breath contains thousands of volatile organic compounds (VOCs) that reflect changes in the body's chemistry and may be useful for monitoring drug pharmacokinetics/pharmacodynamics. We aimed to investigate the association of exhaled VOCs in severe asthma patients from the U-BIOPRED cohort (by gas chromatography coupled with time-of-flight mass spectrometry) with urinary levels of salbutamol and OCSs (by liquid chromatography coupled with high-resolution mass spectrometry)., Methods: Samples were collected at baseline and after 12-18 months of follow-up. Statistical analysis was based on univariate and multivariate modelling, followed by area under the receiver operating characteristic curve (AUC) calculation. Results were verified through longitudinal replication and independent validation., Results: Data were available for 78 patients (baseline n=48, replication n=30 and validation n=30). Baseline AUC values were 82.1% (95% CI 70.4-93.9%) for salbutamol and 78.8% (95% CI 65.8-91.8%) for OCS. These outcomes could be adequately replicated and validated. Additional regression analysis between qualified exhaled VOCs and urinary concentrations of salbutamol and prednisone showed statistically significant correlations (p<0.01)., Conclusion: We have linked exhaled VOCs to urinary detection of salbutamol and OCSs. This merits further development of breathomics into a point-of-care tool for therapeutic drug monitoring., Competing Interests: Conflict of interest: P. Brinkman has nothing to disclose. Conflict of interest: W.M. Ahmed has nothing to disclose. Conflict of interest: C. Gómez has nothing to disclose. Conflict of interest: H.H. Knobel has a patent “Inline drying of breath sample” pending. Conflict of interest: H. Weda has nothing to disclose. Conflict of interest: T.J. Vink has nothing to disclose. Conflict of interest: T.M. Nijsen has nothing to disclose. Conflict of interest: C.E. Wheelock has nothing to disclose. Conflict of interest: S-E. Dahlén has participated in advisory boards for AstraZeneca, GSK, Merck, Novartis, Sanofi and Teva, outside the submitted work. Conflict of interest: P. Montuschi has nothing to disclose. Conflict of interest: R.G. Knowles is director of Knowles Consulting Ltd, outside the submitted work. Conflict of interest: S.J. Vijverberg has nothing to disclose. Conflict of interest: A.H. Maitland-van der Zee reports grants from Boehringer Ingelheim, personal fees for advisory board work from AstraZeneca, and personal fees for travel to meetings from Chiesi, outside the submitted work. Conflict of interest: P.J. Sterk reports grants (paid to institution) from Innovative Medicines Initiative covered by the European Union and the European Federation of Pharmaceutical Industries and Associations, during the conduct of the study; and reports a 4% interest and is a scientific advisor for Breathomix BV, outside the submitted work. Conflict of interest: S.J. Fowler reports personal fees for lectures and nonfinancial support to attend conferences from AstraZeneca, grants and personal fees for lectures from Boehringer Ingelheim, personal fees for lectures from Novartis, Teva and Chiesi, outside the submitted work., (Copyright ©ERS 2020.)

Methods for breath sampling and analysis require robust quality assessment to minimise the risk of false discoveries. Planning large-scale multi-site breath metabolite profiling studies also requires careful consideration of systematic and random variation as a result of sampling and analysis techniques. In this study we use breath sample data from the recent U-BIOPRED cohort to evaluate and discuss some important methodological considerations such as batch variation and correction, variation between sites, storage and transportation, as well as inter-instrument analytical differences. Based on this we provide a summary of recommended best practices for new large scale multi-site studies.

Ventilator-associated pneumonia (VAP) is a healthcare-acquired infection arising from the invasion of the lower respiratory tract by opportunistic pathogens in ventilated patients. The current method of diagnosis requires the culture of an airway sample such as bronchoalveolar lavage, which is invasive to obtain and may take up to seven days to identify a causal pathogen, or indeed rule out infection. While awaiting results, patients are administered empirical antibiotics; risks of this approach include lack of effect on the causal pathogen, contribution to the development of antibiotic resistance and downstream effects such as increased length of intensive care stay, cost, morbidity and mortality. Specific biomarkers which could identify causal pathogens in a timely manner are needed as they would allow judicious use of the most appropriate antimicrobial therapy. Volatile organic compound (VOC) analysis in exhaled breath is proposed as an alternative due to its non-invasive nature and its potential to provide rapid diagnosis at the patient's bedside. VOCs in exhaled breath originate from exogenous, endogenous, as well as microbial sources. To identify potential markers, VAP-associated pathogens Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus were cultured in both artificial sputum medium and nutrient broth, and their headspaces were sampled and analysed for VOCs. Previously reported volatile markers were identified in this study, including indole and 1-undecene, alongside compounds that are novel to this investigation, cyclopentanone and 1-hexanol. We further investigated media components (substrates) to identify those that are essential for indole and cyclopentanone production, with potential implications for understanding microbial metabolism in the lung.

Alkanes and alkenes in the breath are produced through fatty acid peroxidation, which is initialized by reactive oxygen species. Inflammation is an important cause and effect of reactive oxygen species. We aimed to evaluate the association between fatty acid peroxidation products and inflammation of the alveolar and systemic compartment in ventilated intensive care unit (ICU) patients.Volatile organic compounds were measured by gas chromatography and mass spectrometry in the breath of newly ventilated ICU patients within 24 h after ICU admission. Cytokines were measured in non-directed bronchial lavage fluid (NBL) and plasma by cytometric bead array. Correlation coefficients were calculated and presented in heatmaps.93 patients were included. Peroxidation products in exhaled breath were not associated with markers of inflammation in plasma, but were correlated with those in NBL. IL-6, IL-8, IL-1β and TNF-α concentration in NBL showed inverse correlation coefficients with the peroxidation products of fatty acids. Furthermore, NBL IL-10, IL-13, GM-CSF and IFNγ demonstrated positive associations with breath alkanes and alkenes. Correlation coefficients for NBL cytokines were high regarding peroxidation products of n-6, n-7 and particularly in n-9 fatty acids.Levels of lipid peroxidation products in the breath of ventilated ICU patients are associated with levels of inflammatory markers in NBL, but not in plasma. Alkanes and alkenes in breath seems to be associated with an anti-inflammatory, rather than a pro-inflammatory state in the alveoli.

There is a need for biological markers of the acute respiratory distress syndrome (ARDS). Exhaled breath contains hundreds of metabolites in the gas phase, some of which reflect (patho)physiological processes. We aimed to determine the diagnostic accuracy of metabolites in exhaled breath as biomarkers of ARDS. Breath from ventilated intensive care unit patients (n=101) was analysed using gas chromatography and mass spectrometry during the first day of admission. ARDS was defined by the Berlin definition. Training and temporal validation cohorts were used. 23 patients in the training cohort (n=53) had ARDS. Three breath metabolites, octane, acetaldehyde and 3-methylheptane, could discriminate between ARDS and controls with an area under the receiver operating characteristic curve (AUC) of 0.80. Temporal external validation (19 ARDS cases in a cohort of 48) resulted in an AUC of 0.78. Discrimination was insensitive to adjustment for severity of disease, a direct or indirect cause of ARDS, comorbidities, or ventilator settings. Combination with the lung injury prediction score increased the AUC to 0.91 and improved net reclassification by 1.17. Exhaled breath analysis showed good diagnostic accuracy for ARDS, which was externally validated. These data suggest that exhaled breath analysis could be used for the diagnostic assessment of ARDS., (© ERS 2014.)

A model is formulated for arm movements during myoclonic (epileptic) seizures. The system described in the model, consists of a mechanical and an electrophysiological part. The model output is compared to real patient accelerometry (ACM)-data from six epilepsy patients. Eight out of ten myoclonic seizures have a good fit to the model. The values of the model parameters tuned to the real seizures are physiologically feasible. Using mean parameter values leads to agreeable fits in six out of ten myoclonic seizures. Two of the four parameters seem to be robust for variation in patient and seizure. The presented model approach leads to a better understanding of patterns in ACM-recordings that are associated with myoclonic seizures and in the future can contribute to automated detection of these patterns.