Microbial Volatiles as Diagnostic Biomarkers of Bacterial Lung Infection in Mechanically Ventilated Patients.

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.

Development and validation of a point-of-care breath test for octane detection.

BACKGROUND: There is a demand for a non-invasive bedside method to diagnose Acute Respiratory Distress Syndrome (ARDS). Octane was discovered and validated as the most important breath biomarker for diagnosis of ARDS using gas-chromatography and mass-spectrometry (GC-MS). However, GC-MS is unsuitable as a point-of-care (POC) test in the intensive care unit (ICU). Therefore, we determined if a newly developed POC breath test can reliably detect octane in exhaled breath of invasively ventilated ICU patients. METHODS: Two developmental steps were taken to design a POC breath test that relies on gas-chromatography using air as carrier gas with a photoionization detector. Calibration measurements were performed with a laboratory prototype in healthy subjects. Subsequently, invasively ventilated patients were included for validation and assessment of repeatability. After evolving to a POC breath test, this device was validated in a second group of invasively ventilated patients. Octane concentration was based on the area under the curve, which was extracted from the chromatogram and compared to known values from calibration measurements. RESULTS: Five healthy subjects and 53 invasively ventilated patients were included. Calibration showed a linear relation (R2 = 1.0) between the octane concentration and the quantified octane peak in the low parts per billion (ppb) range. For the POC breath test the repeatability was excellent (R2 = 0.98, ICC = 0.97 (95% CI 0.94-0.99)). CONCLUSION: This is the first study to show that a POC breath test can rapidly and reliably detect octane, with excellent repeatability, at clinically relevant levels of low ppb in exhaled breath of ventilated ICU patients. This opens possibilities for targeted exhaled breath analysis to be used as a bedside test and makes it a potential diagnostic tool for the early detection of ARDS.

Volatile organic compound signature from co-culture of lung epithelial cell line with Pseudomonas aeruginosa.

Bacteria are found ubiquitously within and on nearly every site within humans, including the airways. Microbes interact with airway epithelial cells in lung infections such as ventilator-associated pneumonia (VAP). Development of infection results in the production of oxidants such as hydrogen peroxide that may further damage the epithelium. VAP is difficult to diagnose and associated with significant mortality. Current methods are invasive and time consuming impacting on appropriate therapy, antimicrobial resistance and financial costs. Volatile organic compound (VOC) analysis in exhaled breath is proposed as a tool for early detection due to its non-invasive property and potential to facilitate timely diagnosis. To investigate potential early VOC markers, A549 epithelial cells that were originally isolated from human alveoli were cultured with and without Pseudomonas aeruginosa, and the headspace of the culture vessel analysed using sorbent-based capture of VOCs followed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) in order to identify potential discriminatory VOCs. A549 cells were also cultured with hydrogen peroxide to induce oxidative stress in order to investigate potential biomarkers of epithelial cell damage. Previously reported VOCs including acetone and ethanol were observed from the infection experiment along with novel bacterial markers, which we identified as mostly ether based compounds. Alkanes such as decane and octane were also found to be elevated after hydrogen peroxide treatment of A549 cells, likely as a result of peroxidation of oleic acids.

Exhaled breath metabolomics as a noninvasive diagnostic tool for acute respiratory distress syndrome.

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.

Octane in exhaled breath to diagnose acute respiratory distress syndrome in invasively ventilated intensive care unit patients.

Background: The concentration of exhaled octane has been postulated as a reliable biomarker for acute respiratory distress syndrome (ARDS) using metabolomics analysis with gas chromatography and mass spectrometry (GC-MS). A point-of-care (POC) breath test was developed in recent years to accurately measure octane at the bedside. The aim of the present study was to validate the diagnostic accuracy of exhaled octane for ARDS using a POC breath test in invasively ventilated intensive care unit (ICU) patients. Methods: This was an observational cohort study of consecutive patients receiving invasive ventilation for at least 24 h, recruited in two university ICUs. GC-MS and POC breath tests were used to quantify the exhaled octane concentration. ARDS was assessed by three experts following the Berlin definition and used as the reference standard. The area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy. Results: 519 patients were included and 190 (37%) fulfilled the criteria for ARDS. The median (interquartile range) concentration of octane using the POC breath test was not significantly different between patients with ARDS (0.14 (0.05-0.37) ppb) and without ARDS (0.11 (0.06-0.26) ppb; p=0.64). The AUC for ARDS based on the octane concentration in exhaled breath using the POC breath test was 0.52 (95% CI 0.46-0.57). Analysis of exhaled octane with GC-MS showed similar results. Conclusions: Octane in exhaled breath has insufficient diagnostic accuracy for ARDS. This disqualifies the use of octane as a biomarker in the diagnosis of ARDS and challenges most of the research performed up to now in the field of exhaled breath metabolomics.

Diagnosis of acute respiratory distress syndrome (DARTS) by bedside exhaled breath octane measurements in invasively ventilated patients: protocol of a multicentre observational cohort study.

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".

Headspace volatile organic compounds from bacteria implicated in ventilator-associated pneumonia analysed by TD-GC/MS.

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.

TD/GC-MS analysis of volatile markers emitted from mono- and co-cultures of Enterobacter cloacae and Pseudomonas aeruginosa in artificial sputum.

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.

Detection of subtle nocturnal motor activity from 3-D accelerometry recordings in epilepsy patients.

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.

Exhaled breath analysis: a review of 'breath-taking' methods for off-line analysis.

BACKGROUND: The potential of exhaled breath sampling and analysis has long attracted interest in the areas of medical diagnosis and disease monitoring. This interest is attributed to its non-invasive nature, access to an unlimited sample supply (i.e., breath), and the potential to facilitate a rapid at patient diagnosis. However, progress from laboratory setting to routine clinical practice has been slow. Different methodologies of breath sampling, and the consequent difficulty in comparing and combining data, are considered to be a major contributor to this. To fulfil the potential of breath analysis within clinical and pre-clinical medicine, standardisation of some approaches to breath sampling and analysis will be beneficial. OBJECTIVES: The aim of this review is to investigate the heterogeneity of breath sampling methods by performing an in depth bibliometric search to identify the current state of art in the area. In addition, the review will discuss and critique various breath sampling methods for off-line breath analysis. METHODS: Literature search was carried out in databases MEDLINE, BIOSIS, EMBASE, INSPEC, COMPENDEX, PQSCITECH, and SCISEARCH using the STN platform which delivers peer-reviewed articles. Keywords searched for include breath, sampling, collection, pre-concentration, volatile. Forward and reverse search was then performed on initially included articles. The breath collection methodologies of all included articles was subsequently reviewed. RESULTS: Sampling methods differs between research groups, for example regarding the portion of breath being targeted. Definition of late expiratory breath varies between studies. CONCLUSIONS: Breath analysis is an interdisciplinary field of study using clinical, analytical chemistry, data processing, and metabolomics expertise. A move towards standardisation in breath sampling is currently being promoted within the breath research community with a view to harmonising analysis and thereby increasing robustness and inter-laboratory comparisons.

A model of heart rate changes to detect seizures in severe epilepsy.

AIM OF THE STUDY: An explorative study to assess the value of a model for the automatic detection and characterization of heart rate (HR) changes during seizures in severe epilepsy. METHODS: Heart rate changes were monitored in 10 patients with 104 seizures, mostly tonic and myoclonic, to assess the value of various modalities for the detection of seizures based on heart rate. EEG/video monitoring served as the golden standard. Two algorithms were developed. First, a curve-fitting algorithm was used to characterize the heart rate patterns. A second algorithm based on a moving median filter was developed for automatic detection of the heart rate change onset. For varying model parameters the sensitivity (SENS) and positive predictive values (PPV) were determined. RESULTS: Changes in heart rate were found in 8 of the 10 patients and 50 of 104 seizures. Patterns of heart rate changes could be quantitatively characterized and were found to be stereotype for each individual patient. Large differences of the curve-fitting pattern were in some cases due to a tachycardia at seizure onset that was followed by a significant postictal bradycardia. In two out of three patients with more than 10 seizures a PPV of at least 50% yielded a SENS above 90%. CONCLUSIONS: Heart rate patterns can be accurately characterized with a new developed curve-fitting algorithm. Heart rate changes can also be used for automatic detection of seizures in patients with severe epilepsy if the model parameters are chosen according to predefined characteristics of the patient.

Levels of cytokines in broncho-alveolar lavage fluid, but not in plasma, are associated with levels of markers of lipid peroxidation in breath of ventilated ICU patients.

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-1ß 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.

A simple breath sampling method in intubated and mechanically ventilated critically ill patients.

Volatile organic compounds (VOCs) in breath may serve as biomarkers of pulmonary infection or inflammation. We developed and validated a new breath sampling method for VOC analysis in ventilated patients. Breath was collected from the ventilatory circuit using cheap disposables. VOCs were identified by gas-chromatography and mass-spectrometry (GC-MS) at various minute volumes during ventilation of an artificial lung (in vitro) and ventilated patients (in vivo). Sixty-four VOCs emendated from the ventilator and tubing. Their concentrations had an inverse correlation with minute volume in in vitro experiments (median correlation coefficient: -0.61 [25-75th percentile: -0.66 to -0.43]). Forty-four of these "ventilator-associated VOCs" were also observed in vivo, without correlations with minute volume. In vivo experiments showed that only positive end-expiratory pressure influenced the concentration of breath VOCs. The sampling method was highly reproducible (median intra-class correlation 0.95 [25-75th percentile: 0.87-0.97]). In conclusion, a novel, simple and repeatable sampling method was developed and validated for capturing exhaled VOCs in ventilated patients, which could allow for large-scale breath analysis in clinical studies.

Time-frequency analysis of accelerometry data for detection of myoclonic seizures.

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.

Model for arm movements during myoclonic seizures.

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.

The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy.

Seizure detection results based on the visual analysis of three-dimensional (3D) accelerometry (ACM) and video/EEG recordings are reported for 18 patients with severe epilepsy. They were monitored for 36 hours during which 897 seizures were detected. This was seven times higher than the number of seizures reported by nurses during the registration period. The results in this article demonstrate that 3D ACM is a valuable sensing method for seizure detection in this population. Four hundred twenty-eight (48%) seizures were detected by ACM. With 3D ACM alone it was possible to detect all the seizures in 10 of the 18 patients. Three-dimensional ACM also was complementary to EEG in our population. ACM patterns during seizures were stereotypical in 95% of the motor seizures. These characteristic patterns are a starting point for automated seizure detection.