Exhaled Breath Metabolomics for the Diagnosis of Pneumonia in Intubated and Mechanically-Ventilated Intensive Care Unit (ICU)-Patients

Combination of real-time and hyphenated mass spectrometry for improved characterisation of exhaled breath biomarkers in clinical research

Exhaled breath volatilomics is a powerful non-invasive tool for biomarker discovery in medical applications, but compound annotation is essential for pathophysiological insights and technology transfer. This study was aimed at investigating the interest of a hybrid approach combining real-time proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) with comprehensive thermal desorption-two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GCxGC-TOF-MS) to enhance the analysis and characterization of VOCs in clinical research, using COVID-19 as a use case. VOC biomarker candidates were selected from clinical research using PTR-TOF-MS fingerprinting in patients with COVID-19 and matched to the Human Breathomic Database. Corresponding analytical standards were analysed using both a liquid calibration unit coupled to PTR-TOF-MS and TD-GCxGC-TOF-MS, together with confirmation on new clinical samples with TD-GCxGC-TOF-MS. From 26 potential VOC biomarkers, 23 were successfully detected with PTR-TOF-MS. All VOCs were successfully detected using TD-GCxGC-TOF-MS, providing effective separation of highly chemically related compounds, including isomers, and enabling high-confidence annotation based on two-dimensional chromatographic separation and mass spectra. Four VOCs were identified with a level 1 annotation in the clinical samples. For future applications, the combination of real-time PTR-TOF-MS and comprehensive TD-GCxGC-TOF-MS, at least on a subset of samples from a whole study, would enhance the performance of VOC annotation, offering potential advancements in biomarker discovery for clinical research.

Modern approaches for detection of volatile organic compounds in metabolic studies focusing on pathogenic bacteria: Current state of the art

Pathogenic microorganisms produce numerous metabolites, including volatile organic compounds (VOCs). Monitoring these metabolites in biological matrices (e.g., urine, blood, or breath) can reveal the presence of specific microorganisms, enabling the early diagnosis of infections and the timely implementation of targeted therapy. However, complex matrices only contain trace levels of VOCs, and their constituent components can hinder determination of these compounds. Therefore, modern analytical techniques enabling the non-invasive identification and precise quantification of microbial VOCs are needed. In this paper, we discuss bacterial VOC analysis under in vitro conditions, in animal models and disease diagnosis in humans, including techniques for offline and online analysis in clinical settings. We also consider the advantages and limitations of novel microextraction techniques used to prepare biological samples for VOC analysis, in addition to reviewing current clinical studies on bacterial volatilomes that address inter-species interactions, the kinetics of VOC metabolism, and species- and drug-resistance specificity.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

Identification of a marker of infection in the breath using a porcine pneumonia model

Objective

Pneumonia, both in the community and the hospital setting, represents a significant cause of morbidity and mortality in the cardiothoracic patient population. Diagnosis of pneumonia can be masked by other disease processes and is often diagnosed after the patient is already experiencing the disease. A noninvasive, sensitive test for pneumonia could decrease hospitalizations and length of stay for patients. We have developed a porcine model of pneumonia and evaluated the exhaled breath of infected pigs for biomarkers of infection.

Methods

Anesthetized 60-kg adult pigs were intubated, and a bronchoscope was used to instill a solution containing 12 × 108 cfu of methicillin-sensitive Staphylococcus aureus or a control solution without bacteria (Sham) into the distal airways. The pigs were then reintubated on postoperative days 3, 6, and 9, with bronchoscopic bronchial lavages taken at each time point. At each time point, a 500-mL breath was captured from each pig. The breath was evacuated over a silicon microchip, with the volatile carbonyl compounds from the breath captured via oximation reaction, and the results of this capture were analyzed by ultra-high performance liquid chromatography mass spectrometry.

Results

A total of 64% of the pigs inoculated with methicillin-sensitive S. aureus demonstrated consolidation on chest radiography and increasing counts of methicillin-sensitive S. aureus in the bronchial lavages over the span of the experiment, consistent with development of pneumonia. Analysis of the exhaled breath demonstrated 1 carbonyl compound (2-pentenal) that increased 10-fold over the span of the experiment, from an average of 0.0294 nmol/L before infection to an average of 0.3836 nmol/L on postoperative day 9. The amount of 2-pentenal present was greater in the breath of infected pigs than in the noninfected pigs or the sham inoculated pigs at postoperative days 6 and 9. Using an elevated concentration of 2-pentenal as a marker of infection yielded a sensitivity of 88% and specificity of 92% at postoperative day 6, and a sensitivity and specificity of 100% at postoperative day 9.

Conclusions

We were able to successfully develop a clinical pneumonia in adult 60-kg pigs. The concentration of 2-pentenal correlated with the presence of pneumonia, demonstrating the potential for this compound to function as a biomarker for methicillin-sensitive S. aureus infection in pigs.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant

Importance

Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown.

Objective

To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent.

Design, Setting, and Participants

This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography.

Main Outcomes and Measures

Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness.

Results

Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy.

Conclusions and Relevance

The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.

The role of proteomics and metabolomics in severe infections

PURPOSE OF REVIEW

Severe infections are a common cause of ICU admission, with a high morbidity and mortality. Omics, namely proteomics and metabolomics, aim to identify, characterize, and quantify biological molecules to achieve a systems-level understanding of disease. The aim of this review is to provide a clear overview of the current evidence of the role of proteomics and metabolomics in severe infections.

RECENT FINDINGS

Proteomics and metabolomics are technologies that are being used to explore new markers of diagnosis and prognosis, clarify mechanisms of disease, and consequently discover potential targets of therapy and finally of a better disease phenotyping. These technologies are starting to be used but not yet in clinical use.

SUMMARY

Our traditional way of approaching the disease as sepsis is believing that a process can be broken into its parts and that the whole can be explained by the sum of each part. This approach is highly reductionist and does not take the system complexity nor the nonlinear dynamics of the processes. Proteomics and metabolomics allow the analysis of several proteins and metabolites simultaneously, thereby generating diagnostic and prognostic signatures. An exciting future prospect for proteomics and metabolomics is their employment towards precision medicine.

Advanced wearable biosensors for the detection of body fluids and exhaled breath by graphene

Given the huge economic burden caused by chronic and acute diseases on human beings, it is an urgent requirement of a cost-effective diagnosis and monitoring process to treat and cure the disease in their preliminary stage to avoid severe complications. Wearable biosensors have been developed by using numerous materials for non-invasive, wireless, and consistent human health monitoring. Graphene, a 2D nanomaterial, has received considerable attention for the development of wearable biosensors due to its outstanding physical, chemical, and structural properties. Moreover, the extremely flexible, foldable, and biocompatible nature of graphene provide a wide scope for developing wearable biosensor devices. Therefore, graphene and its derivatives could be trending materials to fabricate wearable biosensor devices for remote human health management in the near future. Various biofluids and exhaled breath contain many relevant biomarkers which can be exploited by wearable biosensors non-invasively to identify diseases. In this article, we have discussed various methodologies and strategies for synthesizing and pattering graphene. Furthermore, general sensing mechanism of biosensors, and graphene-based biosensing devices for tear, sweat, interstitial fluid (ISF), saliva, and exhaled breath have also been explored and discussed thoroughly. Finally, current challenges and future prospective of graphene-based wearable biosensors have been evaluated with conclusion. Graphene is a promising 2D material for the development of wearable sensors. Various biofluids (sweat, tears, saliva and ISF) and exhaled breath contains many relevant biomarkers which facilitate in identify diseases. Biosensor is made up of biological recognition element such as enzyme, antibody, nucleic acid, hormone, organelle, or complete cell and physical (transducer, amplifier), provide fast response without causing organ harm.

Severe acute asthma at the pediatric intensive care unit: can we link the clinical phenotypes to immunological endotypes?

INTRODUCTION

The clinical phenotype of severe acute asthma at the pediatric intensive care unit (PICU) is highly heterogeneous. However, current treatment is still based on a 'one-size-fits-all approach'.

AREAS COVERED

We aim to give a comprehensive description of the clinical characteristics of pediatric patients with severe acute asthma admitted to the PICU and available immunological biomarkers, providing the first steps toward precision medicine for this patient population. A literature search was performed using PubMed for relevant studies on severe acute (pediatric) asthma.

EXPERT OPINION

Omics technologies should be used to investigate the relationship between cellular molecules and pathways, and their clinical phenotypes. Inflammatory phenotypes might guide bedside decisions regarding the use of corticosteroids, neutrophil modifiers and/or type of beta-agonist. A next step toward precision medicine should be inclusion of these patients in clinical trials on biologics.

Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies

PURPOSE

Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review and summarize state-of-the-art prediction models detecting or predicting VAP from exhaled breath, patient reports and demographic and clinical characteristics.

METHODS

Both diagnostic and prognostic prediction models were searched from a representative list of multidisciplinary databases. An extensive list of validated search terms was added to the search to cover papers failing to mention predictive research in their title or abstract. Two authors independently selected studies, while three authors extracted data using predefined criteria and data extraction forms. The Prediction Model Risk of Bias Assessment Tool was used to assess both the risk of bias and the applicability of the prediction modelling studies. Technology readiness was also assessed.

RESULTS

Out of 2052 identified studies, 20 were included. Fourteen (70%) studies reported the predictive performance of diagnostic models to detect VAP from exhaled human breath with a high degree of sensitivity and a moderate specificity. In addition, the majority of them were validated on a realistic dataset. The rest of the studies reported the predictive performance of diagnostic and prognostic prediction models to detect VAP from unstructured narratives [2 (10%)] as well as baseline demographics and clinical characteristics [4 (20%)]. All studies, however, had either a high or unclear risk of bias without significant improvements in applicability.

CONCLUSIONS

The development and deployment of prediction modelling studies are limited in VAP and related outcomes. More computational, translational, and clinical research is needed to bring these tools from the bench to the bedside.

REGISTRATION

PROSPERO CRD42020180218, registered on 05-07-2020.

Which Biomarkers Can Be Used as Diagnostic Tools for Infection in Suspected Sepsis?

The diagnosis of infection in patients with suspected sepsis is frequently difficult to achieve with a reasonable degree of certainty. Currently, the diagnosis of infection still relies on a combination of systemic manifestations, manifestations of organ dysfunction, and microbiological documentation. In addition, the microbiologic confirmation of infection is obtained only after 2 to 3 days of empiric antibiotic therapy. These criteria are far from perfect being at least in part responsible for the overuse and misuse of antibiotics, in the community and in hospital, and probably the main drive for antibiotic resistance. Biomarkers have been studied and used in several clinical settings as surrogate markers of infection to improve their diagnostic accuracy as well as in the assessment of response to antibiotics and in antibiotic stewardship programs. The aim of this review is to provide a clear overview of the current evidence of usefulness of biomarkers in several clinical scenarios, namely, to diagnose infection to prescribe antibiotics, to exclude infection to withhold antibiotics, and to identify the causative pathogen to target antimicrobial treatment. In recent years, new evidence with "old" biomarkers, like C-reactive protein and procalcitonin, as well as new biomarkers and molecular tests, as breathomics or bacterial DNA identification by polymerase chain reaction, increased markedly in different areas adding useful information for clinical decision making at the bedside when adequately used. The recent evidence shows that the information given by biomarkers can support the suspicion of infection and pathogen identification but also, and not less important, can exclude its diagnosis. Although the ideal biomarker has not yet been found, there are various promising biomarkers that represent true evolutions in the diagnosis of infection in patients with suspected sepsis.

Identification of volatile compounds from bacteria by spectrometric methods in medicine diagnostic and other areas: current state and perspectives

Diagnosis of bacterial infections until today mostly relies on conventional microbiological methods. The resulting long turnaround times can lead to delayed initiation of adequate antibiotic therapy and prolonged periods of empiric antibiotic therapy (e.g., in intensive care medicine). Therewith, they contribute to the mortality of bacterial infections and the induction of multidrug resistances. The detection of species specific volatile organic compounds (VOCs) emitted by bacteria has been proposed as a possible diagnostic approach with the potential to serve as an innovative point-of-care diagnostic tool with very short turnaround times. A range of spectrometric methods are available which allow the detection and quantification of bacterial VOCs down to a range of part per trillion. This narrative review introduces the application of spectrometric analytical methods for the purpose of detecting VOCs of bacterial origin and their clinical use for diagnosing different infectious conditions over the last decade. KEY POINTS: • Detection of VOCs enables bacterial differentiation in various medical conditions. • Spectrometric methods may function as point-of-care diagnostics in near future.

Breath-Based Diagnosis of Infectious Diseases: A Review of the Current Landscape

Various analytical methods can be applied to concentrate, separate, and examine trace volatile organic metabolites in the breath, with the potential for noninvasive, rapid, real-time identification of various disease processes, including an array of microbial infections. Although biomarker discovery and validation in microbial infections can be technically challenging, it is an approach that has shown great promise, especially for infections that are particularly difficult to identify with standard culture and molecular amplification-based approaches. This article discusses the current state of breath analysis for the diagnosis of infectious diseases.

Targeted exhaled breath analysis for detection of Pseudomonas aeruginosa in cystic fibrosis patients

BACKGROUND

Pseudomonas aeruginosa (PA) is an important respiratory pathogen for cystic fibrosis (CF) patients. Routine microbiology surveillance is time-consuming, and is best performed on expectorated sputum. As alternative, volatile organic compounds (VOCs) may be indicative of PA colonisation. In this study, we aimed to identify VOCs associated with PA in literature and perform targeted exhaled breath analysis to recognize PA positive CF patients non-invasively.

METHODS

This study consisted of 1) a literature review to select VOCs of interest, and 2) a cross-sectional CF study. Definitions used: A) PA positive, PA culture at visit/chronically; B) PA free, no PA culture in ≥12 months. Exhaled VOCs were identified via quadrupole MS. The primary endpoint was the area under the receiver operating characteristics curve (AUROCC) of individual VOCs as well as combined VOCs against PA culture.

RESULTS

241 VOCs were identified in literature, of which 56 were further evaluated, and 13 could be detected in exhaled breath in our cohort. Exhaled breath of 25 pediatric and 28 adult CF patients, PA positive (n=16) and free (n=28) was available. 3/13 VOCs were significantly (p<0.05) different between PA groups in children; none were in adults. Notably, a composite model based on 5 or 1 VOC(s) showed an AUROCC of 0.86 (CI 0.71-1.0) and 0.87 (CI 0.72-1.0) for adults and children, respectively.

CONCLUSIONS

Targeted VOC analysis appears to discriminate children and adults with and without PA positive cultures with clinically acceptable sensitivity values.

Breathomics for the clinician: the use of volatile organic compounds in respiratory diseases

Exhaled breath analysis has the potential to provide valuable insight on the status of various metabolic pathways taking place in the lungs locally and other vital organs, via systemic circulation. For years, volatile organic compounds (VOCs) have been proposed as feasible alternative diagnostic and prognostic biomarkers for different respiratory pathologies.We reviewed the currently published literature on the discovery of exhaled breath VOCs and their utilisation in various respiratory diseasesKey barriers in the development of clinical breath tests include the lack of unified consensus for breath collection and analysis and the complexity of understanding the relationship between the exhaled VOCs and the underlying metabolic pathways. We present a comprehensive overview, in light of published literature and our experience from coordinating a national breathomics centre, of the progress made to date and some of the key challenges in the field and ways to overcome them. We particularly focus on the relevance of breathomics to clinicians and the valuable insights it adds to diagnostics and disease monitoring.Breathomics holds great promise and our findings merit further large-scale multicentre diagnostic studies using standardised protocols to help position this novel technology at the centre of respiratory disease diagnostics.

A literature survey of all volatiles from healthy human breath and bodily fluids: the human volatilome

This paper comprises an updated version of the 2014 review which reported 1846 volatile organic compounds (VOCs) identified from healthy humans. In total over 900 additional VOCs have been reported since the 2014 review and the VOCs from semen have been added. The numbers of VOCs found in breath and the other bodily fluids are: blood 379, breath 1488, faeces 443, milk 290, saliva 549, semen 196, skin 623 and urine 444. Compounds were assigned CAS registry numbers and named according to a common convention where possible. The compounds have been included in a single table with the source reference(s) for each VOC, an update on our 2014 paper. VOCs have also been grouped into tables according to their chemical class or functionality to permit easy comparison. Careful use of the database is needed, as a number of the identified VOCs only have level 2-putative assignment, and only a small fraction of the reported VOCs have been validated by standards. Some clear differences are observed, for instance, a lack of esters in urine with a high number in faeces and breath. However, the lack of compounds from matrices such a semen and milk compared to breath for example could be due to the techniques used or reflect the intensity of effort e.g. there are few publications on VOCs from milk and semen compared to a large number for breath. The large number of volatiles reported from skin is partly due to the methodologies used, e.g. by collecting skin sebum (with dissolved VOCs and semi VOCs) onto glass beads or cotton pads and then heating to a high temperature to desorb VOCs. All compounds have been included as reported (unless there was a clear discrepancy between name and chemical structure), but there may be some mistaken assignations arising from the original publications, particularly for isomers. It is the authors' intention that this work will not only be a useful database of VOCs listed in the literature but will stimulate further study of VOCs from healthy individuals; for example more work is required to confirm the identification of these VOCs adhering to the principles outlined in the metabolomics standards initiative. Establishing a list of volatiles emanating from healthy individuals and increased understanding of VOC metabolic pathways is an important step for differentiating between diseases using VOCs.

Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications

Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.

Airway Pseudomonas aeruginosa density in mechanically ventilated patients: clinical impact and relation to therapeutic efficacy of antibiotics

Background

The bacterial density of Pseudomonas aeruginosa is closely related to its pathogenicity. We evaluated the effect of airway P. aeruginosa density on the clinical course of mechanically ventilated patients and the therapeutic efficacy of antibiotics.

Methods

We retrospectively analyzed data of mechanically ventilated ICU patients with P. aeruginosa isolated from endotracheal aspirates. Patients were divided into three groups according to the peak P. aeruginosa density during ICU stay: low (≤ 10⁴ cfu/mL), moderate (10⁵‒10⁶ cfu/mL), and high (≥ 10⁷ cfu/mL) peak density groups. The relationship between peak P. aeruginosa density and weaning from mechanical ventilation, risk factors for isolation of high peak density of P. aeruginosa, and antibiotic efficacy were investigated using multivariate and propensity score-matched analyses.

Results

Four-hundred-and-sixty-one patients were enrolled. Patients with high peak density of P. aeruginosa had higher inflammation and developed more severe respiratory infections. High peak density of P. aeruginosa was independently associated with few ventilator-free days on day 28 (P < 0.01) and increased ICU mortality (P = 0.047). Risk factors for high peak density of P. aeruginosa were prolonged mechanical ventilation (odd ratio [OR] 3.07 95% confidence interval [CI] 1.35‒6.97), non-antipseudomonal cephalosporins (OR 2.17, 95% CI 1.35‒3.49), hyperglycemia (OR 2.01, 95% CI 1.26‒3.22) during ICU stay, and respiratory diseases (OR 1.9, 95% CI 1.12‒3.23). Isolation of commensal colonizer was associated with lower risks of high peak density of P. aeruginosa (OR 0.43, 95% CI 0.26‒0.73). Propensity score-matched analysis revealed that antibiotic therapy for patients with ventilator-associated tracheobronchitis improved weaning from mechanical ventilation only in the high peak P. aeruginosa group.

Conclusions

Patients with high peak density of P. aeruginosa had worse ventilator outcome and ICU mortality. In patients with ventilator-associated tracheobronchitis, antibiotic therapy was associated with favorable ventilator weaning only in the high peak P. aeruginosa density group, and bacterial density could be a good therapeutic indicator for ventilator-associated tracheobronchitis due to P. aeruginosa.

Detection and quantification of exhaled volatile organic compounds in mechanically ventilated patients - comparison of two sampling methods

Exhaled breath analysis is a promising new diagnostic tool, but currently no standardised method for sampling is available in mechanically ventilated patients. We compared two breath sampling methods, first using an artificial ventilator circuit, then in "real life" in mechanically ventilated patients on the intensive care unit. In the laboratory circuit, a 24-component synthetic-breath volatile organic compound (VOC) mixture was injected into the system as air was sampled: (A) through a port on the exhalation limb of the circuit and (B) through a closed endo-bronchial suction catheter. Sorbent tubes were used to collect samples for analysis by thermal desorption-gas chromatography-mass spectrometry. Realistic mechanical ventilation rates and breath pressure-volume loops were established and method detection limits (MDLs) were calculated for all VOCs. Higher yields of VOCs were retrieved using the closed suction catheter; however, for several VOCs MDLs were compromised due to the background signal associated with plastic and rubber components in the catheters. Different brands of suction catheter were compared. Exhaled VOC data from 40 patient samples collected at two sites were then used to calculate the proportion of data analysed above the MDL. The relative performance of the two methods differed depending on the VOC under study and both methods showed sensitivity towards different exhaled VOCs. Furthermore, method performance differed depending on recruitment site, as the centres were equipped with different brands of respiratory equipment, an important consideration for the design of multicentre studies investigating exhaled VOCs in mechanically ventilated patients.

Antimicrobial Stewardship in the Intensive Care Unit: The Role of Biomarkers, Pharmacokinetics, and Pharmacodynamics

The high prevalence of infectious diseases in the intensive care unit (ICU) and consequently elevated pressure for immediate and effective treatment have led to increased antimicrobial therapy consumption and misuse. Moreover, the emerging global threat of antimicrobial resistance and lack of novel antimicrobials justify the implementation of judicious antimicrobial stewardship programs (ASP) in the ICU. However, even though the importance of ASP is generally accepted, its implementation in the ICU is far from optimal and current evidence regarding strategies such as de-escalation remains controversial. The limitations of clinical guidance for antimicrobial therapy initiation and discontinuation have led to multiple studies for the evaluation of more objective tools, such as biomarkers as adjuncts for ASP. C-reactive protein and procalcitonin can be adequate for clinical use in acute infectious diseases, the latter being the most studied for ASP purposes. Although promising, current evidence highlights challenges in biomarker application and interpretation. Furthermore, the physiological alterations in the critically ill render pharmacokinetics and pharmacodynamics crucial parameters for adequate antimicrobial therapy use. Individual pharmacokinetic and pharmacodynamic targets can reduce antimicrobial therapy misuse and risk of antimicrobial resistance.

Which Current and Novel Diagnostic Avenues for Bacterial Respiratory Diseases?

Bacterial acute pneumonia is responsible for an extremely large burden of death worldwide and diagnosis is paramount in the management of patients. While multidrug-resistant bacteria is one of the biggest health threats in the coming decades, clinicians urgently need access to novel diagnostic technologies. In this review, we will first present the already existing and largely used techniques that allow identifying pathogen-associated pneumonia. Then, we will discuss the latest and most promising technological advances that are based on connected technologies (artificial intelligence-based and Omics-based) or rapid tests, to improve the management of lung infections caused by pathogenic bacteria. We also aim to highlight the mutual benefits of fundamental and clinical studies for a better understanding of lung infections and their more efficient diagnostic management.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

Plasma lipid profiling for the prognosis of 90-day mortality, in-hospital mortality, ICU admission, and severity in bacterial community-acquired pneumonia (CAP)

Introduction

Pneumonia is the most common cause of mortality from infectious diseases, the second leading cause of nosocomial infection, and the leading cause of mortality among hospitalized adults. To improve clinical management, metabolomics has been increasingly applied to find specific metabolic biopatterns (profiling) for the diagnosis and prognosis of various infectious diseases, including pneumonia.

Methods

One hundred fifty bacterial community-acquired pneumonia (CAP) patients whose plasma samples were drawn within the first 24 h of hospital admission were enrolled in this study and separated into two age- and sex-matched cohorts: non-survivors (died ≤ 90 days) and survivors (survived > 90 days). Three analytical tools, ¹H-NMR spectroscopy, GC-MS, and targeted DI-MS/MS, were used to prognosticate non-survivors from survivors by means of metabolic profiles.

Results

We show that quantitative lipid profiling using DI-MS/MS can predict the 90-day mortality and in-hospital mortality among patients with bacterial CAP compared to ¹H-NMR- and GC-MS-based metabolomics. This study showed that the decreased lysophosphatidylcholines and increased acylcarnitines are significantly associated with increased mortality in bacterial CAP. Additionally, we found that decreased lysophosphatidylcholines and phosphatidylcholines (> 36 carbons) and increased acylcarnitines may be used to predict the prognosis of in-hospital mortality for bacterial CAP as well as the need for ICU admission and severity of bacterial CAP.

Discussion

This study demonstrates that lipid-based plasma metabolites can be used for the prognosis of 90-day mortality among patients with bacterial CAP. Moreover, lipid profiling can be utilized to identify patients with bacterial CAP who are at the highest risk of dying in hospital and who need ICU admission as well as the severity assessment of CAP.

Microbiota-derived metabolites as diagnostic markers for respiratory fungal infections

An emerging body of evidence has highlighted the significant role of the pulmonary microbiota during respiratory infections. The individual microbiome is nowadays recognized to supervise the outcome of the host-pathogen interaction by orchestrating mechanisms of immune regulation, inflammation, metabolism, and other physiological processes. A shift in the normal flora of the respiratory tract is associated with several lung inflammatory disorders including asthma, chronic obstructive pulmonary disease, or cystic fibrosis. These diseases are characterized by a lung microenvironment that becomes permissive to infections caused by the opportunistic fungal pathogen Aspergillus fumigatus. Although the role of the lung microbiota in the pathophysiology of respiratory fungal diseases remains elusive, microbiota-derived components have been proposed as important biomarkers to be considered in the diagnosis of these severe infections. Here, we review this emerging area of research and discuss the potential of microbiota-derived products in the diagnosis of respiratory fungal diseases.

On detection thresholds-a review on diagnostic approaches in the infectious disease laboratory and the interpretation of their results

Diagnostic testing in the infectious disease laboratory facilitates decision-making by physicians at the bedside as well as epidemiological assessments and surveillance at study level. Problems may arise if test results are uncritically considered as being the same as the unknown true value. To allow a better understanding, the influence of external factors on the interpretation of test results is introduced with the example of prevalence, followed by the presentation of strengths and weaknesses of important techniques in the infectious disease laboratory like microscopy, cultural diagnostics, serology, mass spectrometry, nucleic acid amplification and hypothesis-free metagenomic sequencing with focus on basic, high-technology and potential future approaches. Special problems like multiplex testing as well as uncertainty of test evaluations, if no gold standard is available, are also stressed with a final glimpse on emerging future technologies for the infectious disease laboratory. In the conclusions, suitability for point-of-care-testing and field laboratory applications is summarized. The aim is to illustrate the limitations of diagnostic accuracy to both clinicians and study planners and to stress the importance of close cooperation with experts in laboratory disciplines so as to avoid potentially critical misunderstandings due to inappropriate interpretation of diagnostic test results.

Breath Metabolomics Provides an Accurate and Noninvasive Approach for Screening Cirrhosis, Primary, and Secondary Liver Tumors

Hepatocellular carcinoma (HCC) and secondary liver tumors, such as colorectal cancer liver metastases are significant contributors to the overall burden of cancer‐related morality. Current biomarkers, such as alpha‐fetoprotein (AFP) for HCC, result in too many false negatives, necessitating noninvasive approaches with improved sensitivity. Volatile organic compounds (VOCs) detected in the breath of patients can provide valuable insight into disease processes and can differentiate patients by disease status. Here, we investigate whether 22 VOCs from the breath of 296 patients can distinguish those with no liver disease (n = 54), cirrhosis (n = 30), HCC (n = 112), pulmonary hypertension (n = 49), or colorectal cancer liver metastases (n = 51). This work extends previous studies by evaluating the ability for VOC signatures to differentiate multiple diseases in a large cohort of patients. Pairwise disease comparisons demonstrated that most of the VOCs tested are present in significantly different relative abundances (false discovery rate P < 0.1), indicating broad impacts on the breath metabolome across diseases. A predictive model developed using random forest machine learning and cross validation classified patients with 85% classification accuracy and 75% balanced accuracy. Importantly, the model detected HCC with 73% sensitivity compared with 53% for AFP in the same cohort. An added value of this approach is that influential VOCs in the predictive model may provide insight into disease etiology. Acetaldehyde and acetone, both of which have roles in tumor promotion, were considered important VOCs for differentiating disease groups in the predictive model and were increased in patients with cirrhosis and HCC compared to patients with no liver disease (false discovery rate P < 0.1). Conclusion: The use of machine learning and breath VOCs shows promise as an approach to develop improved, noninvasive screening tools for chronic liver disease and primary and secondary liver tumors.

Exhaled volatile organic compounds as markers for medication use in asthma

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.

Volatile organic compounds in breath can serve as a non-invasive diagnostic biomarker for the detection of advanced adenomas and colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer diagnosis in the Western world.

AIM

To evaluate exhaled volatile organic compounds (VOCs) as a non-invasive biomarker for the detection of CRC and precursor lesions using an electronic nose.

METHODS

In this multicentre study adult colonoscopy patients, without inflammatory bowel disease or (previous) malignancy, were invited for breath analysis. Two-thirds of the breath tests were randomly assigned to develop training models which were used to predict the diagnosis of the remaining patients (external validation). In the end, all data were used to develop final-disease models to further improve the discriminatory power of the algorithms.

RESULTS

Five hundred and eleven breath samples were collected. Sixty-four patients were excluded due to an inadequate breath test (n = 51), incomplete colonoscopy (n = 8) or colitis (n = 5). Classification was based on the most advanced lesion found; CRC (n = 70), advanced adenomas (AAs) (n = 117), non-advanced adenoma (n = 117), hyperplastic polyp (n = 15), normal colonoscopy (n = 125). Training models for CRC and AAs had an area under the curve (AUC) of 0.76 and 0.71 and blind validation resulted in an AUC of 0.74 and 0.61 respectively. Final models for CRC and AAs yielded an AUC of 0.84 (sensitivity 95% and specificity 64%) and 0.73 (sensitivity and specificity 79% and 59%) respectively.

CONCLUSIONS

This study suggests that exhaled VOCs could potentially serve as a non-invasive biomarker for the detection of CRC and AAs. Future studies including more patients could further improve the discriminatory potential of VOC analysis for the detection of (pre-)malignant colorectal lesions. (https://clinicaltrials.gov Identifier NCT03488537).

A Low-Cost Breath Analyzer Module in Domiciliary Non-Invasive Mechanical Ventilation for Remote COPD Patient Monitoring

Smart Breath Analyzers were developed as sensing terminals of a telemedicine architecture devoted to remote monitoring of patients suffering from Chronic Obstructive Pulmonary Disease (COPD) and home-assisted by non-invasive mechanical ventilation via respiratory face mask. The devices based on different sensors (CO₂/O₂ and Volatile Organic Compounds (VOCs), relative humidity and temperature (R.H. & T) sensors) monitor the breath air exhaled into the expiratory line of the bi-tube patient breathing circuit during a noninvasive ventilo-therapy session; the sensor raw signals are transmitted pseudonymized to National Health Service units by TCP/IP communication through a cloud remote platform. The work is a proof-of-concept of a sensors-based IoT system with the perspective to check continuously the effectiveness of therapy and/or any state of exacerbation of the disease requiring healthcare. Lab tests in controlled experimental conditions by a gas-mixing bench towards CO₂/O₂ concentrations and exhaled breath collected in a sampling bag were carried out to test the realized prototypes. The Smart Breath Analyzers were also tested in real conditions both on a healthy volunteer subject and a COPD suffering patient.

Exhaled breath metabolomics reveals a pathogen-specific response in a rat pneumonia model for two human pathogenic bacteria: a proof-of-concept study

Volatile organic compounds in breath can reflect host and pathogen metabolism and might be used to diagnose pneumonia. We hypothesized that rats with Streptococcus pneumoniae ( SP) or Pseudomonas aeruginosa ( PA) pneumonia can be discriminated from uninfected controls by thermal desorption-gas chromatography-mass-spectrometry (TD-GC-MS) and selected ion flow tube-mass spectrometry (SIFT-MS) of exhaled breath. Male adult rats ( n = 50) received an intratracheal inoculation of 1) 200 µl saline, or 2) 1 × 107 colony-forming units of SP or 3) 1 × 107 CFU of PA. Twenty-four hours later the rats were anaesthetized, tracheotomized, and mechanically ventilated. Exhaled breath was analyzed via TD-GC-MS and SIFT-MS. Area under the receiver operating characteristic curves (AUROCCs) and correct classification rate (CCRs) were calculated after leave-one-out cross-validation of sparse partial least squares-discriminant analysis. Analysis of GC-MS data showed an AUROCC (95% confidence interval) of 0.85 (0.73–0.96) and CCR of 94.6% for infected versus noninfected animals, AUROCC of 0.98 (0.94–1) and CCR of 99.9% for SP versus PA, 0.92 (0.83–1.00), CCR of 98.1% for SP versus controls and 0.97 (0.92–1.00), and CCR of 99.9% for PA versus controls. For these comparisons the SIFT-MS data showed AUROCCs of 0.54, 0.89, 0.63, and 0.79, respectively. Exhaled breath analysis discriminated between respiratory infection and no infection but with even better accuracy between specific pathogens. Future clinical studies should not only focus on the presence of respiratory infection but also on the discrimination between specific pathogens.

Volatile organic compound profiles in outlet air from extracorporeal life-support devices differ from breath profiles in critically ill patients

Introduction

It is highly uncertain whether volatile organic compounds (VOCs) in exhaled breath of critically ill intensive care unit patients are formed in the lung locally, in the air compartment or lung tissue, or elsewhere in the body and transported to the lung via the bloodstream. We compared VOC mixtures in exhaled breath and in air coming from extracorporeal support devices in critically ill patients to address this issue.

Methods

First, we investigated whether it was safe to connect an electronic nose (eNose) or a gas sampling pump to extracorporeal support membranes. Then, breath and air from extracorporeal support devices were collected simultaneously for continuous monitoring of VOC mixtures using an eNose. In addition, samples for gas chromatography/mass spectrometry (GC-MS) analysis were taken daily at the two measurement sites.

Results

10 critically ill patients were monitored for a median (interquartile range) duration of 73 (72-113) h; in total, we had 887 h of air sampling. The eNose signals of breath correlated moderately with signals of air from the extracorporeal support devices (R²=0.25-0.44). After GC-MS analysis, 96 VOCs were found both in breath and air from the extracorporeal support devices; of these, 29 (30%) showed a significant correlation (p<0.05) between the two measurement sites, of which 17 were identified. VOCs that did not correlate were found in a higher concentration in breath than in air from the extracorporeal support devices.

Conclusion

This study suggests VOC analysis in the extracorporeal circulation is safe, and that VOCs of nonpulmonary origin can be measured in the breath and in the extracorporeal circulation of critically ill patients. For VOCs that did not correlate between the two measurement sites, the breath concentration was higher, suggesting pulmonary production of these molecules in a highly selected population of patients that received extracorporeal support.

Metabolomic Insights into Human Arboviral Infections: Dengue, Chikungunya, and Zika Viruses

The global burden of arboviral diseases and the limited success in controlling them calls for innovative methods to understand arbovirus infections. Metabolomics has been applied to detect alterations in host physiology during infection. This approach relies on mass spectrometry or nuclear magnetic resonance spectroscopy to evaluate how perturbations in biological systems alter metabolic pathways, allowing for differentiation of closely related conditions. Because viruses heavily depend on host resources and pathways, they present unique challenges for characterizing metabolic changes. Here, we review the literature on metabolomics of arboviruses and focus on the interpretation of identified molecular features. Metabolomics has revealed biomarkers that differentiate disease states and outcomes, and has shown similarities in metabolic alterations caused by different viruses (e.g., lipid metabolism). Researchers investigating such metabolomic alterations aim to better understand host⁻virus dynamics, identify diagnostically useful molecular features, discern perturbed pathways for therapeutics, and guide further biochemical research. This review focuses on lessons derived from metabolomics studies on samples from arbovirus-infected humans.

Developments to monitor the exhalome in organ failure in critically ill patients-a look into the future

Critically ill patients typically need some kind of functional organ support or replacement. Cardiopulmonary and renal replacement therapies are well established measures in intensive care units. However, there are also inherent risks associated with these treatments. The appropriate and timely commencement, maintenance and termination of organ replacement procedures currently use weak surrogates as decision support in clinical practice. A more reasonable application of extracorporeal organ support can be expected to potentially lower adverse events and save costs in healthcare systems, if a precise online monitoring was available. The analysis of the exhalome offers great opportunities to detect circulatory, pulmonary, and renal failure in critically ill patients. Volatile organic compounds and exhalation patterns are associated with a series of metabolic disorders and may be key to indicate the appropriate time point for initiation, maintenance and termination of organ support technologies. It may thus be expected that mortality, infection risk, replacement therapy days, and medical costs of intensive care treatment may possibly be reduced using exhalome analysis for control of organ replacement therapies in the distant future.

The potential role of exhaled breath analysis in the diagnostic process of pneumonia-a systematic review

Diagnostic strategies currently used for pneumonia are time-consuming, lack accuracy and suffer from large inter-observer variability. Exhaled breath contains thousands of volatile organic compounds (VOCs), which include products of host and pathogen metabolism. In this systematic review we investigated the use of so-called 'breathomics' for diagnosing pneumonia. A Medline search yielded 18 manuscripts reporting on animal and human studies using organic and inorganic molecules in exhaled breath, that all could be used to answer whether analysis of VOC profiles could potentially improve the diagnostic process of pneumonia. Papers were categorised based on their specific aims; the exclusion of pneumonia; the detection of specific respiratory pathogens; and whether targeted or untargeted VOC analysis was used. Ten studies reported on the association between VOCs and presence of pneumonia. Eight studies demonstrated a difference in exhaled VOCs between pneumonia and controls; in the individual studies this discrimination was based on unique sets of VOCs. Eight studies reported on the accuracy of a breath test for a specific respiratory pathogen: five of these concerned pre-clinical studies in animals. All studies were valued as having a high risk of bias, except for one study that used an external validation cohort. The findings in the identified studies are promising. However, as yet no breath test has been shown to have sufficient diagnostic accuracy for pneumonia. We are in need of studies that further translate the knowledge from discovery studies to clinical practice.

Metabolomics in Sepsis and Its Impact on Public Health

Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of "-omics" technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i.e., the study of the metabolome), an "-omics" technology further down in the "-omics" cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software.

Identifying volatile metabolite signatures for the diagnosis of bacterial respiratory tract infection using electronic nose technology: A pilot study

OBJECTIVES

New point of care diagnostics are urgently needed to reduce the over-prescription of antimicrobials for bacterial respiratory tract infection (RTI). We performed a pilot cross sectional study to assess the feasibility of gas-capillary column ion mobility spectrometer (GC-IMS), for the analysis of volatile organic compounds (VOC) in exhaled breath to diagnose bacterial RTI in hospital inpatients.

METHODS

71 patients were prospectively recruited from the Acute Medical Unit of the Royal Liverpool University Hospital between March and May 2016 and classified as confirmed or probable bacterial or viral RTI on the basis of microbiologic, biochemical and radiologic testing. Breath samples were collected at the patient's bedside directly into the electronic nose device, which recorded a VOC spectrum for each sample. Sparse principal component analysis and sparse logistic regression were used to develop a diagnostic model to classify VOC spectra as being caused by bacterial or non-bacterial RTI.

RESULTS

Summary area under the receiver operator characteristic curve was 0.73 (95% CI 0.61-0.86), summary sensitivity and specificity were 62% (95% CI 41-80%) and 80% (95% CI 64-91%) respectively (p = 0.00147).

CONCLUSIONS

GC-IMS analysis of exhaled VOC for the diagnosis of bacterial RTI shows promise in this pilot study and further trials are warranted to assess this technique.

Exhaled Volatile Organic Compounds of Infection: A Systematic Review

With heightened global concern of microbial drug resistance, advanced methods for early and accurate diagnosis of infection are urgently needed. Analysis of exhaled breath volatile organic compounds (VOCs) toward detecting microbial infection potentially allows a highly informative and noninvasive alternative to current genomics and culture-based methods. We performed a systematic review of research literature reporting human and animal exhaled breath VOCs related to microbial infections. In this Review, we find that a wide range of breath sampling and analysis methods are used by researchers, which significantly affects interstudy method comparability. Studies either perform targeted analysis of known VOCs relating to an infection, or non-targeted analysis to obtain a global profile of volatile metabolites. In general, the field of breath analysis is still relatively immature, and there is much to be understood about the metabolic production of breath VOCs, particularly in a host where both commensal microflora as well as pathogenic microorganisms may be manifested in the airways. We anticipate that measures to standardize high throughput sampling and analysis, together with an increase in large scale collaborative international trials, will bring routine breath VOC analysis to improve diagnosis of infection closer to reality.