Host-Based Diagnostics for Acute Respiratory Infections

Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis

At present, the diagnosis of lower respiratory tract infections (LRTIs) is difficult, and there is an urgent need for better diagnostic methods. This study enrolled 136 patients from 2020 to 2021 and collected bronchoalveolar lavage fluid (BALF) specimens. We used metatranscriptome to analyze the lower respiratory tract microbiome (LRTM) and host immune response. The diversity of the LRTM in LRTIs significantly decreased, manifested by a decrease in the abundance of normal microbiota and an increase in the abundance of opportunistic pathogens. The upregulated differentially expressed genes (DEGs) in the LRTIs group were mainly enriched in infection immune response-related pathways. Klebsiella pneumoniae had the most significant increase in abundance in LRTIs, which was strongly correlated with host infection or inflammation genes TNFRSF1B, CSF3R, and IL6R. We combined LRTM and host transcriptome data to construct a machine-learning model with 12 screened features to discriminate LRTIs and non-LRTIs. The results showed that the model trained by Random Forest in the validate set had the best performance (ROC AUC: 0.937, 95% CI: 0.832-1). The independent external dataset showed an accuracy of 76.5% for this model. This study suggests that the model integrating LRTM and host transcriptome data can be an effective tool for LRTIs diagnosis.

Integrated epigenomic exposure signature discovery

Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease

BACKGROUND

Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression.

METHODS

We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics.

RESULTS

Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822-0.972]; DB from DV with AUC of 0.825 [0.691-0.959] (signature-1) and 0.867 [0.753-0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787-0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808-1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%).

CONCLUSIONS

Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups.

Patterns and predictors of positive multiplex polymerase chain reaction respiratory panel among patients with acute respiratory infections in a single center in Lebanon

BACKGROUND

Infectious agents associated with community-acquired acute respiratory infections (ARIs) remain understudied in Lebanon. We aim to assess the microbiological profiles of ARIs by employing polymerase chain reaction (PCR) and identifying predictors of positive PCR results among patients admitted for ARI.

METHODS AND RESULTS

We conducted a retrospective single-center study at the American University of Beirut Medical Center, including all respiratory PCR panels performed on pediatric (< 18) and adult (≥ 18) patients presenting with an ARI from January 2015 to March 2018, prior to the onset of the COVID-19 pandemic. We aimed to identify the epidemiological patterns of ARIs and the factors associated with positive PCRs in both adult and pediatric patients. Among 281 respiratory PCRs, 168 (59.7%) were positive for at least one pathogen, with 54.1% positive PCR for viruses, 7.8% for bacteria species, and 3.9% with virus-bacteria codetection. Almost 60% of the patients received antibiotics prior to PCR testing. PCR panels yielded more positive results in pediatric patients than in adults (P = 0.005). Bacterial detection was more common in adults compared to pediatrics (P < 0.001). The most common organism recovered in the entire population was Human Rhinovirus (RhV) (18.5%). Patients with pleural effusion on chest CT were less likely to have a positive PCR (95% Cl: 0.22-0.99). On multivariate analysis, pediatric age group (P < 0.001), stem cell transplant (P = 0.006), fever (P = 0.03) and UTRI symptoms (P = 0.004) were all predictive of a positive viral PCR.

CONCLUSION

Understanding the local epidemiology of ARI is crucial for proper antimicrobial stewardship. The identification of factors associated with positive respiratory PCR enhances our understanding of clinical characteristics and potential predictors of viral detection in our population.

Managing Viral Emerging Infectious Diseases via Current and Future Molecular Diagnostics

Emerging viral infectious diseases have been a constant threat to global public health in recent times. In managing these diseases, molecular diagnostics has played a critical role. Molecular diagnostics involves the use of various technologies to detect the genetic material of various pathogens, including viruses, in clinical samples. One of the most commonly used molecular diagnostics technologies for detecting viruses is polymerase chain reaction (PCR). PCR amplifies specific regions of the viral genetic material in a sample, making it easier to detect and identify viruses. PCR is particularly useful for detecting viruses that are present in low concentrations in clinical samples, such as blood or saliva. Another technology that is becoming increasingly popular for viral diagnostics is next-generation sequencing (NGS). NGS can sequence the entire genome of a virus present in a clinical sample, providing a wealth of information about the virus, including its genetic makeup, virulence factors, and potential to cause an outbreak. NGS can also help identify mutations and discover new pathogens that could affect the efficacy of antiviral drugs and vaccines. In addition to PCR and NGS, there are other molecular diagnostics technologies that are being developed to manage emerging viral infectious diseases. One of these is CRISPR-Cas, a genome editing technology that can be used to detect and cut specific regions of viral genetic material. CRISPR-Cas can be used to develop highly specific and sensitive viral diagnostic tests, as well as to develop new antiviral therapies. In conclusion, molecular diagnostics tools are critical for managing emerging viral infectious diseases. PCR and NGS are currently the most commonly used technologies for viral diagnostics, but new technologies such as CRISPR-Cas are emerging. These technologies can help identify viral outbreaks early, track the spread of viruses, and develop effective antiviral therapies and vaccines.

Epidemiology of Community-Acquired Respiratory Tract Infections in Patients Admitted at the Emergency Departments

Objectives: Community-acquired respiratory infections (CARTIs) are responsible for serious morbidities worldwide. Identifying the aetiology can decrease the use of unnecessary antimicrobial therapy. In this study, we intend to determine the pathogenic agents responsible for respiratory infections in patients presenting to the emergency department of several Lebanese hospitals. Methods: A total of 100 patients presenting to the emergency departments of four Lebanese hospitals and identified as having CARTIs between September 2017 and September 2018 were recruited. Specimens of upper and lower respiratory tract samples were collected. Pathogens were detected by a multiplex polymerase chain reaction respiratory panel. Results: Of 100 specimens, 84 contained at least one pathogen. Many patients were detected with ≥2 pathogens. The total number of pathogens from these 84 patients was 163. Of these pathogens, 36 (22%) were human rhinovirus, 28 (17%) were Streptococcus pneumoniae, 16 (10%) were metapneumovirus, 16 (10%) were influenza A virus, and other pathogens were detected with lower percentages. As expected, the highest occurrence of pathogens was observed between December and March. Respiratory syncytial virus accounted for 2% of the cases and only correlated to paediatric patients. Conclusion: CARTI epidemiology is important and understudied in Lebanon. This study offers the first Lebanese data about CARTI pathogens. Viruses were the most common aetiologies of CARTIs. Thus, a different approach must be used for the empirical management of CARTI. Rapid testing might be useful in identifying patients who need antibiotic therapy.

Advances in diagnostic tools for respiratory tract infections: from tuberculosis to COVID-19 - changing paradigms?

Respiratory tract infections (RTIs) are one of the most common reasons for seeking healthcare, but are amongst the most challenging diseases in terms of clinical decision-making. Proper and timely diagnosis is critical in order to optimise management and prevent further emergence of antimicrobial resistance by misuse or overuse of antibiotics. Diagnostic tools for RTIs include those involving syndromic and aetiological diagnosis: from clinical and radiological features to laboratory methods targeting both pathogen detection and host biomarkers, as well as their combinations in terms of clinical algorithms. They also include tools for predicting severity and monitoring treatment response. Unprecedented milestones have been achieved in the context of the COVID-19 pandemic, involving the most recent applications of diagnostic technologies both at genotypic and phenotypic level, which have changed paradigms in infectious respiratory diseases in terms of why, how and where diagnostics are performed. The aim of this review is to discuss advances in diagnostic tools that impact clinical decision-making, surveillance and follow-up of RTIs and tuberculosis. If properly harnessed, recent advances in diagnostic technologies, including omics and digital transformation, emerge as an unprecedented opportunity to tackle ongoing and future epidemics while handling antimicrobial resistance from a One Health perspective.

Implications of Using Host Response-Based Molecular Diagnostics on the Management of Bacterial and Viral Infections: A Review

Host-based diagnostics are a rapidly evolving field that may serve as an alternative to traditional pathogen-based diagnostics for infectious diseases. Understanding the exact mechanisms underlying a host-immune response and deriving specific host-response signatures, biomarkers and gene transcripts will potentially achieve improved diagnostics that will ultimately translate to better patient outcomes. Several studies have focused on novel techniques and assays focused on immunodiagnostics. In this review, we will highlight recent publications on the current use of host-based diagnostics alone or in combination with traditional microbiological assays and their potential future implications on the diagnosis and prognostic accuracy for the patient with infectious complications. Finally, we will address the cost-effectiveness implications from a healthcare and public health perspective.

Novel point-of-care biomarker combination tests to differentiate acute bacterial from viral respiratory tract infections to guide antibiotic prescribing: a systematic review

BACKGROUND

Acute respiratory tract infections (RTIs) are the most common reason to seek medical care, with many patients receiving inappropriate antibiotics. Novel testing approaches to identify aetiology at the point-of-care are required to accurately guide antibiotic treatment.

OBJECTIVE

To assess the diagnostic accuracy of biomarker combinations to rapidly differentiate between acute bacterial or viral RTI aetiology.

DATA SOURCES

MEDLINE, Embase and Web of Science databases were searched to February 2021.

STUDY ELIGIBILITY CRITERIA

Diagnostic accuracy studies comparing accuracy of point-of-care and rapid diagnostic tests in primary or secondary care, consisting of biomarker combinations, to identify bacterial or viral aetiology of RTI.

METHODS

Risk of bias was assessed using the QUADAS-2 tool. Sensitivity and specificity of tests reported by more than one study were meta-analysed using a random effects model.

RESULTS

Twenty observational studies (3514 patients) were identified. Eighteen were judged at high risk of bias. For bacterial aetiologies, sensitivity ranged from 61% to 100% and specificity from 18% to 96%. For viral aetiologies, sensitivity ranged from 59% to 97% and specificity from 74% to 100%. Studies evaluating two commercial tests were meta-analysed. For ImmunoXpert, the summary sensitivity and specificity were 85% (95% CI 75%-91%, k = 4) and 86% (95% CI 73%-93%, k = 4) for bacterial infections, and 90% (95% CI 79%-96%, k = 3) and 92% (95% CI 83%-96%, k = 3) for viral infections, respectively. FebriDx had pooled sensitivity and specificity of 84% (95% CI 75%-90%, k = 4) and 93% (95% CI 90%-95%, k = 4) for bacterial infections, and 87% (95% CI 72%-95%; k = 4) and 82% (95% CI 66%-86%, k = 4) for viral infections, respectively.

CONCLUSION

Combinations of biomarkers show potential clinical utility in discriminating the aetiology of RTIs. However, the limitations in the evidence base, due to a high proportion of studies with high risk of bias, preclude firm conclusions. Future research should be in primary care and evaluate patient outcomes and cost-effectiveness with experimental study designs.

CLINICAL TRIAL

PROSPERO registration number: CRD42020178973.

The preventive strategy for pandemics in the elderly is to collect in advance samples & data to counteract chronic inflammation (inflammaging)

Fighting the current COVID-19 pandemic, we must not forget to prepare for the next. Since elderly and frail people are at high risk, we wish to predict their vulnerability, and intervene if possible. For example, it would take little effort to take additional swabs or dried blood spots. Such minimally-invasive sampling, exemplified here during screening for potential COVID-19 infection, can yield the data to discover biomarkers to better handle this and the next respiratory disease pandemic. Longitudinal outcome data can then be combined with other epidemics and old-age health data, to discover the best biomarkers to predict (i) coping with infection & inflammation and thus hospitalization or intensive care, (ii) long-term health challenges, e.g. deterioration of lung function after intensive care, and (iii) treatment & vaccination response. Further, there are universal triggers of old-age morbidity & mortality, and the elimination of senescent cells improved health in pilot studies in idiopathic lung fibrosis & osteoarthritis patients alike. Biomarker studies are needed to test the hypothesis that resilience of the elderly during a pandemic can be improved by countering chronic inflammation and/or removing senescent cells. Our review suggests that more samples should be taken and saved systematically, following minimum standards, and data be made available, to maximize healthspan & minimize frailty, leading to savings in health care, gains in quality of life, and preparing us better for the next pandemic, all at the same time.

Validation of a host response test to distinguish bacterial and viral respiratory infection

BACKGROUND

Distinguishing bacterial and viral respiratory infections is challenging. Novel diagnostics based on differential host gene expression patterns are promising but have not been translated to a clinical platform nor extensively tested. Here, we validate a microarray-derived host response signature and explore performance in microbiology-negative and coinfection cases.

METHODS

Subjects with acute respiratory illness were enrolled in participating emergency departments. Reference standard was an adjudicated diagnosis of bacterial infection, viral infection, both, or neither. An 87-transcript signature for distinguishing bacterial, viral, and noninfectious illness was measured from peripheral blood using RT-PCR. Performance characteristics were evaluated in subjects with confirmed bacterial, viral, or noninfectious illness. Subjects with bacterial-viral coinfection and microbiologically-negative suspected bacterial infection were also evaluated. Performance was compared to procalcitonin.

FINDINGS

151 subjects with microbiologically confirmed, single-etiology illness were tested, yielding AUROCs 0•85-0•89 for bacterial, viral, and noninfectious illness. Accuracy was similar to procalcitonin (88% vs 83%, p = 0•23) for bacterial vs. non-bacterial infection. Whereas procalcitonin cannot distinguish viral from non-infectious illness, the RT-PCR test had 81% accuracy in making this determination. Bacterial-viral coinfection was subdivided. Among 19 subjects with bacterial superinfection, the RT-PCR test identified 95% as bacterial, compared to 68% with procalcitonin (p = 0•13). Among 12 subjects with bacterial infection superimposed on chronic viral infection, the RT-PCR test identified 83% as bacterial, identical to procalcitonin. 39 subjects had suspected bacterial infection; the RT-PCR test identified bacterial infection more frequently than procalcitonin (82% vs 64%, p = 0•02).

INTERPRETATION

The RT-PCR test offered similar diagnostic performance to procalcitonin in some subgroups but offered better discrimination in others such as viral vs. non-infectious illness and bacterial/viral coinfection. Gene expression-based tests could impact decision-making for acute respiratory illness as well as a growing number of other infectious and non-infectious diseases.