The current epidemiology and clinical decisions surrounding acute respiratory infections

Antimicrobial resistance prediction by clinical metagenomics in pediatric severe pneumonia patients

BACKGROUND

Antimicrobial resistance (AMR) is a major threat to children's health, particularly in respiratory infections. Accurate identification of pathogens and AMR is crucial for targeted antibiotic treatment. Metagenomic next-generation sequencing (mNGS) shows promise in directly detecting microorganisms and resistance genes in clinical samples. However, the accuracy of AMR prediction through mNGS testing needs further investigation for practical clinical decision-making.

METHODS

We aimed to evaluate the performance of mNGS in predicting AMR for severe pneumonia in pediatric patients. We conducted a retrospective analysis at a tertiary hospital from May 2022 to May 2023. Simultaneous mNGS and culture were performed on bronchoalveolar lavage fluid samples obtained from pediatric patients with severe pneumonia. By comparing the results of mNGS detection of microorganisms and antibiotic resistance genes with those of culture, sensitivity, specificity, positive predictive value, and negative predictive value were calculated.

RESULTS

mNGS detected bacterial in 71.7% cases (86/120), significantly higher than culture (58/120, 48.3%). Compared to culture, mNGS demonstrated a sensitivity of 96.6% and a specificity of 51.6% in detecting pathogenic microorganisms. Phenotypic susceptibility testing (PST) of 19 antibiotics revealed significant variations in antibiotics resistance rates among different bacteria. Sensitivity prediction of mNGS for carbapenem resistance was higher than penicillins and cephalosporin (67.74% vs. 28.57%, 46.15%), while specificity showed no significant difference (85.71%, 75.00%, 75.00%). mNGS also showed a high sensitivity of 94.74% in predicting carbapenem resistance in Acinetobacter baumannii.

CONCLUSIONS

mNGS exhibits variable predictive performance among different pathogens and antibiotics, indicating its potential as a supplementary tool to conventional PST. However, mNGS currently cannot replace conventional PST.

Development and evaluation of a multiplex digital PCR method for sensitive and accurate detection of respiratory pathogens in children

The emergence of multiplex digital polymerase chain reaction (dPCR) and other detection technologies for respiratory pathogens in recent years has facilitated greater understanding of respiratory virus epidemics. In this study, a multiplex dPCR method was developed and evaluated as a means of detecting five respiratory pathogens in children with acute lower respiratory tract infection (ALRTI). With 139 nasopharyngeal swabs collected from children with ALRTI, pathogens were detected using dPCR and quantitative real-time PCR (qPCR) methods. Of those specimens, dPCR detected 86 positive cases, while qPCR identified 84. Moreover, dPCR exhibited higher sensitivity than qPCR, and displayed no cross-reactivity with common respiratory pathogens. These findings suggest that dPCR-based method could become one of the most promising options for acute respiratory pathogen detection.

Effects of Contagious Respiratory Pathogens on Breath Biomarkers

Due to their immediate exhalation after generation at the cellular/microbiome levels, exhaled volatile organic compounds (VOCs) may provide real-time information on pathophysiological mechanisms and the host response to infection. In recent years, the metabolic profiling of the most frequent respiratory infections has gained interest as it holds potential for the early, non-invasive detection of pathogens and the monitoring of disease progression and the response to therapy. Using previously unpublished data, randomly selected individuals from a COVID-19 test center were included in the study. Based on multiplex PCR results (non-SARS-CoV-2 respiratory pathogens), the breath profiles of 479 subjects with the presence or absence of flu-like symptoms were obtained using proton-transfer-reaction time-of-flight mass spectrometry. Among 223 individuals, one respiratory pathogen was detected in 171 cases, and more than one pathogen in 52 cases. A total of 256 subjects had negative PCR test results and had no symptoms. The exhaled VOC profiles were affected by the presence of Haemophilus influenzae, Streptococcus pneumoniae, and Rhinovirus. The endogenous ketone, short-chain fatty acid, organosulfur, aldehyde, and terpene concentrations changed, but only a few compounds exhibited concentration changes above inter-individual physiological variations. Based on the VOC origins, the observed concentration changes may be attributed to oxidative stress and antioxidative defense, energy metabolism, systemic microbial immune homeostasis, and inflammation. In contrast to previous studies with pre-selected patient groups, the results of this study demonstrate the broad inter-individual variations in VOC profiles in real-life screening conditions. As no unique infection markers exist, only concentration changes clearly above the mentioned variations can be regarded as indicative of infection or colonization.

Metagenomic sequencing of post-mortem tissue samples for the identification of pathogens associated with neonatal deaths

Postmortem minimally invasive tissue sampling together with the detailed review of clinical records has been shown to be highly successful in determining the cause of neonatal deaths. However, conventional tests including traditional culture methods and nucleic acid amplification tests have periodically proven to be insufficient to detect the causative agent in the infectious deaths. In this study, metagenomic next generation sequencing was used to explore for putative pathogens associated with neonatal deaths in post-mortem blood and lung tissue samples, in Soweto, South Africa. Here we show that the metagenomic sequencing results corroborate the findings using conventional methods of culture and nucleic acid amplifications tests on post-mortem samples in detecting the pathogens attributed in the causal pathway of death in 90% (18/20) of the decedents. Furthermore, metagenomic sequencing detected a putative pathogen, including Acinetobacter baumannii, Klebsiella pneumoniae, Escherichia coli, and Serratia marcescens, in a further nine of 11 (81%) cases where no causative pathogen was identified. The antimicrobial susceptibility profile was also determined by the metagenomic sequencing for all pathogens with numerous multi drug resistant organism identified. In conclusion, metagenomic sequencing is able to successfully identify pathogens contributing to infection associated deaths on postmortem blood and tissue samples.

Clinical Evaluation of Metagenomic Next-Generation Sequencing and Identification of Risk Factors in Patients with Severe Community-Acquired Pneumonia

Purpose

Severe community-acquired pneumonia (SCAP) is the leading cause of death among patients with infectious diseases worldwide. This study aimed to evaluate the effectiveness of metagenomic next-generation sequencing (mNGS) through detecting pathogens in bronchoalveolar lavage fluid (BALF) and identifying risk factors for recovery in SCAP patients.

Patients and Methods

This prospective study recruited 158 SCAP patients admitted to respiratory intensive care unit that were randomly divided into control and study groups, with receiving conventional tests and the same conventional tests plus mNGS, respectively. The diagnostic efficiency of mNGS was evaluated by comparing with conventional tests. Furthermore, univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for recovery in SCAP patients, and a nomogram prediction model was established based on these factors.

Results

Within the study group, the pathogen detection rate was significantly higher with mNGS than that with conventional tests (84.81% vs 45.57%, P < 0.001), with a positive coincidence rate of 94.44%. Acinetobacter baumannii (21.52%, 17/79), Candida albicans (17.72%, 14/79), and Klebsiella pneumonia (15.19%, 12/79) were the top three common pathogens detected by mNGS. Of note, the improvement rate of patients in the study group was significantly higher than that in the control group. The further analysis revealed that the increased levels of interleukin-6, blood urea nitrogen, procalcitonin, the longer length of hospital stay, and bacterial infection were independent risk factors for recovery of SCAP patients, while mNGS detection status was a protective factor. The predictive model showed a good performance for the modeling and validation sets.

Conclusion

Early mNGS exhibited a superior diagnostic efficiency to conventional tests in SCAP patients, which can reduce the risk of death in SCAP patients. Moreover, the clinical factors could also be used for the management and prognosis prediction of SCAP patients.

The potential clinical value of pairing procalcitonin and lung ultrasonography to guide antibiotic therapy in patients with community-acquired pneumonia: a narrative review

INTRODUCTION

Lower respiratory tract infections (LRTIs) are among the most frequent infections and are prone to inappropriate antibiotic treatments. This results from a limited accuracy of diagnostic tools in identifying bacterial pneumonia. Lung ultrasound (LUS) has excellent sensitivity and specificity in diagnosing pneumonia. Additionally, elevated procalcitonin (PCT) levels correlate with an increased likelihood of bacterial infection. LUS and PCT appear to be complementary in identifying patients with bacterial pneumonia who are likely to benefit from antibiotics.

AREAS COVERED

This narrative review aims to summarize the current evidence for LUS to diagnose pneumonia, for PCT to guide antibiotic therapy and the clinical value of pairing both tools.

EXPERT OPINION

LUS has excellent diagnostic accuracy for pneumonia in different settings, regardless of the examiner's experience. PCT guidance safely reduces antibiotic prescription in LRTIs. The combination of both tools has demonstrated an enhanced accuracy in the diagnosis of pneumonia, including CAP in the ED and VAP in the ICU, but randomized controlled studies need to validate the clinical impact of a combined approach.

Integrated host/microbe metagenomics enables accurate lower respiratory tract infection diagnosis in critically ill children

BACKGROUNDLower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging because noninfectious respiratory illnesses appear clinically similar and because existing microbiologic tests are often falsely negative or detect incidentally carried microbes, resulting in antimicrobial overuse and adverse outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and treatment remains unclear.METHODSWe used tracheal aspirate RNA-Seq to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n = 117) or of noninfectious respiratory failure (n = 50). We then developed a classifier that integrates the host LRTI probability, abundance of respiratory viruses, and dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm.RESULTSThe host classifier achieved a median AUC of 0.967 by cross-validation, driven by activation markers of T cells, alveolar macrophages, and the interferon response. The integrated classifier achieved a median AUC of 0.986 and increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n = 94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those.CONCLUSIONLower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.FUNDINGSupport for this study was provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Heart, Lung, and Blood Institute (UG1HD083171, 1R01HL124103, UG1HD049983, UG01HD049934, UG1HD083170, UG1HD050096, UG1HD63108, UG1HD083116, UG1HD083166, UG1HD049981, K23HL138461, and 5R01HL155418) as well as by the Chan Zuckerberg Biohub.

Metagenomic prediction of antimicrobial resistance in critically ill patients with lower respiratory tract infections

Background

Antimicrobial resistance (AMR) is rising at an alarming rate and complicating the management of infectious diseases including lower respiratory tract infections (LRTI). Metagenomic next-generation sequencing (mNGS) is a recently established method for culture-independent LRTI diagnosis, but its utility for predicting AMR has remained unclear. We aimed to assess the performance of mNGS for AMR prediction in bacterial LRTI and demonstrate proof of concept for epidemiological AMR surveillance and rapid AMR gene detection using Cas9 enrichment and nanopore sequencing.

Methods

We studied 88 patients with acute respiratory failure between 07/2013 and 9/2018, enrolled through a previous observational study of LRTI. Inclusion criteria were age ≥ 18, need for mechanical ventilation, and respiratory specimen collection within 72 h of intubation. Exclusion criteria were decline of study participation, unclear LRTI status, or no matched RNA and DNA mNGS data from a respiratory specimen. Patients with LRTI were identified by clinical adjudication. mNGS was performed on lower respiratory tract specimens. The primary outcome was mNGS performance for predicting phenotypic antimicrobial susceptibility and was assessed in patients with LRTI from culture-confirmed bacterial pathogens with clinical antimicrobial susceptibility testing (n = 27 patients, n = 32 pathogens). Secondary outcomes included the association between hospital exposure and AMR gene burden in the respiratory microbiome (n = 88 patients), and AMR gene detection using Cas9 targeted enrichment and nanopore sequencing (n = 10 patients).

Results

Compared to clinical antimicrobial susceptibility testing, the performance of respiratory mNGS for predicting AMR varied by pathogen, antimicrobial, and nucleic acid type sequenced. For gram-positive bacteria, a combination of RNA + DNA mNGS achieved a sensitivity of 70% (95% confidence interval (CI) 47–87%) and specificity of 95% (CI 85–99%). For gram-negative bacteria, sensitivity was 100% (CI 87–100%) and specificity 64% (CI 48–78%). Patients with hospital-onset LRTI had a greater AMR gene burden in their respiratory microbiome versus those with community-onset LRTI (p = 0.00030), or those without LRTI (p = 0.0024). We found that Cas9 targeted sequencing could enrich for low abundance AMR genes by > 2500-fold and enabled their rapid detection using a nanopore platform.

Conclusions

mNGS has utility for the detection and surveillance of resistant bacterial LRTI pathogens.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01072-4.

Opportunities for Improving Antimicrobial Stewardship: Findings From a Prospective, Multi-Center Study in Three Low- or Middle-Income Countries

Background

To develop effective antimicrobial stewardship programs (ASPs) for low- and middle-income countries (LMICs), it is important to identify key targets for improving antimicrobial use. We sought to systematically describe the prevalence and patterns of antimicrobial use in three LMIC hospitals.

Methods

Consecutive patients admitted to the adult medical wards in three tertiary care hospitals in Tanzania, Kenya, and Sri Lanka were enrolled in 2018-2019. The medical record was reviewed for clinical information including type and duration of antimicrobials prescribed, indications for antimicrobial use, and microbiologic testing ordered.

Results

A total of 3,149 patients were enrolled during the study period: 1,103 from Tanzania, 750 from Kenya, and 1,296 from Sri Lanka. The majority of patients were male (1,783, 56.6% overall) with a median age of 55 years (IQR 38-68). Of enrolled patients, 1,573 (50.0%) received antimicrobials during their hospital stay: 35.4% in Tanzania, 56.5% in Kenya, and 58.6% in Sri Lanka. At each site, the most common indication for antimicrobial use was lower respiratory tract infection (LRTI; 40.2%). However, 61.0% received antimicrobials for LRTI in the absence of LRTI signs on chest radiography. Among patients receiving antimicrobials, tools to guide antimicrobial use were under-utilized: microbiologic cultures in 12.0% and microbiology consultation in 6.5%.

Conclusion

Antimicrobials were used in a substantial proportion of patients at tertiary care hospitals across three LMIC sites. Future ASP efforts should include improving LRTI diagnosis and treatment, developing antibiograms to direct empiric antimicrobial use, and increasing use of microbiologic tests.

Viral burden and diversity in acute respiratory tract infections in hospitalized children in wet and dry zones of Sri Lanka

The present study was done to identify the viral diversity, seasonality and burden associated with childhood acute respiratory tract infection (ARTI) in Sri Lanka. Nasopharyngeal aspirates (NPA) of hospitalized children (1 month-5 years) with ARTI were collected in 2 centers (wet and dry zones) from March 2013 to August 2014. Respiratory viral antigen detection by immunofluorescence assay (IFA) was used to identify the infecting viruses. IFA negative 100 NPA samples were tested for human metapeumovirus (hMPV), human bocavirus and corona viruses by polymerase chain reaction. Of the 443 and 418 NPAs, 37.2% and 39.4% were positive for any of the 8 different respiratory viruses tested from two centers studied. Viral co-infection was detected with respiratory syncytial virus (RSV) in both centers. Peak viral detection was noted in the wet zone from May-July 2013 and 2014 and in the dry zone from December-January 2014 suggesting a local seasonality for viral ARTI. RSV showed a clear seasonality with a direct correlation of monthly RSV infections with rainy days in the wet zone and an inverse correlation with temperature in both centers. The case fatality rate was 2.7% for RSV associated ARTI. The overall disability adjusted life years was 335.9 and for RSV associated ARTI it was 241.8. RSV was the commonly detected respiratory virus with an annual seasonality and distribution in rainy seasons in the dry and wet zones of Sri Lanka. Identifying the virus and seasonality will contribute to employ preventive measures and reduce the empirical use of antibiotics in resource limited settings.

Comparing the Diagnostic Accuracy of Clinician Judgment to a Novel Host Response Diagnostic for Acute Respiratory Illness

Background

Difficulty discriminating bacterial from viral infections drives antibacterial misuse. Host gene expression tests discriminate bacterial and viral etiologies, but their clinical utility has not been evaluated.

Methods

Host gene expression and procalcitonin levels were measured in 582 emergency department participants with suspected infection. We also recorded clinician diagnosis and clinician-recommended treatment. These 4 diagnostic strategies were compared with clinical adjudication as the reference. To estimate the clinical impact of host gene expression, we calculated the change in overall Net Benefit (∆NB; the difference in Net Benefit comparing 1 diagnostic strategy with a reference) across a range of prevalence estimates while factoring in the clinical significance of false-positive and -negative errors.

Results

Gene expression correctly classified bacterial, viral, or noninfectious illness in 74.1% of subjects, similar to the other strategies. Clinical diagnosis and clinician-recommended treatment revealed a bias toward overdiagnosis of bacterial infection resulting in high sensitivity (92.6% and 94.5%, respectively) but poor specificity (67.2% and 58.8%, respectively), resulting in a 33.3% rate of inappropriate antibacterial use. Gene expression offered a more balanced sensitivity (79.0%) and specificity (80.7%), which corresponded to a statistically significant improvement in average weighted accuracy (79.9% vs 71.5% for procalcitonin and 76.3% for clinician-recommended treatment; P<.0001 for both). Consequently, host gene expression had greater Net Benefit in diagnosing bacterial infection than clinician-recommended treatment (∆NB=6.4%) and procalcitonin (∆NB=17.4%).

Conclusions

Host gene expression-based tests to distinguish bacterial and viral infection can facilitate appropriate treatment, improving patient outcomes and mitigating the antibacterial resistance crisis.

Clinical Impact of Metagenomic Next-Generation Sequencing of Bronchoalveolar Lavage in the Diagnosis and Management of Pneumonia: A Multicenter Prospective Observational Study

Rapid and accurate pathogen identification is necessary for appropriate treatment of pneumonia. Here, we describe the use of shotgun metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage for pathogen identification in pneumonia in a large-scale multicenter prospective study with 159 patients enrolled. We compared the results of mNGS with standard methods including culture, staining, and targeted PCR, and evaluated the clinical impact of mNGS. A positive impact was defined by a definitive diagnosis made using the mNGS results, or change of management because of the mNGS results, leading to a favorable clinical outcome. Overall, mNGS identified more organisms than standard methods (117 versus 72), detected 17 pathogens that consistently were missed in all cases by standard methods, and had an overall positive clinical impact in 40.3% (64 of 159) of cases. mNGS was especially useful in identification of fastidious and atypical organisms causing pneumonia, contributing to detection of definitive pathogens in 45 (28.3%) cases in which standard results were either negative or insufficient. mNGS also helped reassure antibiotic de-escalation in 19 (11.9%) cases. Overall, mNGS led to a change of treatment in 59 (37.1%) cases, including antibiotic de-escalation in 40 (25.2%) cases. This study showed the significant value of mNGS of bronchoalveolar lavage for improving the diagnosis of pneumonia and contributing to better patient care.

Epidemiological and clinical characteristics of respiratory viruses in 4403 pediatric patients from multiple hospitals in Guangdong, China

Background

Acute respiratory infections (ARI) cause considerable morbidity and mortality worldwide, especially in children. Unfortunately, there are limited multi-center data on common viral respiratory infections in south China.

Methods

A total of 4403 nasal swabs were collected from children in 10 cities in Guangdong, China in 2019. Seven respiratory viruses, influenza A virus (IFA), influenza B virus (IFB), respiratory syncytial virus (RSV), adenoviruses (ADV) and parainfluenza virus types 1–3 (PIV1, PIV2 and PIV3), were detected by direct immunofluorescence antibody assay. The personal information and clinical characteristics were recorded and analyzed.

Results

The results showed that at least one virus was detected in 1099 (24.96 %) samples. The detection rates of RSV, IFA, ADV, PIV3, PIV1 and PIV2 were 7.13 % (314/4403), 5.31 % (234/4403), 4.02 % (177/4403), 3.04 % (134/4403), 1.70 % (75/4403) and 1.16 % (51/4403), respectively. The detection rate of RSV was highest in 0–6-month-old children at 18.18 % (106/583), while the detection rate of IFA was highest in 12–18-year-old children at 20.48 % (17/83). The total detection rates in winter and spring were 35.67 % (219/614) and 34.56 % (403/1166), higher than those in summer, 17.41 % (284/1631), and autumn, 19.46 % (193/992).

Conclusions

RSV and IFA were the main respiratory viruses in children. With increasing age the detection rate of RSV decreased in children, but the trends for the detection rates of IFA and IFB were the opposite. This study provided the viral etiology and epidemiology of pediatric patients with ARI in Guangdong, China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-021-02759-0.

Ambient air pollution and risk of respiratory infection among adults: evidence from the multiethnic study of atherosclerosis (MESA)

BACKGROUND

Air pollution may affect the risk of respiratory infection, though research has focused on uncommon infections or infections in children. Whether ambient air pollutants increase the risk of common acute respiratory infections among adults is uncertain, yet this may help understand whether pollutants influence spread of pandemic respiratory infections like COVID-19.

OBJECTIVE

To estimate the association between ambient air pollutant exposures and respiratory infections in adults.

METHODS

During five study examinations over 12 years, 6536 participants in the multiethnic study of atherosclerosis (MESA) reported upper respiratory tract infections, bronchitis, pneumonia or febrile illness in the preceding 2 weeks. Using a validated spatiotemporal model, we estimated residential concentrations of ambient PM2.5, NOx and NO2 for the 2-6 weeks (short-term) and year (long-term) prior to each examination.

RESULTS

In this population aged 44-84 years at baseline, 10%-32% of participants reported a recent respiratory infection, depending on month of examination and study region. PM2.5, NOx and NO2 concentrations over the prior 2-6 weeks were associated with increased reporting of recent respiratory infection, with risk ratios (95% CIs) of 1.04 (1.00 to 1.09), 1.15 (1.10 to 1.20) and 1.21 (1.10 to 1.33), respectively, per increase from 25th to 75th percentile in residential pollutant concentration.

CONCLUSION

Higher short-term exposure to PM2.5 and traffic-related pollutants are associated with increased risk of symptomatic acute respiratory infections among adults. These findings may provide an insight into the epidemiology of COVID-19.

Optimal specimen type for accurate diagnosis of infectious peripheral pulmonary lesions by mNGS

Background

Reports on the application of metagenomic next-generation sequencing (mNGS) to the diagnosis of peripheral pulmonary lesions (PPLs) are scarce. There have been no studies investigating the optimal specimen type for mNGS.

Methods

We used mNGS to detect pathogens in matched transbronchial lung biopsy (TBLB), bronchoalveolar lavage fluid (BALF), and bronchial needle brushing (BB) specimens from 39 patients suspected of having infectious PPLs. We explored differences in microbial composition and diagnostic accuracy of mNGS for the 3 specimen types.

Results

mNGS was more sensitive than conventional culture for detection of bacteria and fungi in TBLB, BALF, and BB specimens, with no difference in the sensitivity of mNGS across the different specimen types. mNGS showed higher sensitivity for fungi or uncategorized pulmonary pathogens in TBLB+BALF+BB compared to TBLB but not BALF or BB specimens. There were no significant differences between the 3 specimen types in the relative abundance of pathogens, or between TBLB and BB specimens in the relative abundance of 6 common lower respiratory tract commensals.

Conclusions

mNGS has a higher sensitivity than the conventional culture method for detecting pathogens in TBLB, BALF, or BB specimens. mNGS of BB samples is a less invasive alternative to TBLB for the diagnosis of infectious PPLs.

Previously Derived Host Gene Expression Classifiers Identify Bacterial and Viral Etiologies of Acute Febrile Respiratory Illness in a South Asian Population

Background

Pathogen-based diagnostics for acute respiratory infection (ARI) have limited ability to detect etiology of illness. We previously showed that peripheral blood-based host gene expression classifiers accurately identify bacterial and viral ARI in cohorts of European and African descent. We determined classifier performance in a South Asian cohort.

Methods

Patients ≥15 years with fever and respiratory symptoms were enrolled in Sri Lanka. Comprehensive pathogen-based testing was performed. Peripheral blood ribonucleic acid was sequenced and previously developed signatures were applied: a pan-viral classifier (viral vs nonviral) and an ARI classifier (bacterial vs viral vs noninfectious).

Results

Ribonucleic acid sequencing was performed in 79 subjects: 58 viral infections (36 influenza, 22 dengue) and 21 bacterial infections (10 leptospirosis, 11 scrub typhus). The pan-viral classifier had an overall classification accuracy of 95%. The ARI classifier had an overall classification accuracy of 94%, with sensitivity and specificity of 91% and 95%, respectively, for bacterial infection. The sensitivity and specificity of C-reactive protein (>10 mg/L) and procalcitonin (>0.25 ng/mL) for bacterial infection were 100% and 34%, and 100% and 41%, respectively.

Conclusions

Previously derived gene expression classifiers had high predictive accuracy at distinguishing viral and bacterial infection in South Asian patients with ARI caused by typical and atypical pathogens.

Sputum microbiome profiling in COPD: beyond singular pathogen detection

Culture-independent microbial sequencing techniques have revealed that the respiratory tract harbours a complex microbiome not detectable by conventional culturing methods. The contribution of the microbiome to chronic obstructive pulmonary disease (COPD) pathobiology and the potential for microbiome-based clinical biomarkers in COPD are still in the early phases of investigation. Sputum is an easily obtainable sample and has provided a wealth of information on COPD pathobiology, and thus has been a preferred sample type for microbiome studies. Although the sputum microbiome likely reflects the respiratory microbiome only in part, there is increasing evidence that microbial community structure and diversity are associated with disease severity and clinical outcomes, both in stable COPD and during the exacerbations. Current evidence has been limited to mainly cross-sectional studies using 16S rRNA gene sequencing, attempting to answer the question 'who is there?' Longitudinal studies using standardised protocols are needed to answer outstanding questions including differences between sputum sampling techniques. Further, with advancing technologies, microbiome studies are shifting beyond the examination of the 16S rRNA gene, to include whole metagenome and metatranscriptome sequencing, as well as metabolome characterisation. Despite being technically more challenging, whole-genome profiling and metabolomics can address the questions 'what can they do?' and 'what are they doing?' This review provides an overview of the basic principles of high-throughput microbiome sequencing techniques, current literature on sputum microbiome profiling in COPD, and a discussion of the associated limitations and future perspectives.

Next-generation sequencing diagnosis of severe pneumonia from fulminant psittacosis with multiple organ failure: a case report and literature review

This study includes a retrospective analysis of the diagnosis and treatment of a case of severe pneumonia from fulminant psittacosis with multiple organ failure. Next-generation sequencing (NGS) of the pathogen was conducted. The purpose of this study was to summarize the clinical, laboratory, and imaging characteristics of the case and to improve understanding of the value of NGS in the diagnosis of severe community-acquired pneumonia (CAP). Fulminant psittacosis can be manifested as severe pneumonia with rapid progression, acute respiratory distress syndrome, sepsis, and multiple organ failure. Imaging shows unilateral lung consolidation, which is difficult to differentiate from CAP caused by common pathogens. The NGS technology can early detect rare pathogens, thus reducing unnecessary use of antibiotics and shortening the course of the disease.

FLASH: a next-generation CRISPR diagnostic for multiplexed detection of antimicrobial resistance sequences

The growing prevalence of deadly microbes with resistance to previously life-saving drug therapies is a dire threat to human health. Detection of low abundance pathogen sequences remains a challenge for metagenomic Next Generation Sequencing (NGS). We introduce FLASH (Finding Low Abundance Sequences by Hybridization), a next-generation CRISPR/Cas9 diagnostic method that takes advantage of the efficiency, specificity and flexibility of Cas9 to enrich for a programmed set of sequences. FLASH-NGS achieves up to 5 orders of magnitude of enrichment and sub-attomolar gene detection with minimal background. We provide an open-source software tool (FLASHit) for guide RNA design. Here we applied it to detection of antimicrobial resistance genes in respiratory fluid and dried blood spots, but FLASH-NGS is applicable to all areas that rely on multiplex PCR.

Infectious particle identity determines dissemination and disease outcome for the inhaled human fungal pathogen Cryptococcus

The majority of invasive human fungal pathogens gain access to their human hosts via the inhalation of spores from the environment into the lung, but relatively little is known about this infectious process. Among human fungal pathogens the most frequent cause of inhaled fatal fungal disease is Cryptococcus, which can disseminate from the lungs to other tissues, including the brain, where it causes meningoencephalitis. To determine the mechanisms by which distinct infectious particles of Cryptococcus cause disseminated disease, we evaluated two developmental cell types (spores and yeast) in mouse models of infection. We discovered that while both yeast and spores from several strains cause fatal disease, there was a consistently higher fungal burden in the brains of spore-infected mice. To determine the basis for this difference, we compared the pathogenesis of avirulent yeast strains with their spore progeny derived from sexual crosses. Strikingly, we discovered that spores produced by avirulent yeast caused uniformly fatal disease in the murine inhalation model of infection. We determined that this difference in outcome is associated with the preferential dissemination of spores to the lymph system. Specifically, mice infected with spores harbored Cryptococcus in their lung draining lymph nodes as early as one day after infection, whereas mice infected with yeast did not. Furthermore, phagocyte depletion experiments revealed this dissemination to the lymph nodes to be dependent on CD11c+ phagocytes, indicating a critical role for host immune cells in preferential spore trafficking. Taken together, these data support a model in which spores capitalize on phagocytosis by immune cells to escape the lung and gain access to other tissues, such as the central nervous system, to cause fatal disease. These previously unrealized insights into early interactions between pathogenic fungal spores and lung phagocytes provide new opportunities for understanding cryptococcosis and other spore-mediated fungal diseases.

Use of clinical algorithms and rapid influenza testing to manage influenza-like illness: a cost-effectiveness analysis in Sri Lanka

Background

Acute respiratory infections are a common reason for antibiotic overuse. We previously showed that providing Sri Lankan clinicians with positive rapid influenza test results was associated with a reduction in antibiotic prescriptions. The economic impact of influenza diagnostic strategies is unknown.

Methods

We estimated the incremental cost per antibiotic prescription avoided with three diagnostic strategies versus standard care when managing Sri Lankan outpatients with influenza-like illness (ILI): (1) influenza clinical prediction tool, (2) targeted rapid influenza testing and (3) universal rapid influenza testing. We compared findings with literature-based estimates of the cost of antimicrobial resistance attributable to each antibiotic prescription.

Results

Standard care was less expensive than other strategies across all parameter values in one-way sensitivity analyses. The incremental cost per antibiotic prescription avoided with clinical prediction versus standard care was US$3.0, which was lower than the base-case estimate of the cost of antimicrobial resistance per ILI antibiotic prescription (US$12.5). The incremental cost per antibiotic prescription avoided with targeted testing and universal testing versus standard care were both higher than the base-case cost of antimicrobial resistance per ILI antibiotic prescription: US$49.1 and US$138.3, respectively. To obtain a cost-effectiveness ratio lower than US$12.5 with targeted testing versus standard care, the test price must be <US$2.6. At a higher threshold of US$28.7, the test price must be <US$7.7.

Conclusion

Clinical prediction tools and targeted rapid influenza testing may be cost-saving strategies in Sri Lanka when accounting for the societal cost of antimicrobial resistance.

Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults

Lower respiratory tract infections (LRTIs) are the leading cause of infectious disease-related deaths worldwide yet remain challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, noninfectious respiratory syndromes that resemble LRTIs further complicate diagnosis and confound targeted treatment. To address this, we developed a metagenomic sequencing-based approach that simultaneously interrogates three core elements of acute airway infections: the pathogen, airway microbiome, and host response. We studied this approach in a prospective cohort of critically ill patients with acute respiratory failure and found that combining pathogen, microbiome, and host gene expression metrics achieved accurate LRTI diagnosis and identified etiologic pathogens in patients with clinically identified infections but otherwise negative testing.

Lower respiratory tract infections (LRTIs) lead to more deaths each year than any other infectious disease category. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests. In critically ill patients, noninfectious inflammatory syndromes resembling LRTIs further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the airway microbiome, and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed a rules-based model (RBM) and logistic regression model (LRM) in a derivation cohort of 20 patients with LRTIs or noninfectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with noninfectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an area under the receiver-operating curve (AUC) of 0.96 (95% CI, 0.86–1.00), the diversity metric with an AUC of 0.80 (95% CI, 0.63–0.98), and the host transcriptional classifier with an AUC of 0.88 (95% CI, 0.75–1.00). Combining these achieved a negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome, and host transcriptome may hold promise as a tool for LRTI diagnosis.

The human viral challenge model: accelerating the evaluation of respiratory antivirals, vaccines and novel diagnostics

The Human Viral Challenge (HVC) model has, for many decades, helped in the understanding of respiratory viruses and their role in disease pathogenesis. In a controlled setting using small numbers of volunteers removed from community exposure to other infections, this experimental model enables proof of concept work to be undertaken on novel therapeutics, including vaccines, immunomodulators and antivirals, as well as new diagnostics.Crucially, unlike conventional phase 1 studies, challenge studies include evaluable efficacy endpoints that then guide decisions on how to optimise subsequent field studies, as recommended by the FDA and thus licensing studies that follow. Such a strategy optimises the benefit of the studies and identifies possible threats early on, minimising the risk to subsequent volunteers but also maximising the benefit of scarce resources available to the research group investing in the research. Inspired by the principles of the 3Rs (Replacement, Reduction and Refinement) now commonly applied in the preclinical phase, HVC studies allow refinement and reduction of the subsequent development phase, accelerating progress towards further statistically powered phase 2b studies. The breadth of data generated from challenge studies allows for exploration of a wide range of variables and endpoints that can then be taken through to pivotal phase 3 studies.We describe the disease burden for acute respiratory viral infections for which current conventional development strategies have failed to produce therapeutics that meet clinical need. The Authors describe the HVC model's utility in increasing scientific understanding and in progressing promising therapeutics through development.The contribution of the model to the elucidation of the virus-host interaction, both regarding viral pathogenicity and the body's immunological response is discussed, along with its utility to assist in the development of novel diagnostics.Future applications of the model are also explored.

Procalcitonin to initiate or discontinue antibiotics in acute respiratory tract infections

BACKGROUND

Acute respiratory infections (ARIs) comprise of a large and heterogeneous group of infections including bacterial, viral, and other aetiologies. In recent years, procalcitonin (PCT), a blood marker for bacterial infections, has emerged as a promising tool to improve decisions about antibiotic therapy (PCT-guided antibiotic therapy). Several randomised controlled trials (RCTs) have demonstrated the feasibility of using procalcitonin for starting and stopping antibiotics in different patient populations with ARIs and different settings ranging from primary care settings to emergency departments, hospital wards, and intensive care units. However, the effect of using procalcitonin on clinical outcomes is unclear. This is an update of a Cochrane review and individual participant data meta-analysis first published in 2012 designed to look at the safety of PCT-guided antibiotic stewardship.

OBJECTIVES

The aim of this systematic review based on individual participant data was to assess the safety and efficacy of using procalcitonin for starting or stopping antibiotics over a large range of patients with varying severity of ARIs and from different clinical settings.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL), which contains the Cochrane Acute Respiratory Infections Group's Specialised Register, MEDLINE, and Embase, in February 2017, to identify suitable trials. We also searched ClinicalTrials.gov to identify ongoing trials in April 2017.

SELECTION CRITERIA

We included RCTs of adult participants with ARIs who received an antibiotic treatment either based on a procalcitonin algorithm (PCT-guided antibiotic stewardship algorithm) or usual care. We excluded trials if they focused exclusively on children or used procalcitonin for a purpose other than to guide initiation and duration of antibiotic treatment.

DATA COLLECTION AND ANALYSIS

Two teams of review authors independently evaluated the methodology and extracted data from primary studies. The primary endpoints were all-cause mortality and treatment failure at 30 days, for which definitions were harmonised among trials. Secondary endpoints were antibiotic use, antibiotic-related side effects, and length of hospital stay. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) using multivariable hierarchical logistic regression adjusted for age, gender, and clinical diagnosis using a fixed-effect model. The different trials were added as random-effects into the model. We conducted sensitivity analyses stratified by clinical setting and type of ARI. We also performed an aggregate data meta-analysis.

MAIN RESULTS

From 32 eligible RCTs including 18 new trials for this 2017 update, we obtained individual participant data from 26 trials including 6708 participants, which we included in the main individual participant data meta-analysis. We did not obtain individual participant data for four trials, and two trials did not include people with confirmed ARIs. According to GRADE, the quality of the evidence was high for the outcomes mortality and antibiotic exposure, and quality was moderate for the outcomes treatment failure and antibiotic-related side effects.Primary endpoints: there were 286 deaths in 3336 procalcitonin-guided participants (8.6%) compared to 336 in 3372 controls (10.0%), resulting in a significantly lower mortality associated with procalcitonin-guided therapy (adjusted OR 0.83, 95% CI 0.70 to 0.99, P = 0.037). We could not estimate mortality in primary care trials because only one death was reported in a control group participant. Treatment failure was not significantly lower in procalcitonin-guided participants (23.0% versus 24.9% in the control group, adjusted OR 0.90, 95% CI 0.80 to 1.01, P = 0.068). Results were similar among subgroups by clinical setting and type of respiratory infection, with no evidence for effect modification (P for interaction > 0.05). Secondary endpoints: procalcitonin guidance was associated with a 2.4-day reduction in antibiotic exposure (5.7 versus 8.1 days, 95% CI -2.71 to -2.15, P < 0.001) and lower risk of antibiotic-related side effects (16.3% versus 22.1%, adjusted OR 0.68, 95% CI 0.57 to 0.82, P < 0.001). Length of hospital stay and intensive care unit stay were similar in both groups. A sensitivity aggregate-data analysis based on all 32 eligible trials showed similar results.

AUTHORS' CONCLUSIONS

This updated meta-analysis of individual participant data from 12 countries shows that the use of procalcitonin to guide initiation and duration of antibiotic treatment results in lower risks of mortality, lower antibiotic consumption, and lower risk for antibiotic-related side effects. Results were similar for different clinical settings and types of ARIs, thus supporting the use of procalcitonin in the context of antibiotic stewardship in people with ARIs. Future high-quality research is needed to confirm the results in immunosuppressed patients and patients with non-respiratory infections.

Transcriptomic Biomarkers to Discriminate Bacterial from Nonbacterial Infection in Adults Hospitalized with Respiratory Illness

Lower respiratory tract infection (LRTI) commonly causes hospitalization in adults. Because bacterial diagnostic tests are not accurate, antibiotics are frequently prescribed. Peripheral blood gene expression to identify subjects with bacterial infection is a promising strategy. We evaluated whole blood profiling using RNASeq to discriminate infectious agents in adults with microbiologically defined LRTI. Hospitalized adults with LRTI symptoms were recruited. Clinical data and blood was collected, and comprehensive microbiologic testing performed. Gene expression was measured using RNASeq and qPCR. Genes discriminatory for bacterial infection were identified using the Bonferroni-corrected Wilcoxon test. Constrained logistic models to predict bacterial infection were fit using screened LASSO. We enrolled 94 subjects who were microbiologically classified; 53 as “non-bacterial” and 41 as “bacterial”. RNAseq and qPCR confirmed significant differences in mean expression for 10 genes previously identified as discriminatory for bacterial LRTI. A novel dimension reduction strategy selected three pathways (lymphocyte, α-linoleic acid metabolism, IGF regulation) including eleven genes as optimal markers for discriminating bacterial infection (naïve AUC = 0.94; nested CV-AUC = 0.86). Using these genes, we constructed a classifier for bacterial LRTI with 90% (79% CV) sensitivity and 83% (76% CV) specificity. This novel, pathway-based gene set displays promise as a method to distinguish bacterial from nonbacterial LRTI.

A novel immune biomarker IFI27 discriminates between influenza and bacteria in patients with suspected respiratory infection

Host response biomarkers can accurately distinguish between influenza and bacterial infection. However, published biomarkers require the measurement of many genes, thereby making it difficult to implement them in clinical practice. This study aims to identify a single-gene biomarker with a high diagnostic accuracy equivalent to multi-gene biomarkers.In this study, we combined an integrated genomic analysis of 1071 individuals with in vitro experiments using well-established infection models.We identified a single-gene biomarker, IFI27, which had a high prediction accuracy (91%) equivalent to that obtained by multi-gene biomarkers. In vitro studies showed that IFI27 was upregulated by TLR7 in plasmacytoid dendritic cells, antigen-presenting cells that responded to influenza virus rather than bacteria. In vivo studies confirmed that IFI27 was expressed in influenza patients but not in bacterial infection, as demonstrated in multiple patient cohorts (n=521). In a large prospective study (n=439) of patients presented with undifferentiated respiratory illness (aetiologies included viral, bacterial and non-infectious conditions), IFI27 displayed 88% diagnostic accuracy (AUC) and 90% specificity in discriminating between influenza and bacterial infections.IFI27 represents a significant step forward in overcoming a translational barrier in applying genomic assay in clinical setting; its implementation may improve the diagnosis and management of respiratory infection.

Antibiotic overuse for acute respiratory tract infections in Sri Lanka: a qualitative study of outpatients and their physicians

Background

Acute respiratory tract infections (ARTIs) are a common reason for antibiotic overuse worldwide. We previously showed that over 80% of outpatients presenting to a tertiary care hospital in Sri Lanka with influenza-like illness received antibiotic prescriptions, although almost half were later confirmed to have influenza. The purpose of this qualitative study was to assess Sri Lankan patients’ and physicians’ attitudes towards ARTI diagnosis and treatment.

Methods

Semi-structured interviews were conducted with 50 outpatients with ARTIs and five physicians in the Outpatient Department (OPD) at a large, public tertiary care hospital in southern Sri Lanka. Interviews were audio-recorded, transcribed, and analyzed for themes related to ARTI diagnosis and treatment.

Results

Patients frequently sought ARTI care in the public sector due to the receipt of free care and the perception that government hospitals carried a sense of responsibility for patients’ health. Patients reported multiple medical visits for their illnesses of short duration and many indicated that they were seeking care in the OPD while at the hospital for another reason. While patients generally expected to receive medication prescriptions at their visit, most patients were not specifically seeking an antibiotic prescription. However, more than 70% of patients received antibiotic prescriptions at their OPD visit. Physicians incorrectly perceived that patients desired antibiotics or “capsules,” a common formulation of antibiotics dispensed in this outpatient setting, and cited patient demand as an important cause of antibiotic overuse. Physicians also indicated that high patient volume and fear of bacterial superinfection drove antibiotic overuse.

Conclusions

Patients in this study were seeking medication prescriptions for their ARTIs, but physicians incorrectly perceived that antibiotic prescriptions were desired. High patient volume and fear of bacterial superinfection were also important factors in antibiotic overuse. Training of physicians regarding guideline-concordant management and dealing with diagnostic uncertainty, education of patients regarding ARTI etiology and management, and systematic changes in the public outpatient care structure may help decrease unnecessary antibiotic prescriptions for ARTIs in this setting.

Nasopharyngeal Protein Biomarkers of Acute Respiratory Virus Infection

Infection of respiratory mucosa with viral pathogens triggers complex immunologic events in the affected host. We sought to characterize this response through proteomic analysis of nasopharyngeal lavage in human subjects experimentally challenged with influenza A/H3N2 or human rhinovirus, and to develop targeted assays measuring peptides involved in this host response allowing classification of acute respiratory virus infection. Unbiased proteomic discovery analysis identified 3285 peptides corresponding to 438 unique proteins, and revealed that infection with H3N2 induces significant alterations in protein expression. These include proteins involved in acute inflammatory response, innate immune response, and the complement cascade. These data provide insights into the nature of the biological response to viral infection of the upper respiratory tract, and the proteins that are dysregulated by viral infection form the basis of signature that accurately classifies the infected state. Verification of this signature using targeted mass spectrometry in independent cohorts of subjects challenged with influenza or rhinovirus demonstrates that it performs with high accuracy (0.8623 AUROC, 75% TPR, 97.46% TNR). With further development as a clinical diagnostic, this signature may have utility in rapid screening for emerging infections, avoidance of inappropriate antibacterial therapy, and more rapid implementation of appropriate therapeutic and public health strategies.

Anomaly Detection in Host Signaling Pathways for the Early Prognosis of Acute Infection

Clinical diagnosis of acute infectious diseases during the early stages of infection is critical to administering the appropriate treatment to improve the disease outcome. We present a data driven analysis of the human cellular response to respiratory viruses including influenza, respiratory syncytia virus, and human rhinovirus, and compared this with the response to the bacterial endotoxin, Lipopolysaccharides (LPS). Using an anomaly detection framework we identified pathways that clearly distinguish between asymptomatic and symptomatic patients infected with the four different respiratory viruses and that accurately diagnosed patients exposed to a bacterial infection. Connectivity pathway analysis comparing the viral and bacterial diagnostic signatures identified host cellular pathways that were unique to patients exposed to LPS endotoxin indicating this type of analysis could be used to identify host biomarkers that can differentiate clinical etiologies of acute infection. We applied the Multivariate State Estimation Technique (MSET) on two human influenza (H1N1 and H3N2) gene expression data sets to define host networks perturbed in the asymptomatic phase of infection. Our analysis identified pathways in the respiratory virus diagnostic signature as prognostic biomarkers that triggered prior to clinical presentation of acute symptoms. These early warning pathways correctly predicted that almost half of the subjects would become symptomatic in less than forty hours post-infection and that three of the 18 subjects would become symptomatic after only 8 hours. These results provide a proof-of-concept for utility of anomaly detection algorithms to classify host pathway signatures that can identify presymptomatic signatures of acute diseases and differentiate between etiologies of infection. On a global scale, acute respiratory infections cause a significant proportion of human co-morbidities and account for 4.25 million deaths annually. The development of clinical diagnostic tools to distinguish between acute viral and bacterial respiratory infections is critical to improve patient care and limit the overuse of antibiotics in the medical community. The identification of prognostic respiratory virus biomarkers provides an early warning system that is capable of predicting which subjects will become symptomatic to expand our medical diagnostic capabilities and treatment options for acute infectious diseases. The host response to acute infection may be viewed as a deterministic signaling network responsible for maintaining the health of the host organism. We identify pathway signatures that reflect the very earliest perturbations in the host response to acute infection. These pathways provide a monitor the health state of the host using anomaly detection to quantify and predict health outcomes to pathogens.

Aetiology of Acute Respiratory Tract Infections in Hospitalised Children in Cyprus

In order to improve clinical management and prevention of viral infections in hospitalised children improved etiological insight is needed. The aim of the present study was to assess the spectrum of respiratory viral pathogens in children admitted to hospital with acute respiratory tract infections in Cyprus. For this purpose nasopharyngeal swab samples from 424 children less than 12 years of age with acute respiratory tract infections were collected over three epidemic seasons and were analysed for the presence of the most common 15 respiratory viruses. A viral pathogen was identified in 86% of the samples, with multiple infections being observed in almost 20% of the samples. The most frequently detected viruses were RSV (30.4%) and Rhinovirus (27.4%). RSV exhibited a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. While RSV and PIV3 incidence decreased significantly with age, the opposite was observed for influenza A and B as well as adenovirus infections. The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.

Viral Infection in Adults with Severe Acute Respiratory Infection in Colombia

OBJECTIVES

To identify the viral aetiology in adult patients with severe acute respiratory infection (SARI) admitted to sentinel surveillance institutions in Bogotá in 2012.

DESIGN

A cross-sectional study was conducted in which microarray molecular techniques for viral identification were used on nasopharyngeal samples of adult patients submitted to the surveillance system, and further descriptions of clinical features and relevant clinical outcomes, such as mortality, need for critical care, use of mechanical ventilation and hospital stay, were obtained.

SETTING

Respiratory infections requiring hospital admission in surveillance centres in Bogotá, Colombia.

PARTICIPANTS

Ninety-one adult patients with acute respiratory infection (55% were female).

MEASUREMENTS

Viral identification, intensive care unit admission, hospital stay, and mortality.

RESULTS

Viral identification was achieved for 63 patients (69.2%). Comorbidity was frequently identified and mainly involved chronic pulmonary disease or pregnancy. Influenza, Bocavirus and Adenovirus were identified in 30.8%, 28.6% and 18.7% of the cases, respectively. Admission to the intensive care unit occurred in 42.9% of the cases, while mechanical ventilation was required for 36.3%. The average hospital stay was 9.9 days, and mortality was 15.4%. Antibiotics were empirically used in 90.1% of patients.

CONCLUSIONS

The prevalence of viral aetiology of SARI in this study was high, with adverse clinical outcomes, intensive care requirements and high mortality.

Use of Rapid Influenza Testing to Reduce Antibiotic Prescriptions Among Outpatients with Influenza-Like Illness in Southern Sri Lanka

Acute respiratory tract infections (ARTIs) are a common reason for unnecessary antibiotic prescriptions worldwide. Our objective was to determine if providing access to rapid influenza test results could reduce antibiotic prescriptions for ARTIs in a resource-limited setting. We conducted a prospective, pre-post study from March 2013 to October 2014. Outpatients presenting to a hospital in Sri Lanka were surveyed for influenza-like illness-onset of fever ≥ 38.0°C and cough in prior 7 days. Enrolled patients were administered a structured questionnaire, physical examination, and nasal/nasopharyngeal sampling for rapid influenza A/B testing. Influenza test results were released only during phase 2 (January-October 2014). We enrolled 571 patients with ILI-316 in phase 1 and 241 in phase 2. The proportion positive for influenza was 46.5% in phase 1 and 28.6% in phase 2, P < 0.001. Between phases, antibiotic prescriptions decreased from 81.3% to 69.3% (P = 0.001) among all patients and from 83.7% to 62.3% (P = 0.001) among influenza-positive patients. On multivariable analysis, a positive influenza result during phase 2 was associated with lower odds of antibiotic prescriptions (OR = 0.50, 95% CI = 0.26-0.95). This prospective study suggests that providing access to rapid influenza testing may reduce unnecessary antibiotic prescriptions in resource-limited settings.

What was old is new again: using the host response to diagnose infectious disease

A century of advances in infectious disease diagnosis and treatment changed the face of medicine. However, challenges continue to develop including multi-drug resistance, globalization that increases pandemic risks and high mortality from severe infections. These challenges can be mitigated through improved diagnostics, focusing on both pathogen discovery and the host response. Here, we review how 'omics' technologies improve sepsis diagnosis, early pathogen identification and personalize therapy. Such host response diagnostics are possible due to the confluence of advanced laboratory techniques (e.g., transcriptomics, metabolomics, proteomics) along with advanced mathematical modeling such as machine learning techniques. The road ahead is promising, but obstacles remain before the impact of such advanced diagnostic modalities is felt at the bedside.