Host-response biomarkers for the diagnosis of bacterial respiratory tract infections

Longitudinal metabolite profiling of Streptococcus pneumoniae-associated community-acquired pneumonia

INTRODUCTION

Longitudinal biomarkers in patients with community-acquired pneumonia (CAP) may help in monitoring of disease progression and treatment response. The metabolic host response could be a potential source of such biomarkers since it closely associates with the current health status of the patient.

OBJECTIVES

In this study we performed longitudinal metabolite profiling in patients with CAP for a comprehensive range of metabolites to identify potential host response biomarkers.

METHODS

Previously collected serum samples from CAP patients with confirmed Streptococcus pneumoniae infection (n = 25) were used. Samples were collected at multiple time points, up to 30 days after admission. A wide range of metabolites was measured, including amines, acylcarnitines, organic acids, and lipids. The associations between metabolites and C-reactive protein (CRP), procalcitonin, CURB disease severity score at admission, and total length of stay were evaluated.

RESULTS

Distinct longitudinal profiles of metabolite profiles were identified, including cholesteryl esters, diacyl-phosphatidylethanolamine, diacylglycerols, lysophosphatidylcholines, sphingomyelin, and triglycerides. Positive correlations were found between CRP and phosphatidylcholine (34:1) (cor = 0.63) and negative correlations were found for CRP and nine lysophosphocholines (cor = - 0.57 to - 0.74). The CURB disease severity score was negatively associated with six metabolites, including acylcarnitines (tau = - 0.64 to - 0.58). Negative correlations were found between the length of stay and six triglycerides (TGs), especially TGs (60:3) and (58:2) (cor = - 0.63 and - 0.61).

CONCLUSION

The identified metabolites may provide insight into biological mechanisms underlying disease severity and may be of interest for exploration as potential treatment response monitoring biomarker.

Antimicrobial treatment imprecision: an outcome-based model to close the data-to-action loop

Health-care systems, food supply chains, and society in general are threatened by the inexorable rise of antimicrobial resistance. This threat is driven by many factors, one of which is inappropriate antimicrobial treatment. The ability of policy makers and leaders in health care, public health, regulatory agencies, and research and development to deliver frameworks for appropriate, sustainable antimicrobial treatment is hampered by a scarcity of tangible outcome-based measures of the damage it causes. In this Personal View, a mathematically grounded, outcome-based measure of antimicrobial treatment appropriateness, called imprecision, is proposed. We outline a framework for policy makers and health-care leaders to use this metric to deliver more effective antimicrobial stewardship interventions to future patient pathways. This will be achieved using learning antimicrobial systems built on public and practitioner engagement; solid implementation science; advances in artificial intelligence; and changes to regulation, research, and development. The outcomes of this framework would be more ecologically and organisationally sustainable patterns of antimicrobial development, regulation, and prescribing. We discuss practical, ethical, and regulatory considerations involved in the delivery of novel antimicrobial drug development, and policy and patient pathways built on artificial intelligence-augmented measures of antimicrobial treatment imprecision.

Differential metabolic host response to pathogens associated with community-acquired pneumonia

Background

Metabolic changes induced by the host immune response to pathogens found in patients with community-acquired pneumonia (CAP) may provide insight into its pathogenesis. In this study, we characterized differences in the host metabolic response to common CAP-associated pathogens.

Method

Targeted metabolomic profiling was performed on serum samples obtained from hospitalized CAP patients (n = 119) at admission. We quantified 347 unique metabolites across multiple biochemical classes, including amines, acylcarnitines, and signaling lipids. We evaluated if unique associations between metabolite levels and specific CAP-associated pathogens could be identified.

Results

Several acylcarnitines were found to be elevated in C. burnetii and herpes simplex virus and lowered in M. pneumoniae as compared to other pathogens. Phenylalanine and kynurenine were found elevated in L. pneumophila as compared to other pathogens. S-methylcysteine was elevated in patients with M. pneumoniae, and these patients also showed lowered cortisol levels in comparison to almost all other pathogens. For the herpes simplex virus, we observed a unique elevation of eicosanoids and several amines. Many lysophosphatidylcholines showed an altered profile in C. burnetii versus S. pneumoniae, L. pneumophila, and respiratory syncytial virus. Finally, phosphatidylcholines were negatively affected by the influenza virus in comparison to S. pneumoniae.

Conclusions

In this exploratory analysis, metabolites from different biochemical classes were found to be altered in serum samples from patients with different CAP-associated pathogens, which may be used for hypothesis generation in studies on differences in pathogen host response and pathogenesis of CAP.

Research priorities towards precision antibiotic therapy to improve patient care

Antibiotic resistance presents an incessant threat to our drug armamentarium that necessitates novel approaches to therapy. Over the past several decades, investigation of pharmacokinetic and pharmacodynamic (PKPD) principles has substantially improved our understanding of the relationships between the antibiotic, pathogen, and infected patient. However, crucial gaps in our understanding of the pharmacology of antibacterials and their optimal use in the care of patients continue to exist; simply attaining antibiotic exposures that are considered adequate based on traditional targets can still result in treatment being unsuccessful and resistance proliferation for some infections. It is this salient paradox that points to key future directions for research in antibiotic therapeutics. This Personal View discusses six priority areas for antibiotic pharmacology research: (1) antibiotic-pathogen interactions, (2) antibiotic targets for combination therapy, (3) mechanistic models that describe the time-course of treatment response, (4) understanding and modelling of host response to infection, (5) personalised medicine through therapeutic drug management, and (6) application of these principles to support development of novel therapies. Innovative approaches that enhance our understanding of antibiotic pharmacology and facilitate more accurate predictions of treatment success, coupled with traditional pharmacology research, can be applied at the population level and to individual patients to improve outcomes.

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.

Pneumonia—Overview

Pneumonia is very common and continues to exact a high burden on health. The Global Burden of Disease Study 2015 found lower respiratory infections (LRIs) were the leading infectious cause of death and the fifth leading cause of death overall. Pneumococcal pneumonia caused 55% of LRI deaths in all ages (1.5 million deaths).

Novel pathogens, particularly viruses, continue to emerge as causes of pneumonia. The rise of drug-resistance among common respiratory pathogens is a further challenge. Pneumonia is commonly classified according to patient location at the time of infection, leading to the categories of community-acquired, hospital-acquired and ventilator-acquired pneumonia.

Clinical Usefulness of Red Cell Distribution Width/Albumin Ratio to Discriminate 28-Day Mortality in Critically Ill Patients with Pneumonia Receiving Invasive Mechanical Ventilation, Compared with Lacate/Albumin Ratio: A Retrospective Cohort Study

The value of the red cell distribution width (RDW) is associated with prognosis in critically ill patients. A simplex combined index—the RDW/albumin ratio—has been proposed for the prediction of mortality, as has the lactate/albumin ratio. The aim of this study was to evaluate the clinical utility of the RDW/albumin ratio regarding 28-day mortality in critically ill patients with pneumonia. Clinical data of critically ill patients with pneumonia who were hospitalized in the medical intensive care unit from May 2018 to December 2020, and received invasive mechanical ventilation (IMV), were reviewed retrospectively. The values of RDW, lactate, and albumin measured at the time of IMV, were used for the index calculations. Of the 234 patients, the median age was 76 years, and 74.2% were male. The 28-day mortality rate was 47.3%. The median RDW/albumin ratio was significantly higher in non-survivors than survivors at 28 days (5.8 vs. 4.9, p < 0.001). A higher RDW/albumin ratio was significantly associated with increased 28-day mortality (odds ratio [OR] 1.338, 95% confidence interval [CI] 1.094–1.637, p = 0.005). The area under the receiver operating curve (AUROC) was 0.694 (95% CI: 0.630–758, p < 0.005) to discern 28-day mortality without significant difference, compared with that of the lactate/albumin ratio. Our data suggest that high RDW/albumin ratio has a similar predictability to the lactate/albumin ratio in critically ill patients with pneumonia receiving IMV.

Prediction of Treatment Outcome with Inflammatory Biomarkers after 2 Months of Therapy in Pulmonary Tuberculosis Patients: Preliminary Results

Pro-inflammatory mediators play an important role in the pathogenesis of pulmonary tuberculosis. Consecutively, 26 pulmonary tuberculosis patients were enrolled in our study based on the exclusion criteria. We have used Spearman’s correlation analysis, hierarchical clustering and regression modelling to evaluate the association of 11 biomarkers with culture status after antituberculosis treatment. The results of our study demonstrated that six inflammatory biomarkers of 11, C-reactive protein (CRP), white blood cells (WBC), neutrophils, interferon gamma inducible protein 10, C-reactive protein (CRP) to albumin ratio (CAR) and neutrophil to albumin ratio (NAR), were significantly associated with culture negativity. The predictive ability of a composite model of seven biomarkers was superior to that of any single biomarker based on area under the receiver operating characteristic curve (AUC) analysis, indicating an excellent prediction efficacy (AUC:0.892; 95% CI:0.732-1.0). We also found that the highest significant trends and lower levels of CRP and IP-10 were observed in the two-month treated tuberculosis (TB) patients. We believe that our study may be valuable in providing preliminary results for an additional strategy in monitoring and management of the clinical outcome of pulmonary tuberculosis. Using a panel of predictors added a superior value in predicting culture status after anti-TB therapy.

Metabolomic profiling of microbial disease etiology in community-acquired pneumonia

Diagnosis of microbial disease etiology in community-acquired pneumonia (CAP) remains challenging. We undertook a large-scale metabolomics study of serum samples in hospitalized CAP patients to determine if host-response associated metabolites can enable diagnosis of microbial etiology, with a specific focus on discrimination between the major CAP pathogen groups S. pneumoniae, atypical bacteria, and respiratory viruses. Targeted metabolomic profiling of serum samples was performed for three groups of hospitalized CAP patients with confirmed microbial etiologies: S. pneumoniae (n = 48), atypical bacteria (n = 47), or viral infections (n = 30). A wide range of 347 metabolites was targeted, including amines, acylcarnitines, organic acids, and lipids. Single discriminating metabolites were selected using Student’s T-test and their predictive performance was analyzed using logistic regression. Elastic net regression models were employed to discover metabolite signatures with predictive value for discrimination between pathogen groups. Metabolites to discriminate S. pneumoniae or viral pathogens from the other groups showed poor predictive capability, whereas discrimination of atypical pathogens from the other groups was found to be possible. Classification of atypical pathogens using elastic net regression models was associated with a predictive performance of 61% sensitivity, 86% specificity, and an AUC of 0.81. Targeted profiling of the host metabolic response revealed metabolites that can support diagnosis of microbial etiology in CAP patients with atypical bacterial pathogens compared to patients with S. pneumoniae or viral infections.

Comparison of sPLA2IIA performance with high-sensitive CRP neutrophil percentage PCT and lactate to identify bacterial infection

Early bacterial infection (BI) identification in resource-limiting Emergency Departments (ED) is challenging, especially in low- and middle-income counties (LMIC). Misdiagnosis predisposes to antibiotic overuse and propagates antimicrobial resistance. This study evaluates new emerging biomarkers, secretory phospholipase A2 group IIA (sPLA2-IIA) and compares with other biomarkers on their performance characteristic of BI detection in Malaysia, an LMIC. A prospective cohort study was conducted involving 151 consecutive patients admitted to the ED. A single measurement was taken upon patient arrival in ED and was analysed for serum levels of sPLA2-IIA, high-sensitive C-reactive protein (CRP), procalcitonin (PCT), neutrophil percentage (N%), and lactate. All biomarkers' performance was compared for the outcomes using area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The performance of sPLA2-IIA (AUROC 0.93 [95% CI: 0.89-0.97]; Sn 80% [95% CI: 72-87]; Sp 94% [95% CI: 81-89]) was the highest among all. It was comparable with high-sensitive CRP (AUROC 0.93 [95% CI: 0.88-0.97]; Sn 75% [95% CI: 66-83]; Sp 91 [95% CI: 77-98]) but had a higher Sn and Sp. The sPLA2-IIA was also found superior to N%, PCT, and lactate. This finding suggested sPLA2-IIA was recommended biomarkers for BI detection in LMIC.

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.

From Therapeutic Drug Monitoring to Model-Informed Precision Dosing for Antibiotics

Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have evolved as important tools to inform rational dosing of antibiotics in individual patients with infections. In particular, critically ill patients display altered, highly variable pharmacokinetics and often suffer from infections caused by less susceptible bacteria. Consequently, TDM has been used to individualize dosing in this patient group for many years. More recently, there has been increasing research on the use of MIPD software to streamline the TDM process, which can increase the flexibility and precision of dose individualization but also requires adequate model validation and re-evaluation of existing workflows. In parallel, new minimally invasive and noninvasive technologies such as microneedle-based sensors are being developed, which-together with MIPD software-have the potential to revolutionize how patients are dosed with antibiotics. Nonetheless, carefully designed clinical trials to evaluate the benefit of TDM and MIPD approaches are still sparse, but are critically needed to justify the implementation of TDM and MIPD in clinical practice. The present review summarizes the clinical pharmacology of antibiotics, conventional TDM and MIPD approaches, and evidence of the value of TDM/MIPD for aminoglycosides, beta-lactams, glycopeptides, and linezolid, for which precision dosing approaches have been recommended.

Investigation of pregnancy associated plasma protein-A and neopterin levels in Behçet's patients

Behçet's disease (BD) is an autoimmune disease that affects many organs. We aimed to investigate the relationship between BD and these pregnancy-associated plasma protein A (PAPP-A), neopterin, and high sensitive C-reactive protein (hsCRP) parameters. The study included 57 BD patients and 54 healthy controls. After evaluating the active and inactive disease status of the patients, analyzes were performed. When comparing the patient and control groups, neopterin (111.27 ± 37.49; 76.77 ± 38.27 [nmol/L]; P < .001) and hsCRP (11.81 ± 16.8; 3.62 ± 5.06 [mg/L]; P = .001) parameters were significantly higher in patients. Neopterin (117.68 ± 41.67; 94.85 ± 14.75 [nmol/L]; P = .038) and hsCRP (14.68 ± 18.7; 4.47 ± 7.27 [mg/L]; P = .002) found different in active and inactive patients. The sensitivities of neopterin and hsCRP were also found to be high in BD (respectively 93%, 67%). PAPP-A was especially elevated in skin pathologies (P = .02) and neopterin in joint involvement (P = .03). We think that the use of neopterin and hsCRP can help in diagnosis and follow-up of BD.

Diagnostic value of pentraxin 3 in respiratory tract infections: A meta-analysis

BACKGROUND

Pentraxin 3 is an acute inflammatory protein of the long pentraxin subfamily. A meta-analysis was performed to assess diagnostic accuracy of pentraxin 3 for respiratory tract infections.

METHODS

We identify studies examining diagnostic value of pentraxin 3 for respiratory tract infections by searching Pubmed, Web of Knowledge, and Cochrane Library. The sensitivity, specificity, negative likelihood ratio (LR), positive LR, and diagnostic odds ratio were pooled. The area under the summary receiver operator characteristic (SROC) curve and Q point value (Q*) were calculated.

RESULTS

A total of 8 studies with 961 individuals were eligible for this meta-analysis. The pooled sensitivity of pentraxin 3 in diagnosis of respiratory tract infections was 0.78, the pooled specificity was 0.73, the area under the SROC curve was 0.84, and the Q* was 0.77. The area under the SROC curve of serum and bronchoalveolar lavage fluid (BALF) pentraxin 3 was 0.85 and 0.89, respectively. Meta-regression analysis revealed that cutoff value was the source of heterogeneity among the included studies. The Deek funnel plot test suggested no evidence of publication bias. Subgroup analyses showed that the area under the SROC curve of pentraxin 3 in diagnosis of ventilator-associated pneumonia (VAP) was 0.89.

CONCLUSION

Pentraxin 3 has a moderate accuracy for diagnosing respiratory tract infections and VAP. The overall diagnostic value of BALF level of pentraxin 3 is superior to its serum concentration.