Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department

Comparison of two rapid host-response tests for distinguishing bacterial and viral infection in adults with acute respiratory infection

OBJECTIVES

Distinguishing bacterial from viral acute respiratory infection (ARI) is challenging, leading to inappropriate antimicrobial use and antimicrobial resistance. We evaluated the accuracy of two host-response tests to differentiate bacterial and viral infection.

METHODS

This study used patient blood samples previously collected during a randomised controlled trial of adults hospitalised with ARI. The aetiology for each patient was clinically adjudicated. PAXgene blood RNA samples were tested using the TriVerity test (which measures 29 mRNAs) and serum samples were tested using the MeMed BV test (which measures 3 proteins). Diagnostic accuracy was calculated against adjudicated aetiology.

RESULTS

169 patients were tested. Median age was 60 (45-74) years and 152 (90%) received antibiotics. 60 (36%) were adjudicated as bacterial, 54 (32%) as viral, 26 (15%) as viral/bacterial co-infection, and 29 (17%) as non-infected. For bacterial (including bacterial/viral co-infection) versus non-bacterial infection, the TriVerity bacterial score had a Positive Percentage Agreement (PPA) of 81% (95%CI 70-89) and a Negative Percentage Agreement (NPA) of 66% (95%CI 55-79) and the MeMed BV score had a PPA of 96% (95%CI 90-99) and NPA of 34% (95%CI 23-47). The AUROC for the two tests was 0.77 (95%CI 0.70-0.84) and 0.81 (95%CI 0.74-0.87) respectively, p = 0.388.

CONCLUSIONS

Both tests demonstrated similar overall accuracy for distinguishing bacterial infection with the Triverity test missing some bacterial infections and MeMed BV misclassifying most viral infections as bacterial. Prospective impact studies evaluating antibiotic use, safety and cost effectiveness are now required.

Role and significance of virus-bacteria interactions in disease progression

Understanding disease pathogenesis caused by bacteria/virus, from the perspective of individual pathogen has provided meaningful insights. However, as viral and bacterial counterparts might inhabit the same infection site, it becomes crucial to consider their interactions and contributions in disease onset and progression. The objective of the review is to highlight the importance of considering both viral and bacterial agents during the course of coinfection. The review provides a unique perspective on the general theme of virus-bacteria interactions, which either lead to colocalized infections that are restricted to one anatomical niche, or systemic infections that have a systemic effect on the human host. The sequence, nature, and underlying mechanisms of certain virus-bacteria interactions have been elaborated with relevant examples from literature. It also attempts to address the various applied aspects, including diagnostic and therapeutic strategies for individual infections as well as virus-bacteria coinfections. The review aims to aid researchers in comprehending the intricate interplay between virus and bacteria in disease progression, thereby enhancing understanding of current methodologies and empowering the development of novel health care strategies to tackle coinfections.

Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study

BACKGROUND

Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This study aimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals.

METHODS

In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set.

FINDINGS

Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission.

INTERPRETATION

These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection.

FUNDING

Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.