Multisite validation of a host response signature for predicting likelihood of bacterial and viral infections in patients with suspected influenza

Shojaei, Maryam; Chen, Uan-I; Midic, Uros; Thair, Simone A.; Teoh, Sally; McLean, Anthony S.; Sweeney, Timothy E.; Thompson, Matthew; Liesenfeld, Oliver; Khatri, Purvesh; Tang, Benjamin

doi:10.1111/eci.13957

Cited by 4 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We and others have validated these signatures in independent prospective cohorts [ 57 , 58 ]. Combined with machine learning-based classifiers, we have moved several of these signatures to clinical development and demonstrated their accuracy in multiple prospective cohorts [ 30 , 31 , 56 , 59 , 60 ]. Our long-term goal is to extend the similar capability to nasal swab samples, and the current work is the first step in that process that demonstrates the feasibility of our approach.…”

Section: Discussionmentioning

confidence: 99%

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples

Pandya,

He,

Sweeney

et al. 2023

Genome Med

Self Cite

View full text Add to dashboard Cite

Background Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful treatment requires timely diagnosis of viral etiology, which is complicated by overlap in clinical presentation with the non-viral ARIs. Multiple pandemics in the twenty-first century to date have further highlighted the unmet need for effective monitoring of clinically relevant emerging viruses. Recent studies have identified conserved host response to viral infections in the blood. Methods We hypothesize that a similarly conserved host response in nasal samples can be utilized for diagnosis and to rule out viral infection in symptomatic patients when current diagnostic tests are negative. Using a multi-cohort analysis framework, we analyzed 1555 nasal samples across 10 independent cohorts dividing them into training and validation. Results Using six of the datasets for training, we identified 119 genes that are consistently differentially expressed in viral ARI patients (N = 236) compared to healthy controls (N = 146) and further down-selected 33 genes for classifier development. The resulting locked logistic regression-based classifier using the 33-mRNAs had AUC of 0.94 and 0.89 in the six training and four validation datasets, respectively. Furthermore, we found that although trained on healthy controls only, in the four validation datasets, the 33-mRNA classifier distinguished viral ARI from both healthy or non-viral ARI samples with > 80% specificity and sensitivity, irrespective of age, viral type, and viral load. Single-cell RNA-sequencing data showed that the 33-mRNA signature is dominated by macrophages and neutrophils in nasal samples. Conclusion This proof-of-concept signature has potential to be adapted as a clinical point-of-care test (‘RespVerity’) to improve the diagnosis of viral ARIs.

show abstract

Section: Discussionmentioning

confidence: 99%

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples

Pandya,

He,

Sweeney

et al. 2023

Genome Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…8,9 In a recent article published in EJCI, Shojaei et al tested a transcriptomic test evaluating the expression levels of 29 genes in blood of patients with suspected influenza in Australia. 10 This test was highly accurate to identify either a bacterial or a viral infection in this setting which included patients attending to the ER, ICU and outpatient clinics. The test provided valuable information to potentially guide antibiotic administration, yielding a positive LR of 4.38, with 88% specificity to rule-in a bacterial infection in the highest quartile of the bacterial score, and a negative LR of .13 and a sensitivity of 96% to rule out bacterial infection in the lowest quartile of the score.…”

Section: Prime-time For Clinical Use Of Transcriptomics For Different...mentioning

confidence: 99%

“…In a recent article published in EJCI, Shojaei et al tested a transcriptomic test evaluating the expression levels of 29 genes in blood of patients with suspected influenza in Australia 10 . This test was highly accurate to identify either a bacterial or a viral infection in this setting which included patients attending to the ER, ICU and outpatient clinics.…”

Section: Figurementioning

confidence: 99%

Prime‐time for clinical use of transcriptomics for differentiating viral from bacterial respiratory infection

Fuente

García-Mateo

Bermejo-Martín

2023

Eur J Clin Investigation

View full text Add to dashboard Cite

“…Improving patient selection in clinical trials by targeting treatment to patients more likely to develop severe outcomes may further enhance the resolution of clinical studies. Recent breakthroughs in omics approaches and machine learning algorithms enabling the discovery of clinically usable biomarkers -such as those we and others have identified to predict progression to severe dengue infection and other severe viral infections (201)(202)(203) -may aid with this effort.…”

Section: Host-targeted Broad-spectrum Antiviral Approachesmentioning

confidence: 99%

Preparing for the next viral threat with broad-spectrum antivirals

Karim¹,

Lo²,

Einav³

2023

Journal of Clinical Investigation

View full text Add to dashboard Cite

There is a large global unmet need for the development of countermeasures to combat hundreds of viruses known to cause human disease and for the establishment of a therapeutic portfolio for future pandemic preparedness. Most approved antiviral therapeutics target proteins encoded by a single virus, providing a narrow spectrum of coverage. This, combined with the slow pace and high cost of drug development, limits the scalability of this direct-acting antiviral (DAA) approach. Here, we summarize progress and challenges in the development of broad-spectrum antivirals that target either viral elements (proteins, genome structures, and lipid envelopes) or cellular proviral factors co-opted by multiple viruses via newly discovered compounds or repurposing of approved drugs. These strategies offer new means for developing therapeutics against both existing and emerging viral threats that complement DAAs.

show abstract

Multisite validation of a host response signature for predicting likelihood of bacterial and viral infections in patients with suspected influenza

Cited by 4 publications

References 23 publications

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples

Prime‐time for clinical use of transcriptomics for differentiating viral from bacterial respiratory infection

Preparing for the next viral threat with broad-spectrum antivirals

Contact Info

Product

Resources

About