Background Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another. Methods This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies. Results Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69–0.97 for viral classification. Signature size varied (1–398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months–1 year and 2–11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets. Conclusions In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature’s size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.

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Purpose: The inappropriate use of antimicrobials, especially in acute respiratory infections (ARIs), is largely driven by difficulty distinguishing bacterial, viral, and noninfectious etiologies of illness. A new frontier in infectious disease diagnostics looks to the host response for disease classification. This article examines how host responseebased diagnostics for ARIs are being used in clinical practice, as well as new developments in the research pipeline. Methods: A limited search was conducted of the relevant literature, with emphasis placed on literature published in the last 5 years (2014e2019). Findings: Advances are being made in all areas of host responseebased diagnostics for ARIs. Specifically, there has been significant progress made in single protein biomarkers, as well as in various "omics" fields (including proteomics, metabolomics, and transcriptomics) and wearable technologies. There are many potential applications of a host responseebased approach; a few key examples include the ability to discriminate bacterial and viral disease, presymptomatic diagnosis of infection, and pathogen-specific host response diagnostics, including modeling disease progression. Implications: As biomarker measurement technologies continue to improve, host responseebased diagnostics will increasingly be translated to clinically available platforms that can generate a holistic characterization of an individual's health. This knowledge, in the hands of both patient and provider, can improve care for the individual patient and help fight rising rates of antibiotic resistance. (Clin Ther.

Objectives Compare three host response strategies to distinguish bacterial and viral etiologies of acute respiratory illness (ARI). Methods In this observational cohort study, procalcitonin, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in 286 subjects with ARI from four emergency departments. Multinomial logistic regression and leave-one-out cross validation were used to evaluate the protein and mRNA tests. Results The mRNA panel performed better than alternative strategies to identify bacterial infection: AUC 0.93 vs. 0.83 for the protein panel and 0.84 for procalcitonin (P<0.02 for each comparison). This corresponded to a sensitivity and specificity of 92% and 83% for the mRNA panel, 81% and 73% for the protein panel, and 68% and 87% for procalcitonin, respectively. A model utilizing all three strategies was the same as mRNA alone. For the diagnosis of viral infection, the AUC was 0.93 for mRNA and 0.84 for the protein panel (p<0.05). This corresponded to a sensitivity and specificity of 89% and 82% for the mRNA panel, and 85% and 62% for the protein panel, respectively. Conclusions A gene expression signature was the most accurate host response strategy for classifying subjects with bacterial, viral, or non-infectious ARI.

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