OBJECTIVES: Host gene expression signatures discriminate bacterial and viral infection but have not been translated to a clinical test platform. This study enrolled an independent cohort of patients to describe and validate a first-in-class host response bacterial/viral test. DESIGN: Subjects were recruited from 2006 to 2016. Enrollment blood samples were collected in an RNA preservative and banked for later testing. The reference standard was an expert panel clinical adjudication, which was blinded to gene expression and procalcitonin results. SETTING: Four U.S. emergency departments. PATIENTS: Six-hundred twenty-three subjects with acute respiratory illness or suspected sepsis. INTERVENTIONS: Forty-five–transcript signature measured on the BioFire FilmArray System (BioFire Diagnostics, Salt Lake City, UT) in ~45 minutes. MEASUREMENTS AND MAIN RESULTS: Host response bacterial/viral test performance characteristics were evaluated in 623 participants (mean age 46 yr; 45% male) with bacterial infection, viral infection, coinfection, or noninfectious illness. Performance of the host response bacterial/viral test was compared with procalcitonin. The test provided independent probabilities of bacterial and viral infection in ~45 minutes. In the 213-subject training cohort, the host response bacterial/viral test had an area under the curve for bacterial infection of 0.90 (95% CI, 0.84–0.94) and 0.92 (95% CI, 0.87–0.95) for viral infection. Independent validation in 209 subjects revealed similar performance with an area under the curve of 0.85 (95% CI, 0.78–0.90) for bacterial infection and 0.91 (95% CI, 0.85–0.94) for viral infection. The test had 80.1% (95% CI, 73.7–85.4%) average weighted accuracy for bacterial infection and 86.8% (95% CI, 81.8–90.8%) for viral infection in this validation cohort. This was significantly better than 68.7% (95% CI, 62.4–75.4%) observed for procalcitonin (p < 0.001). An additional cohort of 201 subjects with indeterminate phenotypes (coinfection or microbiology-negative infections) revealed similar performance. CONCLUSIONS: The host response bacterial/viral measured using the BioFire System rapidly and accurately discriminated bacterial and viral infection better than procalcitonin, which can help support more appropriate antibiotic use.

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Background The initial focus of the US public health response to COVID-19 was the implementation of numerous social distancing policies. While COVID-19 was the impetus for imposing these policies, it is not the only respiratory disease affected by their implementation. This study aimed to assess the impact of social distancing policies on non-SARS-CoV-2 respiratory pathogens typically circulating across multiple US states. Methods Linear mixed-effect models were implemented to explore the effects of five social distancing policies on non-SARS-CoV-2 respiratory pathogens across nine states from January 1 through May 1, 2020. The observed 2020 pathogen detection rates were compared week-by-week to historical rates to determine when the detection rates were different. Results Model results indicate that several social distancing policies were associated with a reduction in total detection rate, by nearly 15%. Policies were associated with decreases in pathogen circulation of human rhinovirus/enterovirus and human metapneumovirus, as well as influenza A, which typically decrease after winter. Parainfluenza viruses failed to circulate at historical levels during the spring. Total detection rate in April 2020 was 35% less than historical average. Many of the pathogens driving this difference fell below historical detection rate ranges within two weeks of initial policy implementation. Conclusion This analysis investigated the effect of multiple social distancing policies implemented to reduce transmission of SARS-CoV-2 on non-SARS-CoV-2 respiratory pathogens. These findings suggest that social distancing policies may be used as an impactful public health tool to reduce communicable respiratory illness.

Acute gastrointestinal infection (AGI) represents a significant public health concern. To control and treat AGI, it is critical to quickly and accurately identify its causes. The use of novel multiplex molecular assays for pathogen detection and identification provides a unique opportunity to improve pathogen detection, and better understand risk factors and burden associated with AGI in the community. In this study, de-identified results from BioFire® FilmArray® Gastrointestinal (GI) Panel were obtained from January 01, 2016 to October 31, 2018 through BioFire® Syndromic Trends (Trend), a cloud database. Data was analyzed to describe the occurrence of pathogens causing AGI across United States sites and the relative rankings of pathogens monitored by FoodNet, a CDC surveillance system were compared. During the period of the study, the number of tests performed increased 10-fold and overall, 42.6% were positive for one or more pathogens. Seventy percent of the detections were bacteria, 25% viruses, and 4% parasites. Clostridium difficile, enteropathogenic Escherichia coli (EPEC) and norovirus were the most frequently detected pathogens. Seasonality was observed for several pathogens including astrovirus, rotavirus, and norovirus, EPEC, and Campylobacter. The co-detection rate was 10.2%. Enterotoxigenic E. coli (ETEC), Plesiomonas shigelloides, enteroaggregative E. coli (EAEC), and Entamoeba histolytica were detected with another pathogen over 60% of the time, while less than 30% of C. difficile and Cyclospora cayetanensis were detected with another pathogen. Positive correlations among co-detections were found between Shigella/Enteroinvasive E. coli with E. histolytica, and ETEC with EAEC. Overall, the relative ranking of detections for the eight GI pathogens monitored by FoodNet and BioFire Trend were similar for five of them. AGI data from BioFire Trend is available in near real-time and represents a rich data source for the study of disease burden and GI pathogen circulation in the community, especially for those pathogens not often targeted by surveillance.

BackgroundThe inability to reliably discriminate bacterial, viral, and non-infectious illness has led to an epidemic of antibiotic overuse and rising rates of antimicrobial resistance. Host gene expression provides a powerful approach to distinguish infection etiologies and guide appropriate therapy. However, existing platforms for transcriptomic analysis are not amenable for clinical application. The FilmArray platform is a sample-to-answer multiplex RT-PCR system that automates transcriptomic analysis including sample preparation and data analysis. Here we report the validation of a host gene expression FilmArray test to discriminate bacterial, viral, and non-infectious etiologies of illness.MethodsResearch use only (RUO) FilmArray pouches were manufactured with 45 host response assays previously shown to discriminate bacterial, viral, and non-infectious illness. These pouches were tested on whole blood samples from 226 patients with acute respiratory illness (ARI). Using clinical adjudication as the reference standard, there were 52 bacterial, 100 viral, and 75 non-infectious cases. Quantification cycles were recorded for each assay and normalized to an internal control. A logistic regression model generated probabilities of each condition, which were used to classify subjects.ResultsBeginning with 100 µL of blood, the FilmArray host response panel provided results in ~45 minutes from sample to answer. Overall accuracy for bacterial ARI relative to clinical adjudication was 85% with an area under the receiver operating characteristic curve (AUC) of 0.92. Accuracy and AUC for viral infection were 85% and 0.91, respectively. Ill patients without infection were correctly identified 86% of the time with an AUC of 0.88.ConclusionThese results show that the FilmArray system can rapidLy measure host gene expression to accurately discriminate bacterial, viral, and non-infectious illness. The development of such a system creates a new option to mitigate inappropriate antibiotic use. It also presents opportunities to use host gene expression as a diagnostic modality for a variety of disease states.Disclosures E. L. Tsalik, Host Response, Inc.: Founder, Equity. J. Montgomery, BioFire Diagnostics: Employee, Salary. J. Nawrocki, BioFire Diagnostics, LLC.: Employee, Salary. M. Deneris, BioFire Diagnostics, LLC.: Employee, Salary. C. Gritzen, BioFire Diagnostics, LLC.: Employee, Salary. J. Jones, BioFire Diagnostics, LLC.: Employee, Salary. R. Crisp, BioFire Diagnostics, LLC: Employee, Salary. G. S. Ginsburg, Host Response Inc: Board Member, Founder, Scientific Advisor and Shareholder, Stock (currently worth < $100). A. Hemmert, BioFire Diagnostics, LLC.: Employee and Investigator, Salary. C. W. Woods, Host Response, Inc.: Founder, Equity.