Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable hostgene-expression-based classifier for acute bacterial and viral infections. We use training data (N = 1069) from 18 retrospective transcriptomic studies. Using only 29 preselected host mRNAs, we train a neural-network classifier with a bacterial-vs-other area under the receiver-operating characteristic curve (AUROC) 0.92 (95% CI 0.90-0.93) and a viral-vsother AUROC 0.92 (95% CI 0.90-0.93). We then apply this classifier, inflammatix-bacterialviral-noninfected-version 1 (IMX-BVN-1), without retraining, to an independent cohort (N = 163). In this cohort, IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.86 (95% CI 0.77-0.93), and viral-vs.-other 0.85 (95% CI 0.76-0.93). In patients enrolled within 36 h of hospital admission (N = 70), IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.92 (95% CI 0.83-0.99), and viral-vs.-other 0.91 (95% CI 0.82-0.98). With further study, IMX-BVN-1 could provide a tool for assessing patients with suspected infection and sepsis at hospital admission.

Objectives: Complex critical syndromes like sepsis and coronavirus disease 2019 may be composed of underling “endotypes,” which may respond differently to treatment. The aim of this study was to test whether a previously defined bacterial sepsis endotypes classifier recapitulates the same clinical and immunological endotypes in coronavirus disease 2019. Design: Prospective single-center observational cohort study. Setting: Patients were enrolled in Athens, Greece, and blood was shipped to Inflammatix (Burlingame, CA) for analysis. Patients: Adult patients within 24 hours of hospital admission with coronavirus disease 2019 confirmed by polymerase chain reaction and chest radiography. Interventions: None. Measurements and Main results: We studied 97 patients with coronavirus disease 2019, of which 50 went on to severe respiratory failure (SRF) and 16 died. We applied a previously defined 33-messenger RNA classifier to assign endotype (Inflammopathic, Adaptive, or Coagulopathic) to each patient. We tested endotype status against other clinical parameters including laboratory values, severity scores, and outcomes. Patients were assigned as Inflammopathic (29%), Adaptive (44%), or Coagulopathic (27%), similar to our prior study in bacterial sepsis. Adaptive patients had lower rates of SRF and no deaths. Coagulopathic and Inflammopathic endotypes had 42% and 18% mortality rates, respectively. The Coagulopathic group showed highest d-dimers, and the Inflammopathic group showed highest C-reactive protein and interleukin-6 levels. Conclusions: Our predefined 33-messenger RNA endotypes classifier recapitulated immune phenotypes in viral sepsis (coronavirus disease 2019) despite its prior training and validation only in bacterial sepsis. Further work should focus on continued validation of the endotypes and their interaction with immunomodulatory therapy.

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Background Whether or not to administer antibiotics is a common and challenging clinical decision in patients with suspected infections presenting to the emergency department (ED). We prospectively validate InSep, a 29-mRNA blood-based host response test for the prediction of bacterial and viral infections. Methods The PROMPT trial is a prospective, non-interventional, multi-center clinical study that enrolled 397 adult patients presenting to the ED with signs of acute infection and at least one vital sign change. The infection status was adjudicated using chart review (including a syndromic molecular respiratory panel, procalcitonin and C-reactive protein) by three infectious disease physicians blinded to InSep results. InSep (version BVN-2) was performed using PAXgene Blood RNA processed and quantified on NanoString nCounter SPRINT. InSep results (likelihood of bacterial and viral infection) were compared to the adjudicated infection status. Results Subject mean age was 64 years, comorbidities were significant for diabetes (17.1%), chronic obstructive pulmonary disease (13.6%), and severe neurological disease (6.8%); 16.9% of subjects were immunocompromised. Infections were adjudicated as bacterial (14.1%), viral (11.3%) and noninfected (0.25%): 74.1% of subjects were adjudicated as indeterminate. InSep distinguished bacterial vs. viral/noninfected patients and viral vs. bacterial/noninfected patients using consensus adjudication with AUROCs of 0.94 (95% CI 0.90–0.99) and 0.90 (95% CI 0.83–0.96), respectively. AUROCs for bacterial vs. viral/noninfected patients were 0.88 (95% CI 0.79–0.96) for PCT, 0.80 (95% CI 0.72–89) for CRP and 0.78 (95% CI 0.69–0.87) for white blood cell counts (of note, the latter biomarkers were provided as part of clinical adjudication). To enable clinical actionability, InSep incorporates score cutoffs to allocate patients into interpretation bands. The Very Likely (rule in) InSep bacterial band showed a specificity of 98% compared to 94% for the corresponding PCT band (> 0.5 µg/L); the Very Unlikely (rule-out) band showed a sensitivity of 95% for InSep compared to 86% for PCT. For the detection of viral infections, InSep demonstrated a specificity of 93% for the Very Likely band (rule in) and a sensitivity of 96% for the Very Unlikely band (rule out). Conclusions InSep demonstrated high accuracy for predicting the presence of both bacterial and viral infections in ED patients with suspected acute infections or suspected sepsis. When translated into a rapid, point-of-care test, InSep will provide ED physicians with actionable results supporting early informed treatment decisions to improve patient outcomes while upholding antimicrobial stewardship. Registration number at Clinicaltrials.gov NCT 03295825.

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Key Points Question Can a whole-blood RNA transcriptomic metric (IMX) obtained in the first 12 hours after intensive care unit (ICU) admission accurately measure the presence of bacterial infection and risk for sepsis mortality? Findings In this diagnostic and prognostic study including 200 patients with critical illness enrolled from a surgical ICU, the IMX transcriptomic metric was equivalent to or significantly better than the sequential organ failure assessment score and existing biomarkers (procalcitonin and interleukin 6 levels) for the diagnosis of acute infections and estimation of 30-day mortality. Meaning These findings suggest that a single, rapid-turnaround, multivalent transcriptomic test could supplant existing metrics in identifying bacterial infection and estimating clinical outcomes among critically ill surgical patients.

Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

Background and importance mRNA-based host response signatures have been reported to improve sepsis diagnostics. Meanwhile, prognostic markers for the rapid and accurate prediction of severity in patients with suspected acute infections and sepsis remain an unmet need. IMX-SEV-2 is a 29-host-mRNA classifier designed to predict disease severity in patients with acute infection or sepsis. Objective Validation of the host-mRNA infection severity classifier IMX-SEV-2. Design, settings and participants Prospective, observational, convenience cohort of emergency department (ED) patients with suspected acute infections. Outcome measures and analysis Whole blood RNA tubes were analyzed using independently trained and validated composite target genes (IMX-SEV-2). IMX-SEV-2-generated risk scores for severity were compared to the patient outcomes in-hospital mortality and 72-h multiorgan failure. Main results Of the 312 eligible patients, 22 (7.1%) died in hospital and 58 (18.6%) experienced multiorgan failure within 72 h of presentation. For predicting in-hospital mortality, IMX-SEV-2 had a significantly higher area under the receiver operating characteristic (AUROC) of 0.84 [95% confidence intervals (CI), 0.76–0.93] compared to 0.76 (0.64–0.87) for lactate, 0.68 (0.57–0.79) for quick Sequential Organ Failure Assessment (qSOFA) and 0.75 (0.65–0.85) for National Early Warning Score 2 (NEWS2), ( P = 0.015, 0.001 and 0.013, respectively). For identifying and predicting 72-h multiorgan failure, the AUROC of IMX-SEV-2 was 0.76 (0.68–0.83), not significantly different from lactate (0.73, 0.65–0.81), qSOFA (0.77, 0.70–0.83) or NEWS2 (0.81, 0.75–0.86). Conclusion The IMX-SEV-2 classifier showed a superior prediction of in-hospital mortality compared to biomarkers and clinical scores among ED patients with suspected infections. No improvement for predicting multiorgan failure was found compared to established scores or biomarkers. Identifying patients with a high risk of mortality or multiorgan failure may improve patient outcomes, resource utilization and guide therapy decision-making.