Thair SA, He YD, Hasin-Brumshtein Y, et al. Transcriptomic Similarities and Differences in Host Response between SARS-CoV-2 and Other Viral Infections. .
This preprint manuscript shows that the host response in SARS-CoV-2 (aka “COVID19”) positive patients is similar to that of patients with other severe viral infections. Using RNA-seq we profiled peripheral blood 62 prospectively enrolled patients with community-acquired lower respiratory tract infection by SARS-Cov-2 within the first 24 hours of hospital admission in Athens, Greece. Their profiles were compared to those of 1855 patients across 23 studies of patients with other viruses including influenza, RSV, Ebola, Dengue, and SARS-CoV-1. Two genes, ACO1 and ATL3, had demonstrably different expression in COVID19+ patients than those of other viral infections.
Schneider JE, Romanowsky J, Schuetz P, et al. Cost Impact Model of a Novel Multi-mRNA Host Response Assay for Diagnosis and Risk Assessment of Acute Respiratory Tract Infections and Sepsis in the Emergency Department. JHEOR. 2020;7(1):24-34. doi:10.36469/jheor.2020.12637.
The publication described a cost impact model comparing the cost of standard of care versus the use of HostDx Sepsis in two hypothetical arms with 1000 patients presenting with symptoms of ARTI in the Emergency Department of an average US hospital. Compared to standard of care, on average, the HostDx Sepsis test arm showed a 0.80 day reduction in hospital ward length of stay (a 36.7% decrease), 1.49 reduction in days of antibiotic treatment (a 29.5% decrease), and a 1.67% decrease in 30-day mortality rate (a 13.64% decrease). Average cost savings were estimated at $1,974 per patient tested and nearly $2 million for the 1000-patient cohort (before considering the price of the HostDx Sepsis test, which has not yet been established).
Mayhew M, et al. “Optimization of genomic classifiers for clinical deployment: evaluation of Bayesian optimization for identification of predictive models of acute infection and in-hospital mortality.” arXiv. Mar 2020.
Currently, detection of acute infection as well as assessment of a patient’s severity of illness are based on imperfect (and often superficial) measures of patient physiology. Characterization of a patient’s immune response by quantifying expression levels of key genes from blood represents a potentially more timely and precise means of accomplishing both tasks. Machine learning methods provide a platform for development of deployment-ready classification models robust to the smaller, more heterogeneous datasets typical of healthcare.
Mayhew M, et al. “A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections.” Nature Communications. Mar 2020.
Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Severe acute infections and sepsis are globally associated with substantial mortality (nearly half of all inpatient deaths) and dollars spent ($24 billion annually in the US). An alternative to testing for pathogens is to examine the host immune response to infection, and thereby infer the presence and type of infection. Recent advances in machine learning and artificial intelligence offer the promise both of improved generalizability and of solving non-binary problems, such as distinguishing between bacterial, viral, and non-infectious inflammation.
Sweeney TE, Schultz B, Khatri P, et al. “Pilot study of a novel serum mRNA gene panel for diagnosis of acute septic arthritis” World Journal of Orthopedics. Dec 2019.
Septic arthritis is an orthopedic emergency requiring immediate surgical intervention. Current diagnostic standard of care is an invasive joint aspiration. Aspirations provide information about the inflammatory cells in the sample within a few hours, but there is often ambiguity about whether the source is infectious (e.g. bacterial) or non-infectious (e.g. gout). Cultures can take days to result, so decisions about surgery are often made with incomplete data. Novel diagnostics are thus needed. The “Sepsis MetaScore” (SMS) is an 11-mRNA host immune blood signature that can distinguish between infectious and non-infectious acute inflammation. It has been validated in multiple cohorts across heterogeneous clinical settings.
Sweeney TE, Liesenfeld O, May L. “Diagnosis of bacterial sepsis: why are tests for bacteremia not sufficient?” Expert Review of Molecular Diagnostics. Aug 2019.
Rapid diagnosis of sepsis, and its underlying causes, is of great interest. With pathogen detection techniques showing a multitude of limitations for clinical utility in early sepsis workflow, probing the host immune response to infection has gained increased interest. This editorial explores the belief that the initial treatment of patients with sepsis will be best supported by ultra-rapid tools that can inform on the ‘two axes’ of sepsis (diagnosis of infection and estimate of severity) at the time of initial presentation.
Gunsolus I, Sweeney TE, et al. “Diagnosing and managing sepsis by probing the host response to infection: Advances, opportunities, and challenges.” Journal of Clinical Microbiology. Jul 2019.
Sepsis is a major source of mortality and morbidity globally. Accurately diagnosing sepsis remains challenging due to the heterogeneous nature of the disease, and delays in diagnosis and intervention contribute to high mortality rates. Measuring the host response to infection enables more rapid diagnosis of sepsis than is possible through direct detection of the causative pathogen, and recent advances in host response diagnostics and prognostics hold promise for improving outcomes.
Sweeney, TE, et al. “Unsupervised analysis of transcriptomics in bacterial sepsis across multiple datasets reveals three robust clusters.” Critical Care Medicine. Mar 2018.
A 33 gene expression test has identified three sepsis subtypes (Inflammopathic, Adaptive, and Coagulopathic), developed and validated via advanced informatics methods. The Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. These findings may enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.
Sweeney TE, et al. “A community approach to mortality prediction in sepsis via gene expression analysis.” Nature Communications. Feb 2018.
The HostDx Sepsis severity gene set, exclusively licensed from Stanford by Inflammatix, when combined with clinical severity scores (the current standard of care), demonstrated a substantial increase in prognostic power for 30-day mortality (i.e., an AUC increase of 10 percent, from 77 percent to 87 percent). This would translate to an ability to rule out approximately 20 percent more sepsis cases, compared to clinical severity scores alone. Such findings suggest this approach could help save substantial resources by avoiding unnecessary care.
Sweeney TE, Khatri P. “Generalizable biomarkers in critical care: toward precision medicine.” Critical Care Medicine. Jun 2017.
Many critical illnesses, like sepsis, are defined syndromically. These syndromes typically have clear, though changing, clinical criteria. Assuming the clinical spectrum of a disease has a common molecular pathophysiology, then a molecular biomarker should exist that is generalizable to the disease. Once a disease is clearly defined, then we can begin dividing it into subtypes, which allows for a “precision medicine” approach to medical care.
Sweeney TE, et al. “Validation of the Sepsis MetaScore for diagnosis of neonatal sepsis.” J Ped Infect Dis Soc. Apr 2017.
Neonates are at increased risk for developing sepsis, but this population often exhibits ambiguous clinical signs that complicate the diagnosis of infection. Until now, no biomarker has yet shown enough diagnostic accuracy to rule out sepsis at the time of clinical suspicion.
Sweeney TE, Khatri P. “Benchmarking sepsis gene expression diagnostics using public data.” Critical Care Medicine. Jan 2017.
In response to a need for better sepsis diagnostics, several new gene expression classifiers have been recently published, including the 11-gene “Sepsis MetaScore” (exclusively licensed by Inflammatix), the “FAIM3-to-PLAC8” ratio, and the Septicyte Lab. We performed a systematic search for publicly available gene expression data in sepsis and tested each gene expression classifier in all included data sets. The Inflammatix classifier was shown to be superior.
Sweeney TE, Wong HR, Khatri P. “Robust classification of bacterial and viral infections via integrated host gene expression diagnostics.” Science Translational Medicine. Jul 2016.
It can be difficult to distinguish patients with noninfectious inflammation from those with bacterial and viral infections, and only those with bacterial sepsis derive any benefit from antibiotics. We have created an integrated score that not only identifies infected patients but also classifies their infection as bacterial or viral, suggesting appropriate treatment.
Sweeney TE, Shidham A, Wong HR, Khatri P. “A Comprehensive time-course-based meta-analysis of sepsis and sterile inflammation reveals a robust discriminatory gene set.” Science Translational Medicine. May 2015.
Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized and demonstrated that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation.