A Multi-mRNA Host-Response Molecular Blood Test for the Diagnosis and Prognosis of Acute Infections and Sepsis: Proceedings from a Clinical Advisory Panel.
James DucharmeWesley H SelfTiffany M OsbornNathan A LedeboerJonathan RomanowskyTimothy E SweeneyOliver LiesenfeldRichard E RothmanPublished in: Journal of personalized medicine (2020)
Current diagnostics are insufficient for diagnosis and prognosis of acute infections and sepsis. Clinical decisions including prescription and timing of antibiotics, ordering of additional diagnostics and level-of-care decisions rely on understanding etiology and implications of a clinical presentation. Host mRNA signatures can differentiate infectious from noninfectious etiologies, bacterial from viral infections, and predict 30-day mortality. The 29-host-mRNA blood-based InSepTM test (Inflammatix, Burlingame, CA, formerly known as HostDxTM Sepsis) combines machine learning algorithms with a rapid point-of-care platform with less than 30 min turnaround time to enable rapid diagnosis of acute infections and sepsis, as well as prediction of disease severity. A scientific advisory panel including emergency medicine, infectious disease, intensive care and clinical pathology physicians discussed technical and clinical requirements in preparation of successful introduction of InSep into the market. Topics included intended use; patient populations of greatest need; patient journey and sample flow in the emergency department (ED) and beyond; clinical and biomarker-based decision algorithms; performance characteristics for clinical utility; assay and instrument requirements; and result readouts. The panel identified clear demand for a solution like InSep, requirements regarding test performance and interpretability, and a need for focused medical education due to the innovative but complex nature of the result readout. Innovative diagnostic solutions such as the InSep test could improve management of patients with suspected acute infections and sepsis in the ED, thereby lessening the overall burden of these conditions on patients and the healthcare system.
Keyphrases
- emergency department
- machine learning
- liver failure
- intensive care unit
- acute kidney injury
- septic shock
- healthcare
- respiratory failure
- drug induced
- sars cov
- high throughput
- end stage renal disease
- primary care
- type diabetes
- deep learning
- chronic kidney disease
- coronary artery disease
- case report
- aortic dissection
- palliative care
- gene expression
- dna methylation
- cardiovascular disease
- patient reported outcomes
- ejection fraction
- health insurance
- acute respiratory distress syndrome
- liquid chromatography
- binding protein
- single molecule
- big data
- adverse drug
- loop mediated isothermal amplification