NEW YORK – Siemens Healthineers said Monday that it has secured a three-year, $5.5 million contract with the National Institute of Allergy and Infectious Diseases to develop a next-generation sequencing-based test to guide antimicrobial treatments in sepsis patients.
The firm said it aims to create a test that could within six hours of a blood draw identify bacteria and fungi in the patient’s bloodstream and provide details on the likely resistance to certain antimicrobials. Under the terms of the contract, the firm plans to create a prototype test that could help guide treatment of patients with life-threatening infections.
"The bacteria causing sepsis have become ever more effective in evading generic treatment options, and a more targeted, precision antimicrobial approach is critical," Rangarajan Sampath, head of Siemens' Center for Innovation in Diagnostics, said in a statement. "It's within our capabilities as diagnostic test manufacturers to bridge this gap and support physicians by providing information they need as quickly as possible to treat their patients more precisely for better outcomes."
Siemens will collaborate on the project with Janus-I Science and the Louis Stokes Cleveland Department of Veterans Affairs Medical Center.
The company said sepsis results in about 350,000 deaths each year in the US and costs between $24 billion and $38 billion in hospital expenses. Sepsis patients whose infections are resistant to the initial antimicrobial therapy have mortality rates upward of 50 percent.
The market for sepsis testing has been heating up with some recent entries that are focused on hastening the time to diagnosis. Ad Astra said last week that it has secured US Food and Drug Administration approval for its QScout RLD point-of-care hematology analyzer that can be used to differentiate sepsis up to 24 hours faster than lactate and procalcitonin. Cytovale also launched this summer its 10-minute sepsis risk-scoring test that assesses cell responses using a combination of deformability cytometry of leukocyte biophysical properties along with high-speed imaging and machine learning.