Bridging the Gaps: Systems-level approaches to antimicrobial resistance
Rapid detection of pathogens from diarrheal stool samples to improve antibiotic stewardship and facilitate target rather than empiric therapy
Sarah Kuehne (Life Sciences), Jamie Twycross (Computer Science) and Mathew Diggle (Microbiology, NHS)
Intestinal infectious diseases (IID) are a major cause of illness, and can cause outbreaks in both community and healthcare settings. Only a very small percentage of cases are seen by general practice, and an even smaller number examined in a laboratory setting. For each reported case of IID, there are an estimated 147 unreported cases, making this a hidden epidemic. Even more concerning is the consistent wasteful use of antibiotics among identified cases, often caused by a lack of sufficient information about the cause of the illness. In addition, because cases can be very severe, there is a tendency to prescribe broad-spectrum antibiotics. Altogether, this leads to a considerable rise in resistance in bacterial intestinal infections.
Researchers at The University of Nottingham are undertaking a pilot study to address the major questions facing the treatment of IID: what organisms are present in the gut, and which microorganism(s) is causing the infection? In order to address these key questions, the study focuses on identifying bacterial pathogens by obtaining patient samples and performing analysis, before using advanced computational and mathematical approaches to identify subsets of infectious agents.
The commonplace prescription of broad-spectrum antibiotics in cases of IID when the cause of the illness can’t be adequately identified is having a considerable impact on resistance. For example, in antimicrobial-resistant Salmonella infections the standard prescriptions used can delay effective treatment, putting the patient at risk. Antimicrobially-resistant strains can increase the risk of invasive infections, hospitalisation and death, and finding a way to effectively screen diarrheal stool samples would massively reduce this risk.
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