Completed Public Health & Healthcare Digestion, Kidneys & Other Organs

MICA: Accelerating Development of Infection Diagnostics for Patient Management and Reduction of Antibiotic Misuse

In plain English

AI plain-English summary

A doctor in a GP surgery or hospital could soon diagnose an infection in under 15 minutes, using a rapid test they perform themselves. This matters because antibiotics are often prescribed as a precaution when the cause of an infection is unclear—viral or bacterial. Without a fast, accurate diagnosis, doctors cannot safely withhold antibiotics, even when they are ineffective. This overuse drives antimicrobial resistance (AMR), a serious threat to modern healthcare. In 2012, nearly 300 deaths in the UK were linked to resistant infections like MRSA. This research programme will develop three rapid diagnostic tests. The first distinguishes viral from bacterial infections. The second identifies the specific pathogen, using *C. difficile* as an example. The third detects a common subtype of Carbapenem Resistant Enterobacteriaceae (CRE), a resistant infection. If successful, these tests would allow doctors to prescribe the right antibiotic—or none at all—before treatment begins. This would reduce total antibiotic use, slow the rise of AMR, and improve patient outcomes by avoiding ineffective or dangerous prescriptions.

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The discovery of antibiotics early in the 20th century revolutionised healthcare provision and antibiotics and other antimicrobials have become an integral part of modern healthcare. However, in recent decades, the use of antibiotics has increased massively, not only in healthcare provision but also in veterinary and agricultural (live stock) applications. This has led to an enormous rise in antimicrobial resistance (AMR), which is forming an ever-growing problem in modern healthcare, proving a serious threat to society. The number of instances where infections are resistant against common antibiotics is increasing rapidly, and bacterial infections with strains that are resistant to almost all known antibiotics (e.g. meticillin-resistant staphylococcus aureusis, MRSA) have contributed to a significant number of death (almost 300 in 2012, source: Office for National Statistics) and caused significant problems for affected patients and healthcare providers. The solution seems simple: drastically reduce the prescriptions of antimicrobials. However, where antimicrobials are required for medical treatment, withholding prescription is dangerous for the patient and unethical, and could furthermore negatively impact on the general public through increased spreading rates. There are two major types of infections: viral and bacterial. Only bacterial infections can be treated with antibiotics, but certain symptoms are common to both types of infections. A typical example is throat pain, which could be caused by a bacterial infection (e.g. Streptococcus pneumonia) or viral (e.g. influenza), or in fact could be caused by non-infection causes such as heart failure. More critical examples include meningitis, which, when caused by a bacterial infection (meningococcal disease) needs immediate medical attention, while viral meningitis tends to take a milder course requiring rest and observation for encephalitis. We argue that antimicrobial prescriptions can be reduced safely and ethically if better infection diagnosis is available. Many infections are viral in origin (and hence do not benefit from antibiotics), but often antibiotics are prescribed as a precaution as without suitable diagnostics the doctor cannot be sure what the origin of the infection is. Although some laboratory-based tests are currently available, these can take several days to give a clear answer, and hence precautionary antibiotic treatments are started before the test results are available. In this research programme we will develop rapid diagnostic tests that can be performed by the doctor her/himself, i.e. a GP in a primary care clinic or a consultant in a hospital, which will give an answer in less than 15 minutes, quick enough to inform treatment before it is prescribed. The first diagnostic test that this programme will develop will thus be to distinguish between viral and bacterial infections. Once a bacterial infection is diagnosed, or if symptoms are encountered which indicate bacterial infections, it is important to identify the bacterial strain that causes the infection, as different strains require different antibiotic treatments. The second diagnostic test that this project aims to develop is thus to test for pathogen that causes the infections and we have chosen the example of C. difficile infections, a common infection that causes severe diarrhoea. Finally, many bacteria are now resistant to common antibiotics and if the type of resistance is known, the antibiotic treatment can be tailored to be effective. The third diagnostic test that will be developed is thus to diagnose a common subtype of Carbapenem Resistant Enterobacteriaceae (CRE), which is common type of infection with antibiotic resistance. These quick and accurate tests will reduce the prescription of the wrong antibiotics, which will not only reduce to the total amount of antibiotics used (thus reducing AMR), but will also lead to a more effective patient management.

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Researchers

Andrew Kirby (Co-Investigator)Bethany Shinkins (Co-Investigator)Christoph Walti (Principal Investigator)Darren Tomlinson (Co-Investigator)Jonathan Sandoe (Co-Investigator)Lars Jeuken (Co-Investigator)Mark Wilcox (Co-Investigator)Michael McPherson (Co-Investigator)Michael Messenger (Co-Investigator)Nikil Kapur (Co-Investigator)Paul Anthony Millner (Co-Investigator)Robert Michael West (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

Accelerating the development of infection diagnostics for the reduction of antibiotic misuse (RID-AMR)
Rapid microfluidic diagnostic tools for fighting antimicrobial resistance
Developing and evaluating a framework for the rational design of antibiotic prescribing policies in resource-constrained hospital settings
Bacterial impedance cytometry for rapid antibiotic susceptibility testing
New smart diagnostics for infection

Original classification

Research Grant

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