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A blood test capable of simultaneously detecting and distinguishing between a range of childhood diseases has been developed by researchers at Imperial College London.
The test could enable clinicians to diagnose the cause of fever from 18 infectious or inflammatory diseases – including group B Streptococcus (GBS), respiratory syncytial virus (RSV), and tuberculosis – based on the distinctive pattern of genes being ‘switched on or off’ by the body in response to specific illnesses.
While current tests for some of the conditions can take several hours, days or even weeks, a test-based on this approach would be capable of providing a result in under 60 minutes.
The preliminary findings were published in Cell Press Med and build on more than a decade of research.
Professor Michael Levin, Chair in Paediatrics and International Child Health within the Department of Infectious Disease at Imperial College London, and co-senior author of the paper, said: “Despite huge strides forward in medical technology, when a child is brought into hospital with a fever, our initial approach is to treat based on the doctors’ ‘impression’ of the likely causes of the child’s illness.
“As clinicians, we need to make rapid decisions on treatment, often just based on the child’s symptoms, information from the parents, and our medical training and experience, but we may not know whether a fever is bacterial, viral, or something else until hours or days after a child has been admitted, when their test results come back. Such delays can stop patients getting the right treatment early on, so there is a clear and urgent need to improve diagnostics. Using this new approach, once it’s translated to near point of care devices, could be transformative for healthcare.”
Widespread overuse of antibiotics in childhood diseases
Infectious and inflammatory diseases often have general symptoms such as fever so clinical teams cannot reliably diagnose bacterial infections, which may be potentially life-threatening, from other causes, which may be less serious.
Often, patients may be given broad spectrum antibiotics until a bacterial infection can be ruled out. But this approach leads to the widespread overuse of antibiotics, ultimately contributing to antimicrobial resistance and an increase in drug-resistant infections.
Dr Myrsini Kaforou, Senior Lecturer within Imperial’s Department of Infectious Disease and co-senior author of the paper, said: “This body of work has enabled us to identify the molecular signature of a wide range of diseases based on 161 genes, out of thousands of genes in the human genome. By distinguishing between many diseases at the same time within the same test, we have developed a more comprehensive and accurate model that aligns with the way clinicians think about diagnosis.
“With this initial proof-of-concept study, we’ve been able to show that our multi-disease machine-learning diagnostic approach works. This kind of advance is only possible through interdisciplinary collaboration and large research consortia, which bring together expertise from infectious disease, molecular science, and bioinformatics.
“There is still much work to be done to progress this test to the clinic, but we are working towards it. A future diagnostic test based on this approach could help provide the right treatment, to the right patient, at the right time, while optimising antibiotic use, and reducing lengthy time to diagnosis for inflammatory diseases.”