An international team, led by and simultaneously distinguishing between 18 infectious or inflammatory diseases, including childhood fever, group B streptococcus (GBS), respiratory syncytial virus (RSV) and tuberculosis. with the potential to deliver a result in a fraction of the time of current diagnostic tests.
The results of the study were published in the scientific journal med.
Infant fever: here’s how to diagnose it in record time
Using a single blood sample, the test could allow doctors to diagnose the cause of childhood fever based on the distinctive pattern of genes being ‘turned 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 able to provide a result in less than 60 minutes.
The researchers explain that while their study represents a proof of concept for the method, showing that it works, a diagnostic test based on patients’ gene expression could dramatically improve the diagnosis of diseases such as childhood fever, reduce delayed and missed diagnoses, and have a significant impact on health care, particularly in developing regions.
The preliminary results, published today for the first time in the journal Med, build on more than a decade of research to detect and diagnose diseases based on gene expression patterns. This seminal work led to the establishment of the DIAMONDS consortium in 2020, an international project led by Imperial College London to develop rapid diagnostic tests for febrile illnesses.
Professor Michael Levin, chair of paediatrics and international child health at Imperial College London’s department of infectious diseases and co-senior author of the paper, explains: ‘Despite enormous advances in medical technology, when a child is brought into hospital with childhood fever, our initial approach is to treat based on the doctors’ “impression” of the likely causes of the child’s illness.”
“As physicians, we have to make quick decisions about treatment, often based only on the child’s symptoms, parental information, and our own medical training and experience,” she adds, “but we may not know whether childhood fever is bacterial, viral, or anything else until hours or days after a child is hospitalized, when the test results come back.”
“Such delays can prevent patients from receiving the right treatment early on, so there is a clear and urgent need to improve diagnostics. The use of this new approach, once translated into near-point-of-care devices, could be transformative for healthcare.”
Infectious and inflammatory diseases are the most common cause for children seeking medical care in hospitals and primary or community clinics. But with general symptoms like childhood fever, it can be difficult for clinical teams to reliably diagnose bacterial infections, which can be potentially life-threatening, from other causes, which may be less serious.
Often, patients can be given broad-spectrum antibiotics until a bacterial infection can be ruled out. But this approach leads to widespread antibiotic overuse, ultimately contributing to antimicrobial resistance and the rise of drug-resistant infections.
Most diagnostic tests focus on detecting pathogens, such as lateral flow tests (LFTs) for SARS-CoV-2, HIV, or influenza, or blood cultures to confirm the presence of bacteria or yeast. But LFTs can only provide a yes or no result for one of the possible causes of the disease, and blood cultures can take 72 hours or more to give reliable results.
In the latest study, the researchers explored an approach focused on detecting a patient’s pattern of gene expression in the blood that occurs in response to specific infections and inflammatory conditions. Using data from thousands of patients (including more than 1000 children with 18 infectious or inflammatory diseases) the team was able to identify which key genes were turned on or off in response to a range of diseases, providing a molecular signature of disease .
Machine learning was then applied to identify which gene expression patterns corresponded to specific disease areas and pathogens, focusing on a panel of 161 genes for 18 conditions. This panel was further validated in a cohort of 411 hospitalized pediatric patients with sepsis or severe infections (representing 13 of 18 diseases), where gene expression was captured by blood testing, and where diagnoses were were performed using current gold standard clinical methods.
New diagnostic tests cannot be tested in a clinical setting until they are approved, as some misdiagnoses could have serious consequences, such as failing to identify a life-threatening bacterial infection. Instead, the team used a “cost-sensitive” measure, based on the consensus of a panel of five clinical experts, to show where the test could be used to avoid misdiagnosis and where this would have the greatest consequences.
Dr Myrsini Kaforou, senior lecturer in Imperial’s Department of Infectious Diseases and co-senior author of the paper, said: ‘This body of work has enabled us to identify the molecular signature of a broad range of diseases based on 161 genes, out of thousands of genes in the human genome. By distinguishing between many diseases simultaneously within the same test, we have developed a more complete and accurate model that aligns with how doctors think about diagnosis.”
“With this first proof-of-concept study, we were able to demonstrate that our multi-disease machine learning-based diagnostic approach works. This kind of progress is only possible through interdisciplinary collaboration and large research consortia, which bring together the expertise of infectious diseases, molecular sciences and bioinformatics”.
“There is still a lot of work to be done to bring this test to the clinic, but we are working on it. A future diagnostic test based on this approach could help deliver the right treatment, to the right patient, at the right time, while optimizing the use of antibiotics and shortening the time-consuming diagnosis of inflammatory diseases.
The researchers point out that a functional test is not yet available for clinical practice and their RNA transcription panel would require further adaptation, testing and translation into a readily usable platform/device before it can be approved by regulatory authorities.
They highlight a potential benefit of their approach that it could help rapidly diagnose respiratory diseases, such as bacterial pneumonia or tuberculosis, whereas a pathogen-based blood test may not be effective (because the pathogen is in the lung, not the blood), a host could still detect the telltale molecular signature it leaves in the patient’s blood, through the patient’s gene expression.
As part of the international multi-partner DIAMONDS study, the next step is to test the approach on thousands of patients in hospitals in Europe, Africa and Asia. This phase will evaluate the new approach against the current gold standard for clinical diagnosis and how likely it is to change clinical decision-making.
Dominic Habgood-Coote, research associate in the Department of Infectious Diseases at Imperial College London and first author of the paper, said: “The use of host transcriptional responses to disease is of interest for the diagnosis of fever in children. , in which traditional diagnostic approaches are particularly difficult.
We have shown that our approach is useful for answering narrowly defined clinical questions, but there is a clear benefit to expanding it to include a wider range of diseases and more detailed classification.”
Professor Levin added: ‘Basically, I’m a clinician and the reason we do research is that we want something better for patients. I think the excitement around this platform is the clinical implication. If we can move it into clinical use, it could really transform healthcare services.”
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