A new blood test that would enable the commonest childhood illnesses to be diagnosed in less than an hour could “transform” medical care, doctors say.
Currently it can take doctors hours, days or even weeks to identify which of 18 infectious and inflammatory diseases a child with a worryingly high temperature is suffering from.
The delay means clinical teams cannot be sure whether the infant has group B streptococcus, respiratory syncytial virus (RSV), tuberculosis or another ailment. Doctors therefore cannot immediately distinguish between a bacterial infection, which may be life-threatening, and other, less serious illnesses.
The new findings suggest that the test, developed by an international team of doctors and scientists, could enable sick children around the world to be diagnosed more quickly and receive better treatment by cutting the time health professionals have to wait for a blood test to be analysed.
Professor Michael Levin, an expert in child health at Imperial College London who has helped to develop the novel approach, said that it could prove “transformative for healthcare”.
“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,” he said.
“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.”
The diagnostic test works by analysing a patient’s “gene expression”, according to new findings by the team involved, which has been published in the Cell Press journal Med.
The experts behind it used data from several thousand patients, including more than 1,000 children who had had an infectious or inflammatory disease, to identify which key genes were turned “on” or “off” in their response to a range of ailments. That gave them a molecular signature of disease.
They then used machine learning to work out which patterns of gene expression corresponded to which particular diseases and pathogens and focused on 161 genes for 18 conditions.
Dr Myrsini Kaforou, a senior lecturer in Imperial’s department of infectious diseases and a co-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.”
