A simple algorithm could help primary care clinicians reduce antibiotic use by identifying which children with coughs and respiratory tract infections are most at risk of hospital admission.
UK researchers believe that, if their new algorithm is used to its full potential, it could reduce the amount of antibiotics prescribed to these children by as much as 10%.
“We believe use of this algorithm could represent a step-change in the care of children with coughs and RTIs”
They highlighted that cough with respiratory tract infection in children was the most common problem managed by GPs and primary care nurses in the NHS.
However, they also suggested that GPs and nurses found making decisions about prescribing antibiotics in children with RTIs difficult.
On one hand, the over-prescription of antibiotics was recognised as a serious concern but on the other, they want to ensure adequate treatment of a significant infection, they noted.
Led by the University of Bristol, the researchers found seven characteristics that could be used to identify children with cough and RTI at very low, normal and high risk of future hospitalisation.
The seven factors, which they have formed to make the mnemonic STARWAVe, are:
- Short (≤3 days) illness
- High temperature
- Age (<24 months)
If children have one or none, there is a very low risk of future admission, said the researchers.
But if three or more of these symptoms are present, there is a higher risk they will be hospitalised for their RTI in the following 30 days, they said.
More than 8,390 children from 224 GP practices around the UK took part in the study that resulted in the algorithm, which was published today in The Lancet Respiratory Medicine journal.
Among all the children in the cohort, there was a 1% risk of a child being hospitalised for their RTI in the following 30 days.
If a child had none or just one of the characteristics, admission risk fell to 0.3%. This then increased to 1.5% if the child had two to three symptoms but jumped to 12% if the child had four or more.
Uncertainty a factor in prescribing antibiotics for children
Lead author Professor Alastair Hay said: “We believe use of this algorithm could represent a step-change in the care of children with coughs and respiratory tract infections in primary care.
“We all know we need to curb the excessive use of antibiotics, but safely reducing prescribing means improving our identification of the patients who do and don’t need them,” he said.
“To our knowledge, this is the first time anyone has produced evidence that could be used to help hard-pushed, front-line clinicians make better antibiotic prescribing decisions,” he added.
The study was carried out as part of the five-year TARGET programme to develop tools for clinicians to help them provide better care for young patients presenting with RTIs.
It also included researchers from University of Southampton, Imperial College London, North Bristol NHS Trust, Public Health England and the University of Washington in the US.
In a linked editorial on the study, three UK respiratory experts backed the algorithm, saying: “Tools that inform antibiotic use according to individualised patient risk profiles could help to realise the benefits of antibiotic stewardship programmes in primary care while minimising potential risks.
“Using the STARWAVe mnemonic to help structure point-of-care assessment of children presenting with cough and respiratory tract infection should predict the risk of hospital admission with remarkable accuracy,” said Professor David Price, Alison Chisholm and Chris Winchester in the Lancet Respiratory Medicine.
- A YouTube video explaining more about STARWAVe is also available for clinicians to watch.