News Release

Decision-support tool could reduce unnecessary antibiotic prescriptions for child diarrhoea

Scientists present an advanced computer modelling tool that integrates real-time data and could help paediatricians determine if a child with diarrhoea needs antibiotics

Peer-Reviewed Publication


A decision-support tool that could be accessed via mobile devices may help clinicians in lower-resource settings avoid unnecessary antibiotic prescriptions for children with diarrhoea, a study published today in eLife shows.

The preliminary findings suggest that incorporating real-time environmental, epidemiologic, and clinical data into an easy-to-access, electronic tool could help clinicians appropriately treat children with diarrhoea even when testing is not available. This could help avoid the overuse of antibiotics, which contributes to the emergence of drug-resistant bacteria.

"Diarrhoea is a common condition among children in low-resource settings," explains lead author Benjamin Brintz, Research Associate at the Division of Epidemiology, University of Utah Health, Salt Lake City, US. "Antibiotics are often prescribed for it, despite the fact these medications will not help patients who have diarrhoea caused by viruses. Helping clinicians determine if a case of diarrhoea is likely caused by a virus or bacteria could help reduce inappropriate antibiotic prescriptions."

In their study, Brintz and his colleagues developed a statistical model that integrated multiple sources of real-time data to help clinicians determine whether a child's diarrhoea was caused by bacteria or a virus. This included information about prior patients, the seasons, and weather, which is useful because some viruses are seasonal in nature and certain bacterial infections may be spread by flooding or similar conditions.

To account for interruptions to electronic information sources, which can be frequent in some settings, the team built the model so it would still work if some of the information was missing. They also optimised it for use on mobile devices. They then tested how well the model would work if it were applied to real cases of diarrhoea in paediatric patients. Their results showed that it could reduce inappropriate antibiotic prescriptions by more than 50%.

The authors say the next step in their research will be to ensure the tool provides enough certainty that clinicians can trust it, and that it will not lead to patients who require antibiotics being undertreated. But if this decision aid can meet these high standards, it could be a valuable resource for clinicians with limited diagnostic tools who often rely solely on their best professional judgement.

"The global burden of diarrhoea is highest in low- and middle-income countries, where there is limited access to laboratory testing," concludes senior author Daniel Leung, Associate Professor of Internal Medicine (Infectious Disease), and Adjunct Associate Professor of Pathology (Microbiology and Immunology), at University of Utah Health. "The care of children in these regions could greatly benefit from an accurate and flexible decision-making tool."



The paper 'A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea' can be freely accessed online at Contents, including text, figures and data, are free to reuse under a CC BY 4.0 license.

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About eLife

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About University of Utah Health

University of Utah Health provides leading-edge and compassionate care for a referral area that encompasses Idaho, Wyoming, Montana, and much of Nevada. A hub for health sciences research and education in the region, U of U Health touts a $408 million research enterprise and trains the majority of Utah's physicians and health care providers at its Colleges of Health, Nursing, and Pharmacy and Schools of Dentistry and Medicine. With more than 20,000 employees, the system includes 12 community clinics and five hospitals. U of U Health is recognised nationally as a transformative health care system and regionally a provider of world-class care in numerous areas of health care, including oncology, cardiology, neuroscience, mental health, genetics and diabetes treatment.

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