Clinical Decision Support May Cut Antibiotic Use for Child Diarrhea

A digital clinical decision support tool can help providers determine whether a child’s diarrhea is caused by bacteria or a virus and potentially cut inappropriate antibiotic use, according to a study published in eLife.

The burden of infectious diarrhea is most prevalent in low- and middle-income countries in southeast Asia and Africa, where there is limited access to diagnostic testing, researchers noted. Providers often turn to antibiotics to treat the condition, although this method of treatment is sometimes inappropriate for patients.

According to researchers, unnecessary antibiotic use exposes children to significant adverse events, including serious allergic reactions and increased antibiotic resistance.

The World Health Organization (WHO) has named antibiotic resistance a global health and development threat, noting that the phenomenon makes infections harder to treat and increases the risk of disease spread, illness, and death.

“Diarrhea is a common condition among children in low-resource settings,” said lead author Benjamin Brintz, Research Associate at the Division of Epidemiology, University of Utah Health, Salt Lake City.

“Antibiotics are often prescribed for it, despite the fact these medications will not help patients who have diarrhea caused by viruses. Helping clinicians determine if a case of diarrhea is likely caused by a virus or bacteria could help reduce inappropriate antibiotic prescriptions.”

Researchers set out to develop a clinical decision support tool that integrates multiple real-time data sources – including real-time environmental, epidemiologic, and clinical information – to improve antibiotic stewardship for pediatric diarrhea.

The team included information about prior patients, the seasons, and the weather, which is useful because some viruses are seasonal in nature and certain bacterial infections can spread through flooding or similar conditions.

To account for interruptions to EHR sources, which can occur frequently in some settings, the team built the model so that it would still work if some information was missing. Researchers also optimized the tool for use on mobile devices.

The group then tested how well the clinical decision support model would work if it were applied to real cases of diarrhea in pediatric patients. The results showed that it could reduce inappropriate antibiotic prescriptions by more than 50 percent.

“Traditional clinical prediction rules focus on the clinical data from the presenting patient alone,” researchers said.

“In this analysis, we present a method that allows flexible integration of multiple data sources, including climate data and clinical or historical information from prior patients, resulting in improved predictive performance over traditional predictive models utilizing a single source of data.”

Researchers noted that because their model is easily accessed by cell phone, it’s well-suited to low-resource clinical settings.

“A mobile phone application is an ideal platform for a decision support tool implemented in low-resource settings. Through internet access by Wi-Fi or cellular data, a smartphone platform could automatically download recent patient or climate data, while its portability would facilitate clinicians in entering current patient clinical information,” researchers stated.

Going forward, the researchers will aim to determine whether the tool provides enough certainty that clinicians can trust it and that it will not lead to patients who need antibiotics being untreated.

If this clinical decision support tool can meet these standards, the team expects that the model can serve as a valuable resource for clinicians with limited diagnostic tools who often rely solely on their best professional judgment.

“The global burden of diarrhoea is highest in low- and middle-income countries, where there is limited access to laboratory testing,” said 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.”