Infectious Disease

Algorithm that predicts viral etiology of diarrhea may curb inappropriate antibiotic use

September 26, 2022

2 min read

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Disclosures:
The Bill and Melinda Gates Foundation and National Institute of Allergy and Infectious Diseases funded this study. Nelson reports no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.

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Use of an algorithm-based tool to determine the likelihood of viral etiology for acute diarrhea in children did not significantly reduce antibiotic prescriptions in developing countries, according to a study in JAMA Pediatrics.

However, as the algorithm-predicted probability of viral diarrhea increased, physicians did prescribe fewer antibiotics, researchers reported in a post hoc analysis.

Source: Adobe Stock.

Source: Adobe Stock.

According to a related press release, most children with diarrhea in developing nations receive antibiotics. “That means the vast majority of cases are treated inappropriately,” study author Eric J Nelson, MD, PhD, MS, a pediatric hospitalist at University of Florida Health Shands Children’s Hospital and faculty member at the university’s Emerging Pathogens Institute, said in the release. “We’re trying to figure out how to build clinical decision-support software that is fast, easy to use, accurate and meets the needs of health care providers in global health settings.”

Nelson and colleagues had previously developed a diarrheal etiologic prediction (DEP) algorithm that uses data from clinical history, patient symptoms and location-specific inputs, including clinical presentation of past patients, historical prevalence and weather parameters.

To determine whether the algorithm would influence antibiotic prescriptions, researchers conducted a randomized, crossover study that included 30 physicians and 941 patients with acute diarrhea (57.1% male; age range, 2-59 months) at three sites in Bangladesh from Nov. 17, 2020, to Jan. 21, 2021, and four sites in Mali from Jan. 6, 2021, to March 5, 2021. Participating physicians were randomized in the first 4-week study period to the intervention arm, in which they accessed the DEP algorithm with a smartphone-based electronic clinical decision-support (eCDS) tool, or the control arm, without the DEP.

A 1-week washout period and a second, 4-week crossover period followed.

The proportion of children who were prescribed an antibiotic served as the primary outcome.

According to study results, there was no difference in the proportion of children given antibiotics when physicians used the DEP [risk difference (RD) = –4.2%; 95% CI, –10.7 to 1].

However, a post hoc analysis that accounted for the predicted probability of a viral cause revealed a statistically significant difference between the DEP arm and the control arm (RD = -0.056; 95% CI, -0.128 to -0.01), with a 14% decline in the likelihood of antibiotic prescribing for every 10% rise in algorithm-predicted probability of viral diarrhea (OR = 0.86; 95% CI, 0.76-0.96).

“These findings represent a technical and behavioral proof-of-concept that a probability-based eCDS in resource-limited settings can impact antibiotic use in pediatric patients,” Nelson and colleagues wrote. “If replicated, the use of etiological prediction in decision support tools represents an important advancement to improve antibiotic stewardship in a clinical context prone to high rates of inappropriate antibiotic use.”

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