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Personalized azithromycin treatment rules for children with watery diarrhea using machine learning

Sara S Kim1, Allison Codi2, James A Platts-Mills3

  • 1Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA. sara.kim2@emory.edu.

Nature Communications
|July 1, 2025
PubMed

Insights

Machine learning identifies children most likely to benefit from azithromycin for watery diarrhea. Personalized rules target treatment, reducing diarrhea risk and improving outcomes, potentially without pathogen diagnostics.

Area of Science:

  • Computational biology and bioinformatics
  • Pediatric infectious diseases
  • Clinical trial data analysis

Background:

  • Azithromycin is used to treat watery diarrhea in children, but optimal targeting strategies are needed.
  • Personalized medicine approaches can improve treatment efficacy by identifying subgroups most likely to benefit.
  • Machine learning offers powerful tools for analyzing complex clinical data to develop predictive treatment rules.

Purpose of the Study:

  • To develop and validate personalized treatment rules for azithromycin in children with watery diarrhea using machine learning.
  • To identify child-specific characteristics that predict benefit from azithromycin treatment.
  • To assess the impact of targeted azithromycin treatment on diarrhea duration, re-hospitalization, and mortality.

Main Methods:

  • Utilized data from a randomized trial (NCT03130114) involving 6692 children with watery diarrhea.
  • Employed an ensemble machine learning procedure to estimate child-level expected treatment benefit based on diagnostic, child, and clinical characteristics.
  • Developed personalized treatment rules and estimated the proportion of children treated and average treatment benefits.

Main Results:

  • Personalized treatment rules recommended azithromycin for approximately one-third of children.
  • Targeted treatment reduced the risk of diarrhea on day 3 by 10.1% (NNT: 10) and day 90 re-hospitalization/death by 2.4% (NNT: 42).
  • Host characteristics, rather than pathogen diagnostics, were stronger predictors for benefits related to re-hospitalization and death.

Conclusions:

  • Machine learning can effectively identify children who will benefit most from azithromycin for watery diarrhea.
  • Personalized targeting of azithromycin can significantly improve clinical outcomes and reduce healthcare utilization.
  • Effective targeting for severe outcomes may be achievable without relying on pathogen diagnostics, simplifying treatment strategies.

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