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

Sara S. Kim (), Allison Codi, James A. Platts-Mills, Patricia B. Pavlinac, Karim Manji, Christopher R. Sudfeld, Christopher P. Duggan, Queen Dube, Naor Bar-Zeev, Karen Kotloff, Samba O. Sow, Sunil Sazawal, Benson O. Singa, Judd Walson, Farah Qamar, Tahmeed Ahmed, Ayesha Costa, David Benkeser and Elizabeth T. Rogawski McQuade
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Sara S. Kim: Emory University
Allison Codi: Emory University
James A. Platts-Mills: University of Virginia
Patricia B. Pavlinac: University of Washington
Karim Manji: Muhimbili University of Health and Allied Sciences
Christopher R. Sudfeld: Harvard T.H. Chan School of Public Health
Christopher P. Duggan: Harvard T.H. Chan School of Public Health
Queen Dube: Queen Elizabeth Central Hospital
Naor Bar-Zeev: Johns Hopkins Bloomberg School of Public Health
Karen Kotloff: University of Maryland School of Medicine
Samba O. Sow: Centre pour le Développement des Vaccins
Sunil Sazawal: Center for Public Health Kinetics
Benson O. Singa: Childhood Acute Illness and Nutrition Network
Judd Walson: Childhood Acute Illness and Nutrition Network
Farah Qamar: Aga Khan University
Tahmeed Ahmed: International Centre for Diarrhoeal Disease Research
Ayesha Costa: World Health Organization
David Benkeser: Emory University
Elizabeth T. Rogawski McQuade: Emory University

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract We use machine learning to identify innovative strategies to target azithromycin to the children with watery diarrhea who are most likely to benefit. Using data from a randomized trial of azithromycin for watery diarrhea (NCT03130114), we develop personalized treatment rules given sets of diagnostic, child, and clinical characteristics, employing a robust ensemble machine learning-based procedure. This procedure estimates the child-level expected benefit for a given set of covariates by combining predictions from a library of statistical models. For each rule, we estimate the proportion treated under the rule and the average benefits of treatment. Among 6692 children, treatment under the most comprehensive rule is recommended on average for one third of children. The risk of diarrhea on day 3 is 10.1% lower (95% CI: 5.4, 14.9) with azithromycin compared to placebo among children recommended for treatment (NNT: 10). For day 90 re-hospitalization and death, risk is 2.4% lower (95% CI: 0.6, 4.1; NNT: 42). While pathogen diagnostics are strong determinants of azithromycin effects on diarrhea duration, host characteristics may better predict benefits for re-hospitalization or death. This suggests that targeting antibiotic treatment for severe outcomes among children with watery diarrhea may be possible without access to pathogen diagnostics.

Date: 2025
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DOI: 10.1038/s41467-025-60682-9

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