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

Author

Listed:
  • Sara S. Kim

    (Emory University)

  • Allison Codi

    (Emory University)

  • James A. Platts-Mills

    (University of Virginia)

  • Patricia B. Pavlinac

    (University of Washington
    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
    Boston Children’s Hospital)

  • Queen Dube

    (Queen Elizabeth Central Hospital)

  • Naor Bar-Zeev

    (Johns Hopkins Bloomberg School of Public Health)

  • Karen Kotloff

    (University of Maryland School of Medicine
    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
    Kenya Medical Research Institute)

  • Judd Walson

    (Childhood Acute Illness and Nutrition Network
    Johns Hopkins University)

  • 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)

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.

Suggested Citation

  • 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 & S, 2025. "Personalized azithromycin treatment rules for children with watery diarrhea using machine learning," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60682-9
    DOI: 10.1038/s41467-025-60682-9
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    References listed on IDEAS

    as
    1. Hubbard Alan E. & Kherad-Pajouh Sara & van der Laan Mark J., 2016. "Statistical Inference for Data Adaptive Target Parameters," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 3-19, May.
    2. Chambaz Antoine & Hubbard Alan & van der Laan Mark J., 2016. "Special Issue on Data-Adaptive Statistical Inference," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 1-1, May.
    3. van der Laan Mark J. & Luedtke Alexander R., 2015. "Targeted Learning of the Mean Outcome under an Optimal Dynamic Treatment Rule," Journal of Causal Inference, De Gruyter, vol. 3(1), pages 61-95.
    4. Ben J Brintz & Joel I Howard & Benjamin Haaland & James A Platts-Mills & Tom Greene & Adam C Levine & Eric J Nelson & Andrew T Pavia & Karen L Kotloff & Daniel T Leung, 2020. "Clinical predictors for etiology of acute diarrhea in children in resource-limited settings," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(10), pages 1-14, October.
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