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[Preprint]. 2024 Oct 28:2024.10.27.24316217.

doi: 10.1101/2024.10.27.24316217.

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

Sara S Kim¹, Allison Codi², James A Platts-Mills³, Patricia Pavlinac^{4

5}, Karim Manji⁶, Chris Sudfeld⁷, Christopher P Duggan^{7

8}, Queen Dube⁹, Naor Bar-Zeev¹⁰, Karen Kotloff^{11

12}, Samba O Sow¹³, Sunil Sazawal¹⁴, Benson O Singa^{15

16}, Judd Walson^{15

17}, Farah Qamar¹⁸, Tahmeed Ahmed¹⁹, Ayesha De Costa²⁰, David Benkeser², Elizabeth T Rogawski McQuade¹

Affiliations

¹ Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
² Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
³ Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, VA, USA.
⁴ Department of Global Health, University of Washington, Seattle, WA, USA.
⁵ Department of Epidemiology, University of Washington, Seattle, WA, USA.
⁶ Department of Pediatrics and Child Health, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.
⁷ Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁸ Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, MA, USA.
⁹ Department of Pediatrics, Queen Elizabeth Central Hospital, Blantyre, Malawi.
¹⁰ International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, ML, USA.
¹¹ Department of Pediatrics, Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore.
¹² Department of Medicine, Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, ML, USA.
¹³ Centre pour le Développement des Vaccins, Bamako, Mali.
¹⁴ Center for Public Health Kinetics, New Delhi, Delhi, India.
¹⁵ Childhood Acute Illness and Nutrition Network, Nairobi, Kenya.
¹⁶ Kenya Medical Research Institute, Nairobi, Kenya.
¹⁷ Departments of International Heath, Medicine and Pediatrics, Johns Hopkins University, Baltimore, MD, USA.
¹⁸ Department of Pediatrics and Child Heath, Aga Khan University, Karachi, Pakistan.
¹⁹ Nutrition and Clinical Services Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
²⁰ Department of Maternal, Child, and Adolescent Health and Aging, World Health Organization, Geneva, Switzerland.

PMID: 39574840
PMCID: PMC11581062
DOI: 10.1101/2024.10.27.24316217

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

Sara S Kim et al. medRxiv. 2024.

[Preprint]. 2024 Oct 28:2024.10.27.24316217.

doi: 10.1101/2024.10.27.24316217.

Authors

Affiliations

¹ Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
² Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
³ Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, VA, USA.
⁴ Department of Global Health, University of Washington, Seattle, WA, USA.
⁵ Department of Epidemiology, University of Washington, Seattle, WA, USA.
⁶ Department of Pediatrics and Child Health, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.
⁷ Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁸ Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, MA, USA.
⁹ Department of Pediatrics, Queen Elizabeth Central Hospital, Blantyre, Malawi.
¹⁰ International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, ML, USA.
¹¹ Department of Pediatrics, Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore.
¹² Department of Medicine, Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, ML, USA.
¹³ Centre pour le Développement des Vaccins, Bamako, Mali.
¹⁴ Center for Public Health Kinetics, New Delhi, Delhi, India.
¹⁵ Childhood Acute Illness and Nutrition Network, Nairobi, Kenya.
¹⁶ Kenya Medical Research Institute, Nairobi, Kenya.
¹⁷ Departments of International Heath, Medicine and Pediatrics, Johns Hopkins University, Baltimore, MD, USA.
¹⁸ Department of Pediatrics and Child Heath, Aga Khan University, Karachi, Pakistan.
¹⁹ Nutrition and Clinical Services Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
²⁰ Department of Maternal, Child, and Adolescent Health and Aging, World Health Organization, Geneva, Switzerland.

PMID: 39574840
PMCID: PMC11581062
DOI: 10.1101/2024.10.27.24316217

Update in

Personalized azithromycin treatment rules for children with watery diarrhea using machine learning.
Kim SS, Codi A, Platts-Mills JA, Pavlinac PB, Manji K, Sudfeld CR, Duggan CP, Dube Q, Bar-Zeev N, Kotloff K, Sow SO, Sazawal S, Singa BO, Walson J, Qamar F, Ahmed T, De Costa A, Benkeser D, Rogawski McQuade ET. Kim SS, et al. Nat Commun. 2025 Jul 1;16(1):5968. doi: 10.1038/s41467-025-60682-9. Nat Commun. 2025. PMID: 40592849 Free PMC article. Clinical Trial.

Abstract

Introduction: We used machine learning to identify novel strategies to target azithromycin to the children with watery diarrhea who are most likely to benefit.

Methods: Using data from a randomized trial of azithromycin for watery diarrhea, we developed personalized treatment rules given sets of diagnostic, child, and clinical characteristics, employing a robust ensemble machine learning-based procedure. For each rule, we estimated the proportion treated under the rule and the average benefits of treatment.

Results: Among 6,692 children, treatment was recommended on average for approximately one third of children. The risk of diarrhea on day 3 was 10.1% lower (95% CI: 5.4, 14.9) with azithromycin compared to placebo among children recommended for treatment. For day 90 re-hospitalization and death, risk was 2.4% lower (95% CI: 0.6, 4.1) with azithromycin compared to placebo among those recommended for treatment. While pathogen diagnostics were strong determinants of azithromycin effects on diarrhea duration, host characteristics were more relevant for predicting benefits for re-hospitalization or death.

Conclusion: The ability of host characteristics to predict which children benefit from azithromycin with respect to the most severe outcomes suggests appropriate targeting of antibiotic treatment among children with watery diarrhea may be possible without access to pathogen diagnostics.

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Conflict of interest statement

Competing interests The authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the interpretation of the article.

Figures

**Figure 1:**
Comparison of average treatment benefit among those recommended for treatment and not recommended for treatment under each rule. The proportion recommended treatment is denoted in parentheses after the label for each rule.

**Figure 2:**
Concordant correlation coefficient (CCC) between child-level expected benefits from the comprehensive and alternative treatment rules.

**Figure 3:**
Proportion treated and average benefit (risk difference, RD) among those recommended for azithromycin under the comprehensive rule based on all known child and clinical characteristics with varying thresholds of clinical benefit to define the rule.

See this image and copyright information in PMC

References

1. Troeger C, Blacker BF, Khalil IA, Rao PC, Cao S, Zimsen SR, et al. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of diarrhoea in 195 countries: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Infect Dis. 2018. Nov 1;18(11):1211–28. - PMC - PubMed
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1. Kotloff KL, Nataro JP, Blackwelder WC, Nasrin D, Farag TH, Panchalingam S, et al. Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, case-control study. The Lancet. 2013. Jul 20;382(9888):209–22. - PubMed
1. Nasrin D, Blackwelder WC, Sommerfelt H, Wu Y, Farag TH, Panchalingam S, et al. Pathogens Associated With Linear Growth Faltering in Children With Diarrhea and Impact of Antibiotic Treatment: The Global Enteric Multicenter Study. J Infect Dis. 2021. Sep 16;224(Suppl 7):S848–55. - PMC - PubMed
1. Rogawski ET, Liu J, Platts-Mills JA, Kabir F, Lertsethtakarn P, Siguas M, et al. Use of quantitative molecular diagnostic methods to investigate the effect of enteropathogen infections on linear growth in children in low-resource settings: longitudinal analysis of results from the MAL-ED cohort study. Lancet Glob Health. 2018. Dec 1;6(12):e1319–28. - PMC - PubMed

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This is a preprint.

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

Affiliations

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

Authors

Affiliations

Update in

Abstract

Conflict of interest statement

Figures

References

Publication types

Grants and funding

LinkOut - more resources

Full Text Sources