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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.
Insights
Machine learning identified personalized strategies to target azithromycin for children with watery diarrhea. This approach effectively reduced diarrhea and severe outcomes like re-hospitalization and death in targeted groups.
Area of Science:
- Pediatric infectious diseases
- Clinical trial methodology
- Machine learning in healthcare
Background:
- Watery diarrhea is a significant cause of childhood morbidity and mortality globally.
- Azithromycin has shown efficacy in treating certain types of diarrhea, but optimal targeting is needed.
- Personalized medicine approaches can improve antibiotic effectiveness and reduce resistance.
Purpose of the Study:
- To develop and validate personalized treatment rules for azithromycin in children with watery diarrhea using machine learning.
- To identify child and clinical characteristics that predict benefit from azithromycin treatment.
- To assess the impact of targeted azithromycin therapy on diarrhea duration and severe outcomes.
Main Methods:
- Utilized data from a randomized trial of azithromycin for watery diarrhea.
- Employed an ensemble machine learning procedure to develop personalized treatment rules based on diagnostic, child, and clinical characteristics.
- Estimated treatment proportions and average benefits for each derived rule.
Main Results:
- Personalized treatment rules recommended azithromycin for approximately one-third of 6,692 children.
- Targeted azithromycin reduced diarrhea risk by 10.1% and re-hospitalization/death risk by 2.4% compared to placebo.
- Host characteristics were more predictive of benefits for severe outcomes than pathogen diagnostics.
Conclusions:
- Machine learning can identify children most likely to benefit from azithromycin for watery diarrhea.
- Targeting antibiotic treatment based on host characteristics may be feasible without pathogen diagnostics.
- Personalized azithromycin strategies hold promise for improving pediatric diarrhea management.
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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