ArticleScience translational medicine2020
A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection.
Article in Science translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 4 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
52 citing papers in PubMed, 4 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- Artificial intelligence for infection surveillance, risk stratification, and antimicrobial decision support in acute-care hospitals: a scoping review.Frontiers in artificial intelligence · 2026Pooled it
- Pooled it
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Machine learning enables prediction of metabolic system evolution in bacteria.Science advances · 2023Pooled it
- Combating antimicrobial resistance through multimodal data via artificial intelligence.One health (Amsterdam, Netherlands) · 2026Review
- Point-Counterpoint: The "obsoletogram"-are annual hospital antibiograms still relevant?Journal of clinical microbiology · 2026Article
- Article
- From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.Frontiers in public health · 2026Article
- Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions.Journal of general internal medicine · 2026Article
- Exploring opportunities to improve health equity with machine learning and artificial intelligence in healthcare epidemiology.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2026Review
- Data limitations hinder the development of AI-based decision support for the treatment of antibiotic-resistant bacteria.Future microbiology · 2025Article
- Monitoring strategies for continuous evaluation of deployed clinical prediction models.Journal of biomedical informatics · 2025Article
- Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap.NPJ digital medicine · 2025Review
- Artificial Intelligence in Infectious Diseases:Sultan Qaboos University medical journal · 2025Article
- Integrating socioeconomic deprivation indices and electronic health record data to predict antimicrobial resistance.npj antimicrobials and resistance · 2025Article
- Antibiotics and Artificial Intelligence: Clinical Considerations on a Rapidly Evolving Landscape.Infectious diseases and therapy · 2025Article
- Machine learning to predict urine culture antibiotic sensitivities in the emergency department.Heliyon · 2025Article
- Predicting appropriateness of antibiotic treatment among ICU patients with hospital-acquired infection.NPJ digital medicine · 2025Article
- Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.The Journal of infection · 2025Article
- The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review.Antibiotics (Basel, Switzerland) · 2025Review
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Antibiotic resistance is a major cause of treatment failure and leads to increased use of broad-spectrum agents, which begets further resistance. This vicious cycle is epitomized by uncomplicated urinary tract infection (UTI), which affects one in two women during their life and is associated with increasing antibiotic resistance and high rates of prescription for broad-spectrum second-line agents. To address this, we developed machine learning models to predict antibiotic susceptibility using electronic health record data and built a decision algorithm for recommending the narrowest possible antibiotic to which a specimen is susceptible. When applied to a test cohort of 3629 patients presenting between 2014 and 2016, the algorithm achieved a 67% reduction in the use of second-line antibiotics relative to clinicians. At the same time, it reduced inappropriate antibiotic therapy, defined as the choice of a treatment to which a specimen is resistant, by 18% relative to clinicians. For specimens where clinicians chose a second-line drug but the algorithm chose a first-line drug, 92% (1066 of 1157) of decisions ended up being susceptible to the first-line drug. When clinicians chose an inappropriate first-line drug, the algorithm chose an appropriate first-line drug 47% (183 of 392) of the time. Our machine learning decision algorithm provides antibiotic stewardship for a common infectious syndrome by maximizing reductions in broad-spectrum antibiotic use while maintaining optimal treatment outcomes. Further work is necessary to improve generalizability by training models in more diverse populations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.