ArticleNature medicine2019
Personal clinical history predicts antibiotic resistance of urinary tract infections.
Article in Nature medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers, 4 of them syntheses that pooled it.
What it found
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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
108 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- 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
- Pooled it
- Early Prediction of Carbapenem Resistance in Gram-Negative Organisms at Culture Ordering from Routinely Collected Hospital Data: Temporal and External Validation of ORDER-CARB.Infectious diseases and therapy · 2026Article
- Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation.BJUI compass · 2026Article
- Physician adoption patterns of AI-driven clinical decision support systems in urinary tract infection management.Scientific reports · 2026Article
- AI-nanotech synergies: recent advances in sustainable bio-manufacturing.World journal of microbiology & biotechnology · 2026Review
- Asymmetric Cu─N─Ru Bridgedsite Nanozyme-Loaded Injectable Thermogel Boosts Cuproptosis-Like Death for Multidrug-Resistant Urinary Tract Infections.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multi task learning based early prediction model for antibiotic resistance using multi institutional cohort data.Scientific reports · 2026Article
- Article
- Fabrication, Characterization, and Antibacterial Performance of Polyvinyl Alcohol (PVA)-Zinc Oxide (ZnO) Thin Films for Packaging Applications.Applied biochemistry and biotechnology · 2026Article
- Linezolid-resistant and vancomycin-resistantASM case reports · 2026Article
- An AI-Driven Antimicrobial Resistance Surveillance Framework for Low-Resource Settings: A Pilot Study from Mogadishu, Somalia.Infection and drug resistance · 2026Article
- The evolution of diagnostic microbiology: integrating culture-based methods and genomic advances.PeerJ · 2026Review
- Development and validation of a machine learning-based early warning model for carbapenem-resistantFrontiers in microbiology · 2026Article
- Surveillance-Driven Machine Learning for Prediction of Antimicrobial Susceptibility: An Explainable Modeling Framework using the Pfizer ATLAS Dataset (2004 - 2023).Wellcome open research · 2026Article
- Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.World journal of methodology · 2025Review
- Dynamical memory underlies prolonged plasmid persistence after transient antibiotic treatment.bioRxiv : the preprint server for biology · 2025Article
- Role of artificial intelligence in ICU therapeutic decision-making for severe infections.Current opinion in critical care · 2025Review
48 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed 'empirically', in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10-year longitudinal data set of over 700,000 community-acquired urinary tract infections with over 5,000,000 individually resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine-learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match an antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a 1-year test period, we find that they greatly reduce the risk of mismatched treatment compared with the current standard of care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments.
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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.