ArticleAntimicrobial agents and chemotherapy2020
Prediction of Antibiotic Susceptibility for Urinary Tract Infection in a Hospital Setting.
Article in Antimicrobial agents and chemotherapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Pooled it
- Optimal antibiotic use in the intensive care unit.Critical care (London, England) · 2025Review
- Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.The Journal of infection · 2025Article
- Risk factors for urological infection after laparoscopic ureterolithotomy.American journal of translational research · 2025Article
- Development of Predictive Models to Inform a Novel Risk Categorization Framework for Antibiotic Resistance in Escherichia coli-Caused Uncomplicated Urinary Tract Infection.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Article
- Prediction of Antibiotic Resistance in Patients With a Urinary Tract Infection: Algorithm Development and Validation.JMIR medical informatics · 2024Article
- Enhancing Antibiotic Stewardship: A Machine Learning Approach to Predicting Antibiotic Resistance in Inpatient Care.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review.Antibiotics (Basel, Switzerland) · 2023Review
- BSAC Vanguard Series: Artificial intelligence and antibiotic stewardship.The Journal of antimicrobial chemotherapy · 2022Article
- Diagnostic value of different urine tests for urinary tract infection: a systematic review and meta-analysis.Translational andrology and urology · 2022Article
- Personalized antibiograms for machine learning driven antibiotic selection.Communications medicine · 2022Article
- A "resistance calculator": Simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals.Infection control and hospital epidemiology · 2021Article
- Amplifying Domain Expertise in Clinical Data Pipelines.JMIR medical informatics · 2020Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Empiric antibiotic prescribing can be supported by guidelines and/or local antibiograms, but these have limitations. We sought to use data from a comprehensive electronic health record to use statistical learning to develop predictive models for individual antibiotics that incorporate patient- and hospital-specific factors. This paper reports on the development and validation of these models with a large retrospective cohort. This was a retrospective cohort study including hospitalized patients with positive urine cultures in the first 48 h of hospitalization at a 1,500-bed tertiary-care hospital over a 4.5-year period. All first urine cultures with susceptibilities were included. Statistical learning techniques, including penalized logistic regression, were used to create predictive models for cefazolin, ceftriaxone, ciprofloxacin, cefepime, and piperacillin-tazobactam. These were validated on a held-out cohort. The final data set used for analysis included 6,366 patients. Final model covariates included demographics, comorbidity score, recent antibiotic use, recent antimicrobial resistance, and antibiotic allergies. Models had acceptable to good discrimination in the training data set and acceptable performance in the validation data set, with a point estimate for area under the receiver operating characteristic curve (AUC) that ranged from 0.65 for ceftriaxone to 0.69 for cefazolin. All models had excellent calibration. We used electronic health record data to create predictive models to estimate antibiotic susceptibilities for urinary tract infections in hospitalized patients. Our models had acceptable performance in a held-out validation cohort.
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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 Socratic graph.