ArticleOpen forum infectious diseases2022
Real-world Antimicrobial Stewardship Experience in a Large Academic Medical Center: Using Statistical and Machine Learning Approaches to Identify Intervention "Hotspots" in an Antibiotic Audit and Feedback Program.
Article in Open forum infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025Pooled it
- Personalized Antibiogram: A Novel Multitask Machine Learning Framework for Simultaneous Prediction of Antimicrobial Resistance Profile With Enhanced Detection of Carbapenem Resistance in Enterobacteriaceae.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026Article
- Supervised Machine Learning to Identify Hospital Inpatients Needing a Change of Antibiotic Therapy in Real Time: Preclinical Diagnostic Evaluation and Feasibility Study.Open forum infectious diseases · 2026Article
- Comparative Evaluation and Performance of Large Language Models in Clinical Infection Control Scenarios: A Benchmark Study.Healthcare (Basel, Switzerland) · 2025Article
- Prospective Audit and Feedback of Targeted Antimicrobials Use at a Tertiary Care Hospital in the United Arab Emirates.Antibiotics (Basel, Switzerland) · 2025Article
- A machine learning model to predict optimal antibiotic use in hospital medicine patients.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Applications of Machine Learning on Electronic Health Record Data to Combat Antibiotic Resistance.The Journal of infectious diseases · 2024Review
- Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship-A Systematic Review.Antibiotics (Basel, Switzerland) · 2024Review
- Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It.Open forum infectious diseases · 2024Review
- Improving the Efficiency of Antimicrobial Stewardship Action in Acute Care Facilities.Open forum infectious diseases · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Background: Prospective audit with feedback (PAF) is an impactful strategy for antimicrobial stewardship program (ASP) activities. However, because PAF requires reviewing large numbers of antimicrobial orders on a case-by-case basis, PAF programs are highly resource intensive. The current study aimed to identify predictors of ASP intervention (ie, feedback) and to build models to identify orders that can be safely bypassed from review, to make PAF programs more efficient. Methods: We performed a retrospective cross-sectional study of inpatient antimicrobial orders reviewed by the University of Maryland Medical Center's PAF program between 2017 and 2019. We evaluated the relationship between antimicrobial and patient characteristics with ASP intervention using multivariable logistic regression models. Separately, we built prediction models for ASP intervention using statistical and machine learning approaches and evaluated performance on held-out data. Results: Across 17 503 PAF reviews, 4219 (24%) resulted in intervention. In adjusted analyses, a clinical pharmacist on the ordering unit or receipt of an infectious disease consult were associated with 17% and 56% lower intervention odds, respectively (adjusted odds ratios [aORs], 0.83 and 0.44; Conclusions: Prediction models substantially reduced PAF review caseloads while maintaining high sensitivities. Our results and approach may offer a blueprint for other ASPs.
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