ArticleNPJ digital medicine2025
Predicting appropriateness of antibiotic treatment among ICU patients with hospital-acquired infection.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Clinical Characteristics, Empirical Antibiotic Appropriateness, and Outcomes of Gram-Positive Versus Gram-Negative Bacteremia in Emergency Department Patients with Sepsis.Medicina (Kaunas, Lithuania) · 2026Article
- Impact of appropriate antimicrobial therapy on patient outcomes and antimicrobial use: a sub analysis of the DIANA Study Dataset.Intensive care medicine · 2026Article
- ASP-COMPLEX: redefining antimicrobial stewardship to improve outcomes in high-risk patients with severe infections.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2026Review
- Association of a Hospital-Wide Integrated Stewardship Intervention with Hospital-Acquired Multidrug-Resistant Organism Infection Incidence Density: A Large-Scale Interrupted Time-Series Study.Antibiotics (Basel, Switzerland) · 2026Article
- Next-Generation Target Discovery in ESKAPE Pathogens: An AI-Driven Framework from Omics-Based to Systems-Level Modeling and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Visible Photosensitizing Disinfectant Spray for Combating Multidrug-ResistantExploration (Beijing, China) · 2026Article
- An explainable machine learning approach to predicting carbapenem resistance inFrontiers in cellular and infection microbiology · 2026Article
- Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.Frontiers in medicine · 2026Review
- Distribution and antimicrobial resistance profiles of common blood culture isolates in a maternal and child health specialty hospital, Shenzhen (January 2021-October 2025), China.Frontiers in microbiology · 2026Article
- Role of artificial intelligence in ICU therapeutic decision-making for severe infections.Current opinion in critical care · 2025Review
- Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management.Diagnostics (Basel, Switzerland) · 2025Review
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Antimicrobial resistance is a rising global health threat, leading to ineffective treatments, increased mortality and rising healthcare costs. In ICUs, inappropriate empiric antibiotic therapy is often given due to treatment urgency, causing poor outcomes. This study developed a machine learning model to predict the appropriateness of empiric antibiotics for ICU-acquired bloodstream infections, using data from the MIMIC-III database. To address missing values and dataset imbalances, novel computational methods were introduced. The model achieved an AUROC of 77.3% and AUPRC of 40.4% on validation, with similar results on external datasets from MIMIC-IV and Rambam Hospital. The model also predicted mortality risk, identifying a 30% mortality rate in high-risk patients versus 16.8% in low-risk groups. External validation on the eICU database showed a comparable gap, with mortality rates at 24% for high-risk and 7.7% for low-risk groups. Our study demonstrates the potential of machine learning models to predict inappropriate empiric antibiotic treatment.
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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.