ArticleFrontiers in public health2022
A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 3 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
- Host-Bacterium Interaction Mechanisms inInternational journal of molecular sciences · 2023Pooled it
- Carbapenem-Resistant Enterobacterales (CRE) in Europe: Surveillance Challenges, Emerging Threats and Public Health Preparedness.Antibiotics (Basel, Switzerland) · 2026Review
- ICU Admission Screening for Multidrug-Resistant Organism Colonization and Subsequent Infection Risk: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Explainable AI for infection prevention and control: modeling CPE acquisition and patient outcomes in an Irish hospital with transformers.BMC medical informatics and decision making · 2025Article
- Machine Learning Models to Establish the Risk of Being a Carrier of Multidrug-Resistant Bacteria upon Admission to the ICU.Antibiotics (Basel, Switzerland) · 2025Article
- Review
- Artificial Intelligence in Bacterial Infections Control: A Scoping Review.Antibiotics (Basel, Switzerland) · 2025Review
- Artificial intelligence in hospital infection prevention: an integrative review.Frontiers in public health · 2025Review
- The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem Resistance: A Comprehensive Review.Antibiotics (Basel, Switzerland) · 2024Review
- Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- A deep learning model for predicting multidrug-resistant organism infection in critically ill patients.Journal of intensive care · 2023Article
- Preventing Multidrug-Resistant Bacterial Transmission in the Intensive Care Unit with a Comprehensive Approach: A Policymaking Manual.Antibiotics (Basel, Switzerland) · 2023Review
- Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study.Health data science · 2023Article
- Infective Endocarditis in High-Income Countries.Metabolites · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Background: The rising prevalence of multi-drug resistant organisms (MDROs), such as Methicillin-resistant Materials and Methods: Leveraging data from electronic healthcare records and a unique MDRO universal screening program, we developed a data-driven modeling framework to predict MRSA, VRE, and CRE colonization upon intensive care unit (ICU) admission, and identified the associated socio-demographic and clinical factors using logistic regression (LR), random forest (RF), and XGBoost algorithms. We performed threshold optimization for converting predicted probabilities into binary predictions and identified the cut-off maximizing the sum of sensitivity and specificity. Results: Four thousand six hundred seventy ICU admissions (3,958 patients) were examined. MDRO colonization rate was 17.59% (13.03% VRE, 1.45% CRE, and 7.47% MRSA). Our study achieved the following sensitivity and specificity values with the best performing models, respectively: 80% and 66% for VRE with LR, 73% and 77% for CRE with XGBoost, 76% and 59% for MRSA with RF, and 82% and 83% for MDRO (i.e., VRE or CRE or MRSA) with RF. Further, we identified several predictors of MDRO colonization, including long-term care facility stay, current diagnosis of skin/subcutaneous tissue or infectious/parasitic disease, and recent isolation precaution procedures before ICU admission. Conclusion: Our data-driven modeling framework can be used as a clinical decision support tool for timely predictions, characterization and identification of high-risk patients, and selective and timely use of infection control measures in ICUs.
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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.