ArticleOpen forum infectious diseases2018
The Rapid Prediction of Carbapenem Resistance in Patients With
Article in Open forum infectious diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Pooled it
- A public health risk model using prior healthcare exposures identifies healthcare-associated pathogen carriage.Infection control and hospital epidemiology · 2026Article
- Machine Learning Model for Predicting Multidrug Resistance in ClinicalDiagnostics (Basel, Switzerland) · 2026Article
- An explainable machine learning approach to predicting carbapenem resistance inFrontiers in cellular and infection microbiology · 2026Article
- The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem Resistance: A Comprehensive Review.Antibiotics (Basel, Switzerland) · 2024Review
- Proof of concept study on early forecasting of antimicrobial resistance in hospitalized patients using machine learning and simple bacterial ecology data.Scientific reports · 2024Article
- Scoping review of risk-scoring tools for early prediction of bloodstream infections caused by carbapenem-resistant Enterobacterales: do we really have a reliable risk-scoring tool?JAC-antimicrobial resistance · 2024Article
- Clinical Risk Scores to Predict Nonsusceptibility to Trimethoprim-Sulfamethoxazole, Fluoroquinolone, Nitrofurantoin, and Third-Generation Cephalosporin Among Adult Outpatient Episodes of Complicated Urinary Tract Infection.Open forum infectious diseases · 2023Article
- Machine learning model for predicting ciprofloxacin resistance and presence of ESBL in patients with UTI in the ED.Scientific reports · 2023Article
- Association between Timing of Colonization and Risk of Developing Klebsiella pneumoniae Carbapenemase-Producing K. pneumoniae Infection in Hospitalized Patients.Microbiology spectrum · 2022Article
- A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.Frontiers in public health · 2022Article
- A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection.Science translational medicine · 2020Article
- Review
- Early Detection of Bacteraemia Using Ten Clinical Variables with an Artificial Neural Network Approach.Journal of clinical medicine · 2019Article
- Development of a bedside tool to predict the probability of drug-resistant pathogens among hospitalized adult patients with gram-negative infections.BMC infectious diseases · 2019Observational
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe administration of active antibiotics is often delayed in cases of carbapenem-resistant gram-negative bacteremia. Using electronic medical record (EMR) data to rapidly predict carbapenem resistance in patients with
methodsAll cases of
resultsA total of 613 cases of
conclusionsA multiple logistic regression model using EMR data can generate rapid, sensitive predictions of carbapenem resistance in patients with
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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.