ArticleTherapeutic advances in infectious disease2017
Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population.
Article in Therapeutic advances in infectious disease, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Development and Prospective Validation of a Machine Learning Tool for Predicting Multidrug-resistant Organism Infections in an Indian Critical Care Unit.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Differential Profiles of intensive care unit multidrug-resistant patients: Influence of prior antibiotic therapy on clinical features.Pakistan journal of medical sciences · 2025Article
- Tracking Epidemiological Characteristics and Risk Factors of Multi-Drug Resistant Bacteria in Intensive Care Units.Infection and drug resistance · 2023Article
- Derivation and validation of a simple score to predict the presence of bacteria requiring carbapenem treatment in ICU-acquired bloodstream infection and pneumonia: CarbaSCORE.Antimicrobial resistance and infection control · 2019Article
- A Study of Multidrug-Resistant, Colistin-Only-Sensitive Infections in Intubated and Mechanically Ventilated Patients Over 2 Years.Journal of global infectious diseasesArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo create a model predictive of an individual's risk of developing a
methodsThis is a case-control study in which 189 ICU patients diagnosed with their first infection with an MDR organism were compared on the basis of demographic, past medical and clinical variables to randomly selected ICU patients without such an infection, era-matched in a 2:1 ratio. A prediction tool was derived using multivariate logistic regression.
resultsFive features remained predictive of developing an infection with a drug-resistant pathogen: hospitalization within a year [adjusted odds ratio (OR) 2.14], chronic hemodialysis (3.86), underlying oxygen-dependent pulmonary disease (1.86), endotracheal intubation within 24 h (2.46) and reason for ICU admission (respiratory failure 2.89, non-respiratory failure, non-shock presentation 1.85). Using a scoring system (0-7 points) based on the adjusted OR, risk categories were derived (low: 0-2 points, intermediate: 3-4 points and high risk: 5-7 points). The negative predictive value at a score cutoff of 2 is excellent (88.9%).
conclusionsA clinical prediction rule comprised of five easily measured ICU variables reasonably discriminates between patients who will develop their first MDR infection versus those who will not.
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Registered trials
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