ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2016
A Clinical Decision Tree to Predict Whether a Bacteremic Patient Is Infected With an Extended-Spectrum β-Lactamase-Producing Organism.
Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04699981 (Can the Relative Fecal Abundance of BLSE and the Digestive Microbiota be Predictive of the Risk of Infection in a Carrier Patient?), which is not on this map. Cited by 87 papers, 3 of them syntheses that pooled it.
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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.
Can the Relative Fecal Abundance of BLSE and the Digestive Microbiota be Predictive of the Risk of Infection in a Carrier Patient?
Who cites it
87 citing papers in PubMed, 3 syntheses or guidelines pooled it, 173 citations in OpenAlex.
- Pooled it
- Pooled it
- Surviving sepsis campaign international guidelines for the management of septic shock and sepsis-associated organ dysfunction in children.Intensive care medicine · 2020Guideline
- Trial
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- Risk factors for positive urine culture and antimicrobial resistance in suspected UTI after flexible ureteroscopic lithotripsy.Frontiers in surgery · 2026Article
- Beyond bacteremia: clinical phenotypes and determinants of mortality in hospitalized adults with community-acquired urinary tract infection.Frontiers in medicine · 2026Article
- From Raw Hospital Records to an AI-Ready Surveillance Dataset: A FAIR-Compliant Data Pipeline for Healthcare-Associated Infection Research in a Chinese District Hospital.Computational and structural biotechnology journal · 2026Article
- Role of artificial intelligence in ICU therapeutic decision-making for severe infections.Current opinion in critical care · 2025Review
- Clinical and Demographic Features Associated with the Isolation of Ceftriaxone-Resistant Enterobacteriaceae in a Private Hospital in Kingston, Jamaica.The American journal of tropical medicine and hygiene · 2025Article
- Predicting appropriateness of antibiotic treatment among ICU patients with hospital-acquired infection.NPJ digital medicine · 2025Article
- Prioritizing isolation precautions: a patient-centered approach to infection prevention and control.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Review
- Machine Learning-Based Prediction Model for Multidrug-Resistant Organisms Infections: Performance Evaluation and Interpretability Analysis.Infection and drug resistance · 2025Article
- Identification and validation of a risk assessment scoring tool for extended-spectrum beta-lactamase-producing Enterobacterales bacteremia at a tertiary teaching hospital.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
- Performance of Risk Models for Antimicrobial Resistance in Adult Patients With Sepsis.JAMA network open · 2024Article
- Retrospective validation study of a machine learning-based software for empirical and organism-targeted antibiotic therapy selection.Antimicrobial agents and chemotherapy · 2024Observational
- Epidemiology ofAntimicrobial agents and chemotherapy · 2024Article
- Laboratory Tests, Bacterial Resistance, and Treatment Options in Adult Patients Hospitalized with a Suspected Urinary Tract Infection.Diagnostics (Basel, Switzerland) · 2024Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
27 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundTimely identification of extended-spectrum β-lactamase (ESBL) bacteremia can improve clinical outcomes while minimizing unnecessary use of broad-spectrum antibiotics, including carbapenems. However, most clinical microbiology laboratories currently require at least 24 additional hours from the time of microbial genus and species identification to confirm ESBL production. Our objective was to develop a user-friendly decision tree to predict which organisms are ESBL producing, to guide appropriate antibiotic therapy.
methodsWe included patients ≥18 years of age with bacteremia due to Escherichia coli or Klebsiella species from October 2008 to March 2015 at Johns Hopkins Hospital. Isolates with ceftriaxone minimum inhibitory concentrations ≥2 µg/mL underwent ESBL confirmatory testing. Recursive partitioning was used to generate a decision tree to determine the likelihood that a bacteremic patient was infected with an ESBL producer. Discrimination of the original and cross-validated models was evaluated using receiver operating characteristic curves and by calculation of C-statistics.
resultsA total of 1288 patients with bacteremia met eligibility criteria. For 194 patients (15%), bacteremia was due to a confirmed ESBL producer. The final classification tree for predicting ESBL-positive bacteremia included 5 predictors: history of ESBL colonization/infection, chronic indwelling vascular hardware, age ≥43 years, recent hospitalization in an ESBL high-burden region, and ≥6 days of antibiotic exposure in the prior 6 months. The decision tree's positive and negative predictive values were 90.8% and 91.9%, respectively.
conclusionsOur findings suggest that a clinical decision tree can be used to estimate a bacteremic patient's likelihood of infection with ESBL-producing bacteria. Recursive partitioning offers a practical, user-friendly approach for addressing important diagnostic questions.
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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.