Observational studyBMJ open2022
Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study.
Observational study in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06163781 (Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning), which is not on this map. Cited by 17 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.
Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning: a Randomized Controlled Trial
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Diagnostic Performance of Machine Learning Models for Predicting Bloodstream Infections in Large Cohorts: A Systematic Review and Meta-analysis.Infectious diseases and therapy · 2026Pooled it
- Risk Factors of Blood Culture Positivity and Mortality in Bloodstream Infections: A Population-Based Study of Patients With B-Cell Malignancies.European journal of haematology · 2026Article
- Evaluation of machine learning pipeline for blood culture outcome prediction on prospectively collected emergency department data.Journal of medical microbiology · 2026Article
- Low-Value Blood Culture Use in Adult Emergency Department Patients: A Scoping Review.Emergency medicine Australasia : EMA · 2026Article
- Reference intervals reimagined with IRIS for earlier detection and better disease monitoring.Scientific reports · 2026Article
- A protocol for retrospective evaluation of potential patient-level harms of an AI tool for blood culture stewardship.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2026Article
- An open-source, configurable machine learning pipeline for predicting blood culture outcomes from routine haematology parameters.Access microbiology · 2026Article
- Early prediction of antibiotic need and bacteremia risk in non-immunocompromised pediatric emergency patients using machine learning.Pediatric research · 2025Article
- Machine learning based analysis of leucocyte cell population data by Sysmex XN series hematology analyzer for the diagnosis of bacteremia.Scientific reports · 2025Article
- Letter to the Editor regarding "blood culture bottle shortage mitigation efforts: analysis of impact on ordering and patient impact" by Doern et al.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.PLOS digital health · 2024Article
- Article
- Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning.BMC medical informatics and decision making · 2024Article
- Assessing sepsis-induced immunosuppression to predict positive blood cultures.Frontiers in immunology · 2024Article
- Assessing a standardized decision-making algorithm for blood culture collection in the intensive care unit.Journal of critical care · 2023Article
- Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms.Clinics (Sao Paulo, Brazil) · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop predictive models for blood culture (BC) outcomes in an emergency department (ED) setting.
designRetrospective observational study.
settingED of a large teaching hospital in the Netherlands between 1 September 2018 and 24 June 2020.
participantsAdult patients from whom BCs were collected in the ED. Data of demographic information, vital signs, administered medications in the ED and laboratory and radiology results were extracted from the electronic health record, if available at the end of the ED visits.
main outcome measuresThe primary outcome was the performance of two models (logistic regression and gradient boosted trees) to predict bacteraemia in ED patients, defined as at least one true positive BC collected at the ED.
resultsIn 4885 out of 51 399 ED visits (9.5%), BCs were collected. In 598/4885 (12.2%) visits, at least one of the BCs was true positive. Both a gradient boosted tree model and a logistic regression model showed good performance in predicting BC results with area under curve of the receiver operating characteristics of 0.77 (95% CI 0.73 to 0.82) and 0.78 (95% CI 0.73 to 0.82) in the test sets, respectively. In the gradient boosted tree model, the optimal threshold would predict 69% of BCs in the test set to be negative, with a negative predictive value of over 94%.
conclusionsBoth models can accurately identify patients with low risk of bacteraemia at the ED in this single-centre setting and may be useful to reduce unnecessary BCs and associated healthcare costs. Further studies are necessary for validation and to investigate the potential clinical benefits and possible risks after implementation.
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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.