Trial reportPloS one2014
A risk prediction model for screening bacteremic patients: a cross sectional study.
Trial report in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 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
- Regression without regrets -initial data analysis is a prerequisite for multivariable regression.BMC medical research methodology · 2024Article
- Antimicrobial resistance prevalence in bloodstream infection in 29 European countries by age and sex: An observational study.PLoS medicine · 2024Observational
- A Simple Bacteremia Score for Predicting Bacteremia in Patients with Suspected Infection in the Emergency Department: A Cohort Study.Journal of personalized medicine · 2023Article
- Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study.Diagnostics (Basel, Switzerland) · 2022Article
- Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers.The EPMA journal · 2021Article
- Regression with Highly Correlated Predictors: Variable Omission Is Not the Solution.International journal of environmental research and public health · 2021Article
- Added value of clinical prediction rules for bacteremia in hemodialysis patients: An external validation study.PloS one · 2021Article
- The Shapiro-Procalcitonin algorithm (SPA) as a decision tool for blood culture sampling: validation in a prospective cohort study.Infection · 2020Article
- Risk of bacteremia in patients presenting with shaking chills and vomiting - a prospective cohort study.Epidemiology and infection · 2020Article
- Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study.Scientific reports · 2018Article
- Development and Validation of a Clinical Prediction Rule for Bacteremia among Maintenance Hemodialysis Patients in Outpatient Settings.PloS one · 2017Article
- Clinical Impact of Blood Culture Results in Acutely Ill Hospitalized Adult Patients With Cystic Fibrosis.Journal of clinical medicine research · 2016Article
- Neither Single nor a Combination of Routine Laboratory Parameters can Discriminate between Gram-positive and Gram-negative Bacteremia.Scientific reports · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBacteraemia is a frequent and severe condition with a high mortality rate. Despite profound knowledge about the pre-test probability of bacteraemia, blood culture analysis often results in low rates of pathogen detection and therefore increasing diagnostic costs. To improve the cost-effectiveness of blood culture sampling, we computed a risk prediction model based on highly standardizable variables, with the ultimate goal to identify via an automated decision support tool patients with very low risk for bacteraemia.
methodsIn this retrospective hospital-wide cohort study evaluating 15,985 patients with suspected bacteraemia, 51 variables were assessed for their diagnostic potency. A derivation cohort (n = 14.699) was used for feature and model selection as well as for cut-off specification. Models were established using the A2DE classifier, a supervised Bayesian classifier. Two internally validated models were further evaluated by a validation cohort (n = 1,286).
resultsThe proportion of neutrophile leukocytes in differential blood count was the best individual variable to predict bacteraemia (ROC-AUC: 0.694). Applying the A2DE classifier, two models, model 1 (20 variables) and model 2 (10 variables) were established with an area under the receiver operating characteristic curve (ROC-AUC) of 0.767 and 0.759, respectively. In the validation cohort, ROC-AUCs of 0.800 and 0.786 were achieved. Using predefined cut-off points, 16% and 12% of patients were allocated to the low risk group with a negative predictive value of more than 98.8%.
conclusionApplying the proposed models, more than ten percent of patients with suspected blood stream infection were identified having minimal risk for bacteraemia. Based on these data the application of this model as an automated decision support tool for physicians is conceivable leading to a potential increase in the cost-effectiveness of blood culture sampling. External prospective validation of the model's generalizability is needed for further appreciation of the usefulness of this tool.
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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.