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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
general
A risk prediction model for screening bacteremic patients: A cross sectional study
PLoS ONE, Volume 9, No. 9, Article e106765, Year 2014
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Description
Background: Bacteraemia 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. Methods: In 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). Results: The 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%. Conclusion: Applying 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. © 2014 Ratzinger et al.
Authors & Co-Authors
Ratzinger, Franz
Austria, Vienna
Medizinische Universität Wien
Perkmann, Thomas
Austria, Vienna
Medizinische Universität Wien
Burgmann, H.
Austria, Vienna
Medizinische Universität Wien
Makristathis, Athanasios
Austria, Vienna
Medizinische Universität Wien
Lötsch, Felix
Austria, Vienna
Medizinische Universität Wien
Ramharter, Michael
Austria, Vienna
Medizinische Universität Wien
Germany, Tubingen
Eberhard Karls Universität Tübingen
Statistics
Citations: 15
Authors: 6
Affiliations: 2
Identifiers
Doi:
10.1371/journal.pone.0106765
ISSN:
19326203
Research Areas
Health System And Policy
Study Design
Cross Sectional Study
Cohort Study
Study Approach
Quantitative