A risk prediction model for screening bacteremic patients: a cross sectional study.

<h4>Background</h4>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 im...

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Autores principales:	Franz Ratzinger, Michel Dedeyan, Matthias Rammerstorfer, Thomas Perkmann, Heinz Burgmann, Athanasios Makristathis, Georg Dorffner, Felix Lötsch, Alexander Blacky, Michael Ramharter
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/55169854b6d34492af5130be821d3d3c
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spelling	oai:doaj.org-article:55169854b6d34492af5130be821d3d3c2021-11-25T06:02:10ZA risk prediction model for screening bacteremic patients: a cross sectional study.1932-620310.1371/journal.pone.0106765https://doaj.org/article/55169854b6d34492af5130be821d3d3c2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25184209/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methods</h4>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).<h4>Results</h4>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%.<h4>Conclusion</h4>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.Franz RatzingerMichel DedeyanMatthias RammerstorferThomas PerkmannHeinz BurgmannAthanasios MakristathisGeorg DorffnerFelix LötschAlexander BlackyMichael RamharterPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e106765 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Franz Ratzinger Michel Dedeyan Matthias Rammerstorfer Thomas Perkmann Heinz Burgmann Athanasios Makristathis Georg Dorffner Felix Lötsch Alexander Blacky Michael Ramharter A risk prediction model for screening bacteremic patients: a cross sectional study.
description	<h4>Background</h4>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.<h4>Methods</h4>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).<h4>Results</h4>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%.<h4>Conclusion</h4>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.
format	article
author	Franz Ratzinger Michel Dedeyan Matthias Rammerstorfer Thomas Perkmann Heinz Burgmann Athanasios Makristathis Georg Dorffner Felix Lötsch Alexander Blacky Michael Ramharter
author_facet	Franz Ratzinger Michel Dedeyan Matthias Rammerstorfer Thomas Perkmann Heinz Burgmann Athanasios Makristathis Georg Dorffner Felix Lötsch Alexander Blacky Michael Ramharter
author_sort	Franz Ratzinger
title	A risk prediction model for screening bacteremic patients: a cross sectional study.
title_short	A risk prediction model for screening bacteremic patients: a cross sectional study.
title_full	A risk prediction model for screening bacteremic patients: a cross sectional study.
title_fullStr	A risk prediction model for screening bacteremic patients: a cross sectional study.
title_full_unstemmed	A risk prediction model for screening bacteremic patients: a cross sectional study.
title_sort	risk prediction model for screening bacteremic patients: a cross sectional study.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/55169854b6d34492af5130be821d3d3c
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A risk prediction model for screening bacteremic patients: a cross sectional study.

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