Literature DB >> 20557905

[Prediction of bacteremia in patients with suspicion of infection in emergency room].

Pere Tudela1, Alicia Lacoma, Cristina Prat, Josep Maria Mòdol, Montserrat Giménez, Jaume Barallat, Jordi Tor.   

Abstract

BACKGROUND AND OBJECTIVES: To evaluate the relationship between some clinical and analytical data and the presence of bacteremia in order to establish a clinical decision rule. PATIENTS AND METHODS: All the patients with blood cultures obtained from the emergency room in a two months period were analyzed. Patients were randomly assigned to derivation or validation sets. A logistic regression of the significant values in the univariate analysis was performed and a score obtained. The prevalence of bacteraemia for every score was calculated. The diagnostic efficacy curves and the performance of the predictive model were calculated.
RESULTS: 412 patients were enrolled. The blood cultures were positive in 12.8% of them. The significant values in the univariate analysis were Charlson index ≥2 and PCT > 0.4ng/ml. Four groups of increasing risk of bacteraemia were designed, from 0 to 35% in the derivation set and from 2.9% to 27.2% in the validation set. In the diagnostic efficacy curve, the AUC was 0.8 in the derivation set and 0.74 in the validation set. The model presented a negative predictive value of 95.2% in the derivation set and 95.3% in the validation set.
CONCLUSIONS: A model that includes Charlson index and PCT makes possible to define a group of patients with a very low risk of bacteremia.
Copyright © 2009 Elsevier España, S.L. All rights reserved.

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Year:  2010        PMID: 20557905     DOI: 10.1016/j.medcli.2010.04.009

Source DB:  PubMed          Journal:  Med Clin (Barc)        ISSN: 0025-7753            Impact factor:   1.725


  5 in total

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

Authors:  Franz Ratzinger; Michel Dedeyan; Matthias Rammerstorfer; Thomas Perkmann; Heinz Burgmann; Athanasios Makristathis; Georg Dorffner; Felix Lötsch; Alexander Blacky; Michael Ramharter
Journal:  PLoS One       Date:  2014-09-03       Impact factor: 3.240

2.  Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study.

Authors:  Franz Ratzinger; Helmuth Haslacher; Thomas Perkmann; Matilde Pinzan; Philip Anner; Athanasios Makristathis; Heinz Burgmann; Georg Heinze; Georg Dorffner
Journal:  Sci Rep       Date:  2018-08-15       Impact factor: 4.379

3.  [Predictive factors of bacteraemia in the patients seen in emergency departments due to infections].

Authors:  S Z Iqbal-Mirza; R Estévez-González; V Serrano-Romero de Ávila; E de Rafael González; E Heredero-Gálvez; A Julián-Jiménez
Journal:  Rev Esp Quimioter       Date:  2019-11-29       Impact factor: 1.553

4.  Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study.

Authors:  Anneroos W Boerman; Michiel Schinkel; Lotta Meijerink; Eva S van den Ende; Lara Ca Pladet; Martijn G Scholtemeijer; Joost Zeeuw; Anuschka Y van der Zaag; Tanca C Minderhoud; Paul W G Elbers; W Joost Wiersinga; Robert de Jonge; Mark Hh Kramer; Prabath W B Nanayakkara
Journal:  BMJ Open       Date:  2022-01-04       Impact factor: 2.692

Review 5.  [New predictive models of bacteremia in the emergency department: a step forward].

Authors:  A Julián-Jiménez; R Rubio-Díaz; J González Del Castillo; F J Candel
Journal:  Rev Esp Quimioter       Date:  2022-04-13       Impact factor: 2.515

  5 in total

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