Literature DB >> 25677625

Predicting bacteraemia in validated models--a systematic review.

N Eliakim-Raz1, D W Bates2, L Leibovici3.   

Abstract

Bacteraemia is associated with high mortality. Although many models for predicting bacteraemia have been developed, not all have been validated, and even when they were, the validation processes varied. We identified validated models that have been developed; asked whether they were successful in defining groups with a very low or high prevalence of bacteraemia; and whether they were used in clinical practice. Electronic databases were searched to identify studies that underwent validation on prediction of bacteraemia in adults. We included only studies that were able to define groups with low or high probabilities for bacteraemia (arbitrarily defined as below 3% or above 30%). Fifteen publications fulfilled inclusion criteria, including 59 276 patients. Eleven were prospective and four retrospective. Study populations and the parameters included in the different models were heterogeneous. Ten studies underwent internal validation; the model performed well in all of them. Twelve performed external validation. Of the latter, seven models were validated in a different hospital, using a new independent database. In five of these, the model performed well. After contacting authors, we found that none of the models was implemented in clinical practice. We conclude that heterogeneous studies have been conducted in different defined groups of patients with limited external validation. Significant savings to the system and the individual patient can be gained by refraining from performing blood cultures in groups of patients in which the probability of true bacteraemia is very low, while the probability of contamination is constant. Clinical trials of existing or new models should be done to examine whether models are helpful and safe in clinical use, preferably multicentre in order to secure utility and safety in diverse clinical settings.
Copyright © 2015 European Society of Clinical Microbiology and Infectious Diseases. All rights reserved.

Entities:  

Keywords:  Bacterial bloodstream infections; blood cultures; prediction models; sepsis; validation

Mesh:

Year:  2015        PMID: 25677625     DOI: 10.1016/j.cmi.2015.01.023

Source DB:  PubMed          Journal:  Clin Microbiol Infect        ISSN: 1198-743X            Impact factor:   8.067


  18 in total

Review 1.  Bloodstream infections in older patients.

Authors:  Dafna Yahav; Noa Eliakim-Raz; Leonard Leibovici; Mical Paul
Journal:  Virulence       Date:  2015-12-18       Impact factor: 5.882

2.  Evaluation of a model to improve collection of blood cultures in patients with sepsis in the emergency room.

Authors:  B Mariani; M Corbella; E Seminari; L Sacco; P Cambieri; F Capra Marzani; I F Martino; M A Bressan; A Muzzi; C Marena; C Tinelli; P Marone
Journal:  Eur J Clin Microbiol Infect Dis       Date:  2017-10-28       Impact factor: 3.267

Review 3.  Practical Guidance for Clinical Microbiology Laboratories: A Comprehensive Update on the Problem of Blood Culture Contamination and a Discussion of Methods for Addressing the Problem

Authors:  Gary V Doern; Karen C Carroll; Daniel J Diekema; Kevin W Garey; Mark E Rupp; Melvin P Weinstein; Daniel J Sexton
Journal:  Clin Microbiol Rev       Date:  2019-10-30       Impact factor: 26.132

4.  Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms.

Authors:  Michael Roimi; Ami Neuberger; Anat Shrot; Mical Paul; Yuval Geffen; Yaron Bar-Lavie
Journal:  Intensive Care Med       Date:  2020-01-07       Impact factor: 17.440

5.  Clinical- vs. model-based selection of patients suspected of sepsis for direct-from-blood rapid diagnostics in the emergency department: a retrospective study.

Authors:  Logan Ward; Steen Andreassen; Jesper Johnsen Astrup; Zakia Rahmani; Michela Fantini; Vittorio Sambri
Journal:  Eur J Clin Microbiol Infect Dis       Date:  2019-05-11       Impact factor: 3.267

6.  A bacteraemia risk prediction model: development and validation in an emergency medicine population.

Authors:  Agustín Julián-Jiménez; Juan González Del Castillo; Eric Jorge García-Lamberechts; Itziar Huarte Sanz; Carmen Navarro Bustos; Rafael Rubio Díaz; Josep María Guardiola Tey; Ferrán Llopis-Roca; Pascual Piñera Salmerón; Mikel de Martín-Ortiz de Zarate; Jesús Álvarez-Manzanares; Julio Javier Gamazo-Del Rio; Marta Álvarez Alonso; Begoña Mora Ordoñez; Oscar Álvarez López; María Del Mar Ortega Romero; María Del Mar Sousa Reviriego; Ramón Perales Pardo; Henrique Villena García Del Real; María José Marchena González; José María Ferreras Amez; Félix González Martínez; Francisco Javier Martín-Sánchez; Pedro Beneyto Martín; Francisco Javier Candel González; Antonio Jesús Díaz-Honrubia
Journal:  Infection       Date:  2021-09-06       Impact factor: 3.553

Review 7.  How to Optimize the Use of Blood Cultures for the Diagnosis of Bloodstream Infections? A State-of-the Art.

Authors:  Brigitte Lamy; Sylvie Dargère; Maiken C Arendrup; Jean-Jacques Parienti; Pierre Tattevin
Journal:  Front Microbiol       Date:  2016-05-12       Impact factor: 5.640

8.  Development and Validation of a Clinical Prediction Rule for Bacteremia among Maintenance Hemodialysis Patients in Outpatient Settings.

Authors:  Sho Sasaki; Takeshi Hasegawa; Hiroo Kawarazaki; Atsushi Nomura; Daisuke Uchida; Takahiro Imaizumi; Masahide Furusho; Hiroki Nishiwaki; Shingo Fukuma; Yugo Shibagaki; Shunichi Fukuhara
Journal:  PLoS One       Date:  2017-01-12       Impact factor: 3.240

9.  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

10.  The Development and Validation of a Machine Learning Model to Predict Bacteremia and Fungemia in Hospitalized Patients Using Electronic Health Record Data.

Authors:  Sivasubramanium V Bhavani; Zachary Lonjers; Kyle A Carey; Majid Afshar; Emily R Gilbert; Nirav S Shah; Elbert S Huang; Matthew M Churpek
Journal:  Crit Care Med       Date:  2020-11       Impact factor: 9.296

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