Identification of risk factors for hospital-onset bacteremia to inform a routine data based risk prediction - an umbrella review.

Authors

Anna Bludau, Joëlle Naim, Mike Marquet, Meta M Bönniger, Nicolás Reinoso Schiller, Martin Misailovski, Ramsia Geisler, Tim Eckmanns, Michael Marschollek, Mathias W Pletz, André Scherag, Simone Scheithauer

Published in

The Journal of hospital infection. Jul 02, 2026. Epub Jul 02, 2026.

Abstract

The newly proposed surveillance entity hospital-onset bacteremia (HOB) is associated with significant morbidity, mortality, and costs. Identifying and understanding risk factors is crucial for the development of reliable risk prediction models to guide targeted infection prevention and control strategies.
.To identify and classify risk factors for the onset of HOB in the general patient population in OECD countries.
We conducted an umbrella review to synthesize the evidence from systematic reviews and meta-analyses. We searched CINAHL, PubMed, Cochrane Library, Web of Science and grey literature. Two researchers independently screened abstracts and full texts, extracted data, and assessed quality with AMSTAR2. Data were analysed descriptively; Risk factors were categorized according to whether they can be modified through infection prevention and control interventions.
.We included 19 systematic reviews and identified 43 risk and 5 protective factors which we categorized into patient-related (e.g. prior bloodstream infection (OR=6.56, p=0.004), male sex (OR=2.18, CI=1.52-3.12), multiple comorbidities (OR=1.66, CI=1.35-2.49), smoking (OR=1.26, CI=1.01-1.57)), procedure-related (e.g. red blood cell transfusion (RR=4.82, CI=2.9-8.08)) and setting-related (e.g. single room (RR=0.64, CI=0.53-0.76)).
Most of the risk factors identified are well-recognized for Central Line-Associated Bloodstream Infection (CLABSI) and plausible contributors to HOB. Although not all HOB incidents are preventable, early identification of high-risk patients combined with risk-stratified targeted interventions can prevent cases and improve outcomes. To ensure real-world applicability with algorithmic support, relevant data must be automatically available and changeable factors should be monitored.
The review is registered in PROSPERO and the protocol can be found under CRD42023480112.

PMID:
42392267
Bibliographic data and abstract were imported from PubMed on 03 Jul 2026.

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