1a - Identification of risk factors for hospital-onset bacteremia to inform a routine data based risk prediction– an umbrella review

Background : Hospital-onset bacteremia (HOB) places an enormous burden on affected patients, healthcare workers and society. It is important to identify patient groups – and ideally individual patients – with the highest risk of HOB in order to intensify infection prevention/control measures. The overarching goal of the national BMBF-funded project RISK PRINCIPE (RISK Prediction for Risk-stratified INfection Control and PrEvention, Grant number: 01ZZ2323A) is to develop and implement a data-driven, risk stratified infection control system to effectively and efficiently reduce HOBs. To understand infection-related risks, the aim of the umbrella review was to identify, classify, and potentially weigh already known risk factors for the onset of HOBs and support algorithm development.

Methods : We searched CINAHL, Medline, Cochrane Library and Web of Science. Abstracts and full text were screened by two independent reviewers according to predefined criteria. Discrepancies were resolved by a third reviewer. We included systematic reviews reporting risk factors for the onset of HOB in all inpatients in OECD countries published since 2013. Risks of bias were assessed using AMSTAR2. The review was reported according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines. A narrative synthesis approach was used to interpret the results. PROSPERO registration: CRD42023480112

Results : From 1668 screened records, 20 systematic reviews reporting 50 individual risk factors for different stages of HOB and patient populations were included. We categorized the risk factors into patient-related (e.g. immunosuppression, comorbidities, male sex, preterm birth, smoking, vitamin D deficiency), procedure-related (e.g. length of stay, regular admission, device utilization, catheter placement/type) and setting-related (multiple-bed rooms).

Conclusions : These findings support the development of routine data-based risk prediction for the targeted identification of high-risk patients and the corresponding resource allocation.