TY - JOUR
T1 - An electronic health record-wide association study to identify populations at increased risk of E. coli bacteraemia
AU - Pritchard, Emma
AU - Vihta, Karina Doris
AU - Lipworth, Samuel
AU - Pouwels, Koen B.
AU - Stoesser, Nicole
AU - Hope, Russell
AU - Muller-Pebody, Berit
AU - Quan, T. Phuong
AU - Cregan, Jack
AU - Brown, Colin
AU - Hopkins, Susan
AU - Eyre, David W.
AU - Walker, A. Sarah
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/10
Y1 - 2025/10
N2 - Objectives: Escherichia coli bacteraemias have been under mandatory surveillance in the UK for fifteen years, but cases continue to rise. Systematic searches of all features present within electronic healthcare records (EHRs), described here as an EHR-wide association study (EHR-WAS), could potentially identify under-appreciated factors that could be targeted to reduce infections. Methods: We used data from Oxfordshire, UK, and an EHR-WAS method developed for use with large-scale COVID-19 data to estimate associations between E. coli bacteraemia cases, hospital-exposed controls, and 377 potential risk factors using Poisson regression models adjusted for potential confounders for three two-year financial year (FY) periods. Results: FY2022/23–2023/24 analysis included 757 (0.3%) cases and 276,758 (99.7%) controls. We identified six broad disease areas associated with increased or decreased E. coli bacteraemia risk. Renal/urological/urinary tract infection-related variables had the largest impact, with 47% of cases theoretically removed if these factors could be minimised. Cancer-related variables were associated with higher E. coli bacteraemia risk (1.20 times higher (95%CI 1.08–1.34) per three months closer to chemotherapy in the last year), as were gastrointestinal- and infectious disease-related variables. Cardiac/respiratory-related variables were associated with lower E. coli bacteraemia risk, whereas greater healthcare exposure showed no consistent effect. Associated factors varied across periods, but broad groups remained similar. Conclusions: Applying an EHR-WAS approach, we show E. coli bacteraemias are largely driven by known risk factors and frailty, highlighting the importance of monitoring these factors and targeting modifiable risks where possible.
AB - Objectives: Escherichia coli bacteraemias have been under mandatory surveillance in the UK for fifteen years, but cases continue to rise. Systematic searches of all features present within electronic healthcare records (EHRs), described here as an EHR-wide association study (EHR-WAS), could potentially identify under-appreciated factors that could be targeted to reduce infections. Methods: We used data from Oxfordshire, UK, and an EHR-WAS method developed for use with large-scale COVID-19 data to estimate associations between E. coli bacteraemia cases, hospital-exposed controls, and 377 potential risk factors using Poisson regression models adjusted for potential confounders for three two-year financial year (FY) periods. Results: FY2022/23–2023/24 analysis included 757 (0.3%) cases and 276,758 (99.7%) controls. We identified six broad disease areas associated with increased or decreased E. coli bacteraemia risk. Renal/urological/urinary tract infection-related variables had the largest impact, with 47% of cases theoretically removed if these factors could be minimised. Cancer-related variables were associated with higher E. coli bacteraemia risk (1.20 times higher (95%CI 1.08–1.34) per three months closer to chemotherapy in the last year), as were gastrointestinal- and infectious disease-related variables. Cardiac/respiratory-related variables were associated with lower E. coli bacteraemia risk, whereas greater healthcare exposure showed no consistent effect. Associated factors varied across periods, but broad groups remained similar. Conclusions: Applying an EHR-WAS approach, we show E. coli bacteraemias are largely driven by known risk factors and frailty, highlighting the importance of monitoring these factors and targeting modifiable risks where possible.
KW - Bloodstream infections
KW - Electronic health records
KW - Escherichia coli
KW - Infectious disease epidemiology
KW - Population health
KW - Risk factors
UR - https://www.scopus.com/pages/publications/105015184830
UR - https://www.mendeley.com/catalogue/ef91206b-0b96-3886-99d4-5d3a705758ea/
U2 - 10.1016/j.jinf.2025.106612
DO - 10.1016/j.jinf.2025.106612
M3 - Article
AN - SCOPUS:105015184830
SN - 0163-4453
VL - 91
JO - Journal of Infection
JF - Journal of Infection
IS - 4
M1 - 106612
ER -