BACKGROUND: Healthcare-associated urinary tract infections (HCA UTI) account for a large proportion of hospital infections, with recently launched surveillance in the UK focusing on reducing catheter-associated urinary tract infections. However, a wealth of administrative information already collected routinely by hospitals is currently not used to its maximum potential for surveillance. AIM: To quantify the evidence base of HCA UTI risk factors and to determine their potential for shaping and informing innovative surveillance tools using local hospital data. METHODS: A systematic literature review was undertaken to find established risks for HCA UTI. Population-attributable risk percentages (PAR%) were calculated for these risk factors, generating a hierarchy of risks. Administrative hospital data were subsequently interrogated for these quantified risks. FINDINGS: Over 30% of the risk factors identified from the systematic literature review were independent predictors of infection. The highest PAR% was associated with urinary catheterization, with the calculation that 79.3% of UTI would be prevented if catheterization was not performed. PAR% calculations were performed for 60% of the independent predictors for HCA UTI. Sixty-five percent of the identified independent risk factors were found to be coded within the administrative hospital dataset, including urinary catheterization. CONCLUSION: This work has quantified established HCA UTI risks and demonstrates that there is potential for more effective use of administrative hospital data for risk monitoring and surveillance of HCA UTI.
BACKGROUND: Healthcare-associated urinary tract infections (HCA UTI) account for a large proportion of hospital infections, with recently launched surveillance in the UK focusing on reducing catheter-associated urinary tract infections. However, a wealth of administrative information already collected routinely by hospitals is currently not used to its maximum potential for surveillance. AIM: To quantify the evidence base of HCA UTI risk factors and to determine their potential for shaping and informing innovative surveillance tools using local hospital data. METHODS: A systematic literature review was undertaken to find established risks for HCA UTI. Population-attributable risk percentages (PAR%) were calculated for these risk factors, generating a hierarchy of risks. Administrative hospital data were subsequently interrogated for these quantified risks. FINDINGS: Over 30% of the risk factors identified from the systematic literature review were independent predictors of infection. The highest PAR% was associated with urinary catheterization, with the calculation that 79.3% of UTI would be prevented if catheterization was not performed. PAR% calculations were performed for 60% of the independent predictors for HCA UTI. Sixty-five percent of the identified independent risk factors were found to be coded within the administrative hospital dataset, including urinary catheterization. CONCLUSION: This work has quantified established HCA UTI risks and demonstrates that there is potential for more effective use of administrative hospital data for risk monitoring and surveillance of HCA UTI.
Authors: Jacob R Gillen; James M Isbell; Alex D Michaels; Christine L Lau; Robert G Sawyer Journal: Surg Infect (Larchmt) Date: 2015-06-26 Impact factor: 2.150
Authors: Jyothi Manohar; Savannah Hatt; Brigette B DeMarzo; Freida Blostein; Anna E W Cronenwett; Jianfeng Wu; Kyu Han Lee; Betsy Foxman Journal: Am J Infect Control Date: 2019-09-17 Impact factor: 2.918