Mortality associated with in-hospital bacteraemia caused by Staphylococcus aureus: a multistate analysis with follow-up beyond hospital discharge.

OBJECTIVES The main objective was to study the impact of in-hospital bacteraemia caused by Staphylococcus aureus on mortality within 90 days after admission. We compared methicillin-resistant S. aureus (MRSA) with methicillin-susceptible S. aureus (MSSA). PATIENTS AND METHODS The study population consisted of adult residents of Tayside, Scotland, UK, from 1 January 2005 to 30 September 2006 who had a new admission to Ninewells Hospital between 1 July 2005 and 30 June 2006. All patients (n = 3132) in the same wards as the patients infected with S. aureus were included. We addressed key weaknesses in previous studies by using a cohort design and applying a multistate model, which addressed the temporal dynamics. Critically, the model recognized that death and discharge from the hospital are competing events and that delay in discharge independently increases the risk of death. RESULTS The cohort included 3132 patients, of whom 494 died within 90 days after admission, 34 developed MRSA bacteraemia and 26 MSSA bacteraemia in the hospital. In comparison with patients without S. aureus bacteraemia, the death hazard was 5.6 times greater with MRSA [95% confidence interval (CI) 3.36-9.41] and 2.7 times greater with MSSA bacteraemia (95% CI 1.33-5.39). After adjustment for co-morbidity, hospitalization, age and sex, the death hazard was 2.9 times greater with MRSA (95% CI 1.70-4.88) and 1.7 times greater with MSSA bacteraemia (95% CI 0.84-3.47). CONCLUSIONS Time-dependent models such as the proposed multistate model are necessary to address the temporal dynamics of admission, infection, discharge and death. The impact of S. aureus bacteraemia on mortality should be considered on two levels: the burden of disease, i.e. nosocomial infection with S. aureus bacteraemia, and the burden of resistance to methicillin.

[1]  B. Cooper,et al.  Methicillin-resistant Staphylococcus aureus in hospitals and the community: stealth dynamics and control catastrophes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[2]  J. Hurley Comparison of mortality associated with methicillin-susceptible and methicillin-resistant Staphylococcus aureus bacteremia: an ecological analysis. , 2003, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[3]  Tim E A Peto,et al.  Mortality after Staphylococcus aureus bacteraemia in two hospitals in Oxfordshire, 1997-2003: cohort study , 2006, BMJ : British Medical Journal.

[4]  How could primary care meet the informatics needs of UK Biobank? A Scottish proposal. , 2003, Informatics in primary care.

[5]  S. Cosgrove,et al.  Clinical and economic burden of antimicrobial resistance , 2008, Expert review of anti-infective therapy.

[6]  Martin Schumacher,et al.  Time-dependent covariates in the proportional subdistribution hazards model for competing risks. , 2008, Biostatistics.

[7]  M. Schumacher,et al.  Modeling the effect of time-dependent exposure on intensive care unit mortality , 2009, Intensive Care Medicine.

[8]  S. Cosgrove,et al.  Comparison of mortality associated with methicillin-resistant and methicillin-susceptible Staphylococcus aureus bacteremia: a meta-analysis. , 2003, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[9]  K. Anstrom,et al.  Costs and Outcomes Among Hemodialysis-Dependent Patients With Methicillin-Resistant or Methicillin-Susceptible Staphylococcus aureus Bacteremia , 2005, Infection Control & Hospital Epidemiology.

[10]  P. G. Davey,et al.  Health and Economic Impacts of Antibiotic Resistance in European Hospitals – Outlook on the BURDEN Project , 2008, Infection.

[11]  W. Bilker,et al.  Role of Matching in Case-Control Studies of Antimicrobial Resistance , 2009, Infection Control & Hospital Epidemiology.

[12]  Martin Schumacher,et al.  An easy mathematical proof showed that time-dependent bias inevitably leads to biased effect estimation. , 2008, Journal of clinical epidemiology.

[13]  R. Gaynes,et al.  Practices to Improve Antimicrobial Use at 47 US Hospitals the Status of the 1997 SHEA/IDSA Position Paper Recommendations , 2000, Infection Control & Hospital Epidemiology.

[14]  H Putter,et al.  Tutorial in biostatistics: competing risks and multi‐state models , 2007, Statistics in medicine.

[15]  Martin Schumacher,et al.  Risk factors for the development of nosocomial pneumonia and mortality on intensive care units: application of competing risks models , 2008, Critical care.

[16]  F. Colardyn,et al.  Outcome and attributable mortality in critically Ill patients with bacteremia involving methicillin-susceptible and methicillin-resistant Staphylococcus aureus. , 2002, Archives of internal medicine.

[17]  M. Whitby,et al.  Risk of death from methicillin‐resistant Staphylococcus aureus bacteraemia: a meta‐analysis , 2001, The Medical journal of Australia.

[18]  D. Elston Using a Longitudinal Model to Estimate the Effect of Methicillin-resistant Staphylococcus aureus Infection on Length of Stay in an Intensive Care Unit , 2011 .

[19]  K. Schulman,et al.  Clinical Outcomes and Costs Due to Staphylococcus aureus Bacteremia Among Patients Receiving Long-Term Hemodialysis , 2005, Infection Control & Hospital Epidemiology.