BACKGROUND: Health care-acquired urinary tract infection is common, and the risk factors should be understood by those who manage hospitalized patients and researchers interested in interventions and programs designed to reduce rates. METHODS: We used multivariable logistic regression to identify factors that demonstrated a statistical association with infection. RESULTS: The incidence rate for infection was 1.66%, and risks increased for patients with prolonged length of stay (odd ratio [OR], 5.28; 95% confidence interval [CI]: 2.46-11.34), urinary catheter (OR, 5.16; 95% CI: 2.84-9.36), unresolved spinal injury (OR, 4.07; 95% CI: 1.04-15.92), transfer to/from another hospital (OR, 2.9; 95% CI: 1.39-6.04), some assistance for daily living prior to admission (OR, 2.58; 95% CI: 1.51-4.41), underlying neurologic disease (OR, 2.59; 95% CI: 1.49-4.49), previous stroke (OR, 1.94; 95% CI: 1.03-3.67), and fracture or dislocation on admission (OR, 3.34; 95% CI: 1.75-6.38). Male sex was protective (OR, 0.44; 95% CI: 0.26-0.77). CONCLUSION: Our data describe a general hospital population and therefore have relevance to many hospital-based health care professionals. The statistical model is a good fit to the data and has good predictive power. We identify high-risk groups and confirm the need for good decision making for managing the risks of health care-acquired urinary tract infection. This requires information on the effectiveness of risk-reducing strategies and the changes to economic costs and health benefits that result and the synthesis of these data in appropriately designed economic models.
BACKGROUND: Health care-acquired urinary tract infection is common, and the risk factors should be understood by those who manage hospitalized patients and researchers interested in interventions and programs designed to reduce rates. METHODS: We used multivariable logistic regression to identify factors that demonstrated a statistical association with infection. RESULTS: The incidence rate for infection was 1.66%, and risks increased for patients with prolonged length of stay (odd ratio [OR], 5.28; 95% confidence interval [CI]: 2.46-11.34), urinary catheter (OR, 5.16; 95% CI: 2.84-9.36), unresolved spinal injury (OR, 4.07; 95% CI: 1.04-15.92), transfer to/from another hospital (OR, 2.9; 95% CI: 1.39-6.04), some assistance for daily living prior to admission (OR, 2.58; 95% CI: 1.51-4.41), underlying neurologic disease (OR, 2.59; 95% CI: 1.49-4.49), previous stroke (OR, 1.94; 95% CI: 1.03-3.67), and fracture or dislocation on admission (OR, 3.34; 95% CI: 1.75-6.38). Male sex was protective (OR, 0.44; 95% CI: 0.26-0.77). CONCLUSION: Our data describe a general hospital population and therefore have relevance to many hospital-based health care professionals. The statistical model is a good fit to the data and has good predictive power. We identify high-risk groups and confirm the need for good decision making for managing the risks of health care-acquired urinary tract infection. This requires information on the effectiveness of risk-reducing strategies and the changes to economic costs and health benefits that result and the synthesis of these data in appropriately designed economic models.
Authors: F M E Wagenlehner; Mete Cek; Kurt G Naber; Hiroshi Kiyota; Truls E Bjerklund-Johansen Journal: World J Urol Date: 2011-09-07 Impact factor: 4.226
Authors: Margaret Hansen; George Oosthuizen; John Windsor; Iain Doherty; Samuel Greig; Karina McHardy; Lloyd McCann Journal: J Med Internet Res Date: 2011-03-01 Impact factor: 5.428