Nosocomial infections: validation of surveillance and computer modeling to identify patients at risk.
To estimate the accuracy of routine hospital-wide surveillance for nosocomial infection, the authors performed a validation study at the University of Iowa Hospitals and Clinics, a 900-bed tertiary care institution, by daily concurrent surveys of all patients' charts. The study extended over a 10-month period from January to October 1987. The sensitivity and specificity of the reported data were 80.7% (95% confidence interval (CI) 72.2-89.2) and 97.5% (95% CI 96.4-98.5), respectively. The predictive values of positive or negative reports of an infection were 75.3% (95% CI 66.3-84.2) and 98.1% (95% CI 97.3-99.1), respectively. In a separate analysis, the data entry system was reviewed for eight descriptive variables among all patients with infections (n = 443) identified over a 2-month period. The data entry was found to be 94-99% accurate. To improve the efficiency of current surveillance, the authors used data gathered during the study to develop a computer model for the identification of patients with a high probability of having a nosocomial infection. The use of stepwise logistic regression identified five variables which independently predicted infection: age of the patient (years), days of antibiotics, days of hospitalization, and the number of days on which urine and/or wound cultures were obtained. Optimal sensitivity and specificity (81.6% and 72.5%, respectively) were found when the model examined patients with an 8% or higher a priori probability of infection; this figure corresponded to a review of 33% of the patients' charts. Increasing the a priori probability would progressively increase specificity and reduce both sensitivity and the number of charts needed for review. If it is prospectively validated, the model may provide a more efficient mechanism by which to conduct hospital-wide surveillance.
Broderick
A
A
Department of Internal Medicine, University of Iowa College of Medicine, Iowa City.
Mori
M
M
Nettleman
M D
MD
Streed
S A
SA
Wenzel
R P
RP
D000367:Age Factors; D000704:Analysis of Variance; D000900:Anti-Bacterial Agents / Q000627:therapeutic use; D003198:Computer Simulation; D003428:Cross Infection / Q000453:epidemiology* / Q000209:etiology; D006785:Hospitals, University; D006801:Humans; D007484:Iowa / Q000453:epidemiology; D007902:Length of Stay; D011159:Population Surveillance; D011336:Probability; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013048:Specimen Handling
D000900:Anti-Bacterial Agents