NOSOCOMIAL INFECTIONS: VALIDATION OF SURVEILLANCE AND COMPUTER MODELING TO IDENTIFY PAT AT RISK

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Abstract
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 (Cl) 72.2–89.2) and 97.5% (95% Cl 96.4–98.5), respectively. The predictive values of positive or negative reports of an infection were 75.3% (95% Cl 66.3–84.2) and 98.1% (95% Cl 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 regres sion 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 (8 1.6% 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.