American Journal of Epidemiology Vol. 131, No. 4: 734-742
Copyright © 1990 by The Johns Hopkins University School of Hygiene and Public Health
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NOSOCOMIAL INFECTIONS: VALIDATION OF SURVEILLANCE AND COMPUTER MODELING TO IDENTIFY PAT AT RISK
1Department of Internal Medicine, University of Iowa College of Medicine Iowa City, IA
2Department of Preventive Medicine, University of Iowa College of Medicine Iowa City, IA
3Program of Hospital Epidennology, University Iowa Hospitals and Clinics Iowa City, IA
Reprint requests to Dr. Richard P. Wenzel, Division of General Medicine, Clinical Epiderniolo and Health Services Research, Department of Internal Medicine, University of Iowa Hospitals and Clinics, Iowa City, Iowa 52242
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.
computer simulation; cross infection; patient identification systems; risk; sensitivity and specificity (epidemiology)
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