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TECHNOLOGY, COMPUTING, AND SIMULATION

The Use of an Anesthesia Information Management System for Prediction of Antiemetic Rescue Treatment at the Postanesthesia Care Unit

Department of Anesthesiology and Intensive Care Medicine, Justus-Liebig-University Giessen, Germany

Address correspondence and reprint requests to Axel Junger, Dr med, Abt. Anaesthesiologie und Operative Intensivmedizin, Justus-Liebig-Universität, Rudolf-Buchheim-Str. 7, D-35392 Giessen, Germany. Address e-mail to Axel.Junger{at}chiru.med.uni-giessen.de

We used an anesthesia information management system (AIMS) to devise a score for predicting antiemetic rescue treatment as an indicator for postoperative nausea and vomiting (PONV) in the postanesthesia care unit (PACU). Furthermore, we wanted to investigate whether data collected with an AIMS are suitable for comparable clinical investigations. Over a 3-yr period (January 1, 1997, to December 31, 1999), data sets of 27,626 patients who were admitted postoperatively to the PACU were recorded online by using the automated anesthesia record keeping system NarkoData® (IMESO GmbH, Hüttenberg, Germany). Ten patient-related, 5 operative, 15 anesthesia-related, and 4 postoperative variables were studied by using forward stepwise logistic regression. Not only can the probability of having PONV in the PACU be estimated from the 3 previously described patient-related (female gender, odds ratio [OR] = 2.45; smoker, OR = 0.53; and age, OR = 0.995) and one operative variables (duration of surgery, OR = 1.005), but 3 anesthesia-related variables (intraoperative use of opioids, OR = 4.18; use of N₂O, OR = 2.24; and IV anesthesia with propofol, OR = 0.40) are predictive. In implementing an equation for risk calculation into the AIMS, the individual risk of PONV can be calculated automatically.

Implications: The aim of this study was to investigate predictors for postoperative nausea and vomiting by using online anesthesia records. With the help of computerized data evaluation, 7 of 34 variables could be detected as risk factors. By implementing an automatic score into the record keeping system, an individual risk calculation could be made possible.

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