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- Linda M Peelen, Jilles B Bijker, Wilton A van Klei, Jurgen C de Graaff, Leo van Wolfswinkel, Nathalie P Kool, and Judith A R van Waes.
- Department of Anesthesiology, University Medical Center, Utrecht, The Netherlands. n.p.kool@umcutrecht.nl
- Can J Anaesth. 2012 Sep 1;59(9):833-41.
PurposeArtifacts in anesthesia information management system (AIMS) databases may influence research results. Filtering during data capturing can prevent artifacts from being stored. In this prospective study, we assessed the reliability of AIMS data by determining the incidence of artifactual values stored in the AIMS.MethodsVital parameter values regarding 86 surgical patients were collected in the AIMS both manually and automatically after filtering using the median value per minute. The percentage of artifactual values with a 95% confidence interval (CI) was calculated for each parameter. Secondary outcomes included the number of values that deviated from a predefined baseline, the percentage of these deviations that were caused by artifacts, the number of episodes across which these artifacts were distributed, and the most common causes of artifacts.ResultsAltogether, 9,534 min of anesthesia time were recorded. The overall percentages of artifacts were: 0.0 for heart rate (95% CI: 0.0 to 0.1), 0.3 for oxygen saturation (95% CI: 0.2 to 0.4), 4.7 for ST-segment (95% CI: 4.3 to 5.2), 2.3 for noninvasive blood pressure values (95% CI: 1.8 to 2.9), and 14 for invasive blood pressure values (95% CI: 12 to 15). Artifacts as a percentage of deviations from baseline were: 1.6 for heart rate (95% CI: 0.4 to 5.7), 24 for saturation (95% CI: 18 to 32), 83 for ST-segment (95% CI: 76 to 87), 3.3 for noninvasive blood pressure values (95% CI: 2.5 to 87), and 27 for invasive blood pressure values (95% CI: 24 to 31).ConclusionsStoring a median value per minute to filter capturing of vital parameter values in an AIMS database provides reliable data for heart rate and oxygen saturation and acceptable reliability for noninvasive blood pressure data. Knowledge about the method of artifact filtering is essential in studies using AIMS data.
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