Department of Anesthesiology, University Medical Center, Utrecht, The Netherlands.
Can J Anaesth. 2012 Sep;59(9):833-41. doi: 10.1007/s12630-012-9754-0. Epub 2012 Jul 18.
Artifacts 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.
Vital 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.
Altogether, 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).
Storing 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.
麻醉信息管理系统(AIMS)数据库中的伪影可能会影响研究结果。在数据采集过程中进行过滤可以防止伪影被存储。在这项前瞻性研究中,我们通过确定存储在 AIMS 中的异常值的发生率来评估 AIMS 数据的可靠性。
在 AIMS 中手动和自动收集了 86 例手术患者的生命参数值,每分钟使用中位数进行过滤。计算每个参数的异常值百分比(95%置信区间[CI])。次要结局包括偏离预定义基线的数值数量、这些偏差中由伪影引起的百分比、这些伪影分布的时间段数量以及伪影最常见的原因。
总共记录了 9534 分钟的麻醉时间。总的异常值百分比为:心率为 0.0(95%CI:0.0 至 0.1)、氧饱和度为 0.3(95%CI:0.2 至 0.4)、ST 段为 4.7(95%CI:4.3 至 5.2)、无创血压值为 2.3(95%CI:1.8 至 2.9)和 14 为有创血压值(95%CI:12 至 15)。异常值作为偏离基线偏差百分比为:心率为 1.6(95%CI:0.4 至 5.7)、饱和度为 24(95%CI:18 至 32)、ST 段为 83(95%CI:76 至 87)、无创血压值为 3.3(95%CI:2.5 至 87)和有创血压值为 27(95%CI:24 至 31)。
在 AIMS 数据库中过滤关键参数值每分钟存储中位数可为心率和氧饱和度提供可靠的数据,并为无创血压数据提供可接受的可靠性。在使用 AIMS 数据的研究中,了解伪影过滤方法至关重要。