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在麻醉后护理单元使用麻醉信息管理系统预测止吐补救治疗。

The use of an anesthesia information management system for prediction of antiemetic rescue treatment at the postanesthesia care unit.

作者信息

Junger A, Hartmann B, Benson M, Schindler E, Dietrich G, Jost A, Béye-Basse A, Hempelmannn G

机构信息

Department of Anesthesiology and Intensive Care Medicine, Justus-Liebig-University, Rudolf-Buchheim-Str. 7, D-35392 Giessen, Germany.

出版信息

Anesth Analg. 2001 May;92(5):1203-9. doi: 10.1097/00000539-200105000-00023.

DOI:10.1097/00000539-200105000-00023
PMID:11323347
Abstract

UNLABELLED

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(R) (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 N(2)O, 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.

摘要

未标注

我们使用麻醉信息管理系统(AIMS)设计了一个用于预测抗呕吐急救治疗的评分,以此作为麻醉后护理单元(PACU)中术后恶心呕吐(PONV)的一个指标。此外,我们想研究通过AIMS收集的数据是否适用于类似的临床研究。在1997年1月1日至1999年12月31日的3年期间,使用自动麻醉记录保存系统NarkoData®(德国胡滕贝格的IMESO有限公司)在线记录了27626例术后入住PACU患者的数据集。通过向前逐步逻辑回归研究了10个患者相关、5个手术相关、15个麻醉相关和4个术后变量。不仅可以根据之前描述的3个患者相关变量(女性,优势比[OR]=2.45;吸烟者,OR=0.53;年龄,OR=0.995)和1个手术变量(手术持续时间,OR=1.005)估计在PACU中发生PONV的概率,而且3个麻醉相关变量(术中使用阿片类药物,OR=4.18;使用N₂O,OR=2.24;丙泊酚静脉麻醉,OR=0.40)也具有预测性。在将风险计算方程应用于AIMS时,可以自动计算出个体发生PONV的风险。

启示

本研究的目的是通过使用在线麻醉记录来研究术后恶心呕吐的预测因素。借助计算机化数据评估,可以检测出34个变量中的7个作为危险因素。通过在记录保存系统中实施自动评分,可以进行个体风险计算。

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