Röhrig Rainer, Junger Axel, Hartmann Bernd, Klasen Joachim, Quinzio Lorenzo, Jost Andreas, Benson Matthias, Hempelmann Gunter
Department of Anesthesiology, University Hospital Giessen, Giessen, Germany.
Anesth Analg. 2004 Mar;98(3):569-77, table of contents. doi: 10.1213/01.ane.0000103262.26387.9c.
The objective of this study was to evaluate prognostic models for quality assurance purposes in predicting automatically detected intraoperative cardiovascular events (CVE) in 58458 patients undergoing noncardiac surgery. To this end, we assessed the performance of two established models for risk assessment in anesthesia, the Revised Cardiac Risk Index (RCRI) and the ASA physical status classification. We then developed two new models. CVEs were detected from the database of an electronic anesthesia record-keeping system. Logistic regression was used to build a complex and a simple predictive model. Performance of the prognostic models was assessed using analysis of discrimination and calibration. In 5249 patients (17.8%) of the evaluation (n = 29437) and 5031 patients (17.3%) of the validation cohorts (n = 29021), a minimum of one CVE was detected. CVEs were associated with significantly more frequent hospital mortality (2.1% versus 1.0%; P < 0.01). The new models demonstrated good discriminative power, with an area under the receiver operating characteristic curve (AUC) of 0.709 and 0.707 respectively. Discrimination of the ASA classification (AUC 0.647) and the RCRI (AUC 0.620) were less. Neither the two new models nor ASA classification nor the RCRI showed acceptable calibration. ASA classification and the RCRI alone both proved unsuitable for the prediction of intraoperative CVEs.
The objective of this study was to evaluate prognostic models for quality assurance purposes to predict the occurrence of automatically detected intraoperative cardiovascular events in 58,458 patients undergoing noncardiac surgery. Two newly developed models showed good discrimination but, because of reduced calibration, their clinical use is limited. The ASA physical status classification and the Revised Cardiac Risk Index are unsuitable for the prediction of intraoperative cardiovascular events.
本研究的目的是评估用于质量保证目的的预后模型,以预测58458例接受非心脏手术患者自动检测到的术中心血管事件(CVE)。为此,我们评估了两种既定的麻醉风险评估模型,即修订心脏风险指数(RCRI)和美国麻醉医师协会(ASA)身体状况分类。然后我们开发了两种新模型。CVE从电子麻醉记录保存系统的数据库中检测出来。使用逻辑回归构建一个复杂和一个简单的预测模型。使用辨别力分析和校准来评估预后模型的性能。在评估队列(n = 29437)的5249例患者(17.8%)和验证队列(n = 29021)的5031例患者(17.3%)中,至少检测到一次CVE。CVE与明显更高的医院死亡率相关(2.1%对1.0%;P < 0.01)。新模型显示出良好的辨别力,受试者操作特征曲线(AUC)下的面积分别为0.709和0.707。ASA分类(AUC 0.647)和RCRI(AUC 0.620)的辨别力较低。两种新模型、ASA分类和RCRI均未显示出可接受的校准。单独的ASA分类和RCRI均被证明不适用于预测术中CVE。
本研究的目的是评估用于质量保证目的的预后模型,以预测58458例接受非心脏手术患者自动检测到的术中心血管事件的发生。两种新开发的模型显示出良好的辨别力,但由于校准降低,其临床应用有限。ASA身体状况分类和修订心脏风险指数不适用于预测术中心血管事件。