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用于预防非心脏大手术术中低血压的低血压预测指数软件:欧洲多中心前瞻性观察登记研究(EU-HYPROTECT)方案

Hypotension Prediction Index Software to Prevent Intraoperative Hypotension during Major Non-Cardiac Surgery: Protocol for a European Multicenter Prospective Observational Registry (EU-HYPROTECT).

作者信息

Monge García Manuel Ignacio, García-López Daniel, Gayat Étienne, Sander Michael, Bramlage Peter, Cerutti Elisabetta, Davies Simon James, Donati Abele, Draisci Gaetano, Frey Ulrich H, Noll Eric, Ripollés-Melchor Javier, Wulf Hinnerk, Saugel Bernd

机构信息

Medical Affairs Department, Critical Care Europe, Edwards Lifesciences, 1260 Nyon, Switzerland.

Department of Anaesthesiology and Reanimation, University Hospital Marqués de Valdecilla, 39008 Santander, Spain.

出版信息

J Clin Med. 2022 Sep 23;11(19):5585. doi: 10.3390/jcm11195585.

Abstract

Background: Intraoperative hypotension is common in patients having non-cardiac surgery and associated with postoperative acute myocardial injury, acute kidney injury, and mortality. Avoiding intraoperative hypotension is a complex task for anesthesiologists. Using artificial intelligence to predict hypotension from clinical and hemodynamic data is an innovative and intriguing approach. The AcumenTM Hypotension Prediction Index (HPI) software (Edwards Lifesciences; Irvine, CA, USA) was developed using artificial intelligence—specifically machine learning—and predicts hypotension from blood pressure waveform features. We aimed to describe the incidence, duration, severity, and causes of intraoperative hypotension when using HPI monitoring in patients having elective major non-cardiac surgery. Methods: We built up a European, multicenter, prospective, observational registry including at least 700 evaluable patients from five European countries. The registry includes consenting adults (≥18 years) who were scheduled for elective major non-cardiac surgery under general anesthesia that was expected to last at least 120 min and in whom arterial catheter placement and HPI monitoring was planned. The major objectives are to quantify and characterize intraoperative hypotension (defined as a mean arterial pressure [MAP] < 65 mmHg) when using HPI monitoring. This includes the time-weighted average (TWA) MAP < 65 mmHg, area under a MAP of 65 mmHg, the number of episodes of a MAP < 65 mmHg, the proportion of patients with at least one episode (1 min or more) of a MAP < 65 mmHg, and the absolute maximum decrease below a MAP of 65 mmHg. In addition, we will assess causes of intraoperative hypotension and investigate associations between intraoperative hypotension and postoperative outcomes. Discussion: There are only sparse data on the effect of using HPI monitoring on intraoperative hypotension in patients having elective major non-cardiac surgery. Therefore, we built up a European, multicenter, prospective, observational registry to describe the incidence, duration, severity, and causes of intraoperative hypotension when using HPI monitoring in patients having elective major non-cardiac surgery.

摘要

背景

术中低血压在接受非心脏手术的患者中很常见,且与术后急性心肌损伤、急性肾损伤及死亡率相关。对麻醉医生而言,避免术中低血压是一项复杂的任务。利用人工智能从临床和血流动力学数据预测低血压是一种创新且引人关注的方法。AcumenTM低血压预测指数(HPI)软件(爱德华生命科学公司;美国加利福尼亚州欧文市)是利用人工智能——具体而言是机器学习——开发的,可根据血压波形特征预测低血压。我们旨在描述在接受择期大型非心脏手术的患者中使用HPI监测时术中低血压的发生率、持续时间、严重程度及原因。

方法

我们建立了一个欧洲多中心前瞻性观察性注册库,纳入来自五个欧洲国家的至少700例可评估患者。该注册库包括同意参与的成年人(≥18岁),他们计划接受全身麻醉下的择期大型非心脏手术,预计手术持续至少120分钟,且计划放置动脉导管并进行HPI监测。主要目标是量化和描述使用HPI监测时的术中低血压(定义为平均动脉压[MAP]<65mmHg)。这包括时间加权平均(TWA)MAP<65mmHg、MAP为65mmHg时的曲线下面积、MAP<65mmHg的发作次数、至少有一次发作(持续1分钟或更长时间)MAP<65mmHg的患者比例,以及MAP低于65mmHg时的绝对最大降幅。此外,我们将评估术中低血压的原因,并研究术中低血压与术后结局之间的关联。

讨论

关于在接受择期大型非心脏手术的患者中使用HPI监测对术中低血压影响的数据非常稀少。因此,我们建立了一个欧洲多中心前瞻性观察性注册库,以描述在接受择期大型非心脏手术的患者中使用HPI监测时术中低血压的发生率、持续时间、严重程度及原因。

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