Anesthesia and Critical Care, Hospital Juan Ramon Jimenez, Huelva, Spain
School of Medicine and Health Science, Universitat Internacional de Catalunya, Barcelona, Spain.
BMJ Open. 2022 Jun 2;12(6):e051728. doi: 10.1136/bmjopen-2021-051728.
Intraoperative arterial hypotension is associated with poor postoperative outcomes. The Hypotension Prediction Index (HPI) developed using machine learning techniques, allows the prediction of arterial hypotension analysing the arterial pressure waveform. The use of this index may reduce the duration and severity of intraoperative hypotension in adults undergoing non-cardiac surgery. This study aims to determine whether a treatment protocol based on the prevention of arterial hypotension using the HPI algorithm reduces the duration and severity of intraoperative hypotension compared with the recommended goal-directed fluid therapy strategy and may improve tissue oxygenation and organ perfusion.
We will conduct a multicentre, randomised, controlled trial (N=80) in high-risk surgical patients scheduled for elective major abdominal surgery. All participants will be randomly assigned to a control or intervention group. Haemodynamic management in the control group will be based on standard haemodynamic parameters. Haemodynamic management of patients in the intervention group will be based on functional haemodynamic parameters provided by the HemoSphere platform (Edwards Lifesciences), including dynamic arterial elastance, dP/dt and the HPI. Tissue oxygen saturation will be recorded non-invasively and continuously by using near-infrared spectroscopy technology. Biomarkers of acute kidney stress (cTIMP2 and IGFBP7) will be obtained before and after surgery. The primary outcome will be the intraoperative time-weighted average of a mean arterial pressure <65 mm Hg.
Ethics committee approval was obtained from the Ethics Committee of Hospital Gregorio Marañón (Meeting of 27 July 2020, minutes 18/2020, Madrid, Spain). Findings will be widely disseminated through peer-reviewed publications and conference presentations.
NCT04301102.
术中动脉低血压与术后不良结局相关。使用机器学习技术开发的低血压预测指数（HPI）通过分析动脉压力波形来预测动脉低血压。在接受非心脏手术的成人中使用该指数可能会减少术中低血压的持续时间和严重程度。本研究旨在确定基于使用 HPI 算法预防动脉低血压的治疗方案是否与推荐的目标导向液体治疗策略相比，可减少术中低血压的持续时间和严重程度，并可能改善组织氧合和器官灌注。
我们将在计划接受择期大腹部手术的高危手术患者中进行一项多中心、随机、对照试验（N=80）。所有参与者将被随机分配到对照组或干预组。对照组的血流动力学管理将基于标准血流动力学参数。干预组患者的血流动力学管理将基于 HemoSphere 平台（爱德华兹生命科学公司）提供的功能血流动力学参数，包括动态动脉弹性、dp/dt 和 HPI。组织氧饱和度将通过近红外光谱技术无创连续记录。手术前后将获得急性肾应激的生物标志物（cTIMP2 和 IGFBP7）。主要结局将是术中平均动脉压<65mmHg 的时间加权平均值。
医院 Gregorio Marañón 伦理委员会已批准（2020 年 7 月 27 日会议，第 18/2020 分钟，马德里，西班牙）。研究结果将通过同行评审的出版物和会议报告广泛传播。
NCT04301102。
