Background: Intraoperative arterial hypotension is associated with poor postoperative outcomes. The Hypotension Prediction Index developed from machine learning, predicts the occurrence of arterial hypotension from the analysis of the arterial pressure waveform. The use of this index can reduce the duration and severity of intraoperative hypotension in adults undergoing noncardiac surgery.~Methods: We will conduct a multicenter, randomized, 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. Hemodynamic management in the control group will be based on standard hemodynamic parameters. Hemodynamic management of patients in the intervention group will be based on functional hemodynamic parameters provided by Hemosphere platform (Edwards Lifesciences Ltd), including dynamic arterial elastance, dP/dtmax and the Hypotension Prediction Index. Tissue oxygen saturation will be non-invasively and continuously recorded 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 intraoperative time-weighted average with a mean arterial pressure < 65mmHg.~Discussion: The aim of the study is to determine whether a goal-directed algorithm based on the prevention of arterial hypotension using the Hypotension Prediction Index reduces the duration and severity of intraoperative hypotension when compared with the recommended standard therapy and if this intraoperative strategy affects tissue oxygenation and organ perfusion.
The aim of the study is to determine whether a goal-directed algorithm based on the prevention of arterial hypotension using the Hypotension Prediction Index reduces the duration and severity of intraoperative hypotension when compared with the recommended standard therapy and if this intraoperative strategy affects tissue oxygenation and organ perfusion.