Hemodynamic Monitoring and Prediction of Hypotension Through the HPI Algorithm During Major Gynecologic Oncologic Surgery: a Randomized Controlled Trial

Intraoperative hypotension (IOH) represents a common event during general anesthesia (GA), with an estimated incidence between 5% and 99%, according to definition adopted [1].

Actually, a mean arterial pressure (MAP) below 65 mmHg is considered an appropriate definition of IOH [2].

Hypotension mainly occurs during anaesthesia due to three pathophysiological dysregulations: hypovolemia and consecutively decreased cardiac output, myocardial depression and low systemic vascular resistance [3]. IOH has been associated with postoperative acute acute kidney injury and myocardial injury; it seems that the cumulative time spent in hypotension increases the risk, and this has implications as even relatively short episodes of hypotension that are treated promptly can over time reach accumulated hypotension time associated with increased injury rates [4-7]. Therefore, anaesthetic management that aims to prevent IOH using a "pro-active" treatment protocol might potentially reduce the amount and severity of IOH, perioperative complications and mortality. In order to detect and treat these haemodynamic alterations, advanced haemodynamic monitoring combined with a treatment algorithm can be used [8].

The Hypotension Prediction Index (HPI) algorithm was recently established by Edwards Lifesciences (Irvine, USA). Based on the Edward´s monitoring platform (HemoSphere), HPI is a monitoring tool which aims to predict IOH up to 15 min before its onset [9, 10]. HPI is a unitless number that ranges from 1 to 100, and as the number increases, the risk of an event occurring in the future increases. The HPI was developed using machine learning methods and is a data-driven model developed from over 200,000 hypotensive patient events and it predicts upcoming hypotensive events based on features of the arterial pressure waveform [9].

When HPI rises over 85, the monitor Hemosphere provides a secondary screen showing the following hemodynamic parameters: stroke volume variation (SVV) as indicator of fluid responsiveness - preload, radial dP/dtmax as indicator of cardiac contractility, and dynamic elastance (Eadyn) as dynamic indicator of resistance - afterload. Hemodynamic variables, hemodynamic diagnostic guidance and the definition of a treatment protocol, should allow for determination and treatment of the underlying cause of the impending hypotension.