Comparing Hypotension Prediction Index and Arterial Pressure Cardiac Output Monitoring During Major Abdominal Surgery

What this Trial Is About

Researchers are evaluating different types of advanced hemodynamic monitoring in patients undergoing major abdominal surgery to reduce episodes of low blood pressure during surgery. The study compares the Hypotension Prediction Index, a machine learning algorithm that predicts and helps prevent low blood pressure, with the arterial pressure cardiac output algorithm currently used in standard care. The aim is to see which method better reduces the rate of intraoperative hypotension, which is linked to serious complications like stroke, heart injury, and kidney damage. Participants will be randomly assigned to one of two groups during surgery. Group A receives monitoring with the arterial pressure cardiac output algorithm using the FloTrac sensor, which guides treatment after low blood pressure occurs. Group B receives monitoring guided by the Hypotension Prediction Index algorithm, which predicts hypotension before it happens to allow earlier intervention. The study collects data from the start of anesthesia until discharge from the post-anesthesia care unit. During the study, patients will have their blood pressure continuously monitored, and treatments will be adjusted based on their assigned monitoring method. Researchers will measure the time and severity of low blood pressure below 65 mmHg, as well as other outcomes such as very low blood pressure episodes, high blood pressure episodes, heart injury, kidney injury, hospital stay length, and complications up to 30 days after surgery. The study involves a single-blind design and includes patients aged 18 to 80 undergoing major abdominal surgery.

The Hypotension Prediction Index in Major Abdominal Surgery