Machine Learning-Assisted Intraoperative Hypotension Management: Developing Personalized Treatment Recommendations

What this Trial Is About

Researchers are investigating intraoperative hypotension, which is a drop in blood pressure during surgery under general anesthesia. This condition is common and may reduce blood flow to vital organs, leading to complications after surgery. The study aims to improve understanding of this condition by collecting detailed blood pressure and heart function data and analyzing it with machine learning to identify different patterns and causes of hypotension. The study observes adult patients undergoing planned surgeries lasting at least two hours with continuous invasive arterial blood pressure monitoring. During surgery, detailed hemodynamic measurements such as cardiac output, stroke volume, and vascular resistance are recorded regularly. When low blood pressure occurs, its timing, severity, and treatments like fluids or medications are noted. The study does not change patient care but collects data to analyze treatment responses and physiological subtypes of hypotension. Participants will have their blood pressure and heart function continuously monitored during surgery. The study gathers information on treatments used and how quickly blood pressure recovers. Researchers will analyze relationships between patient factors like age, sex, and health conditions with hypotension episodes. The main outcome is classifying hypotension subtypes during surgery, aiming to support future personalized treatment recommendations. Data privacy and ethical standards are maintained throughout the study.

Machine Learning-Assisted Management of Intraoperative Hypotension for Personalized Treatment