Validating a Non-Invasive Prediction Algorithm for Low Blood Pressure During Surgery in Adults

What this Trial Is About

Researchers are evaluating a prediction algorithm that uses non-invasive monitoring devices to forecast low blood pressure during surgery, a condition called intraoperative hypotension. This condition is linked to poorer outcomes after surgery, including complications like kidney injury. The study aims to test how well this algorithm works in real clinical settings and to assess its usefulness and limitations. Participants will undergo general anesthesia for elective surgery while connected to five common non-invasive monitoring devices blood pressure, electrocardiogram, oxygen saturation, carbon dioxide levels, and anesthesia depth monitors. Data from these devices will be analyzed by the prediction algorithm to anticipate hypotension events. This study follows a single group design without comparison arms. During the study, participants will be closely monitored throughout their surgery with these devices. Researchers will measure the algorithms accuracy by analyzing how well it predicts hypotension five minutes before it occurs. This involves collecting and assessing data during anesthesia to confirm the algorithms performance. The total study period includes preoperative preparation and surgery monitoring, with data analysis concluding after the surgery.

External Validation of Prediction Algorithm Using Non-invasive Monitoring Device for Intraoperative Hypotension