Clinical Research Directory

AI-Assisted Blood Pressure Control During Anesthesia

Sponsor: Beijing Tsinghua Chang Gung Hospital

Summary

This proof-of-concept randomized controlled trial evaluates a reinforcement learning (RL)-based clinical decision support system for intraoperative hemodynamic management during non-cardiac surgery. Background: Intraoperative hypotension is common during general anesthesia and is associated with adverse outcomes including acute kidney injury, myocardial injury, and increased mortality. Current hemodynamic management relies on the individual anesthesiologist's clinical judgment, which can vary in consistency and timeliness. An RL-based system that learns optimal vasoactive agent dosing strategies from clinical data may help standardize and improve real-time hemodynamic decision-making. Purpose: The primary objective is to evaluate whether the RL-based decision support system can learn intraoperative hemodynamic management decisions comparable to those of experienced anesthesiologists, as measured by the mean absolute error (MAE) between RL-recommended and clinician-executed vasoactive agent doses. The secondary objective is to assess whether RL-guided management improves clinical hemodynamic outcomes, including the time-weighted average of hypotension and the percentage of time with mean arterial pressure within the target range. Participants: Adult patients (aged 18 to 85 years, ASA I-IV) scheduled for elective non-cardiac surgery under general anesthesia with continuous invasive arterial blood pressure monitoring. Procedures: Participants will be randomly assigned (1:1) to one of two groups. In the RL-guided group, the anesthesiologist will receive real-time vasoactive agent dosing recommendations from the decision support system displayed on a bedside screen; the anesthesiologist retains full clinical autonomy over all final decisions. In the standard care group, the anesthesiologist will manage hemodynamics according to institutional standard practice without input from the system. The patient and the outcomes assessor will be masked to group assignment. Data collection covers the intraoperative period and 30-day postoperative follow-up.

Official title: A Dual-Center, Randomized, Controlled, Proof-of-Concept Trial to Evaluate an AI-Based Decision Support System for Intraoperative Blood Pressure Management

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Locations (2)