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AI-Assisted Blood Pressure Control During Anesthesia

NCT07123675 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 40

Last updated 2026-04-06

No results posted yet for this study

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.

Conditions

  • Intraoperative Hypotension
  • Hemodynamic Instability

Interventions

DEVICE

Device: AI Clinical Decision Support System

An AI-based software system that provides real-time, on-screen recommendations to the anesthesiologist regarding drug administration and adjustments to maintain optimal patient physiological parameter

PROCEDURE

Procedure: Standard of Care Anesthesia

Standard anesthesia management according to institutional guidelines. The attending anesthesiologist makes all clinical decisions based on their expertise and judgment without input from the investigational AI system.

Sponsors & Collaborators

  • Peking University People's Hospital

    collaborator OTHER

  • Beijing Tsinghua Chang Gung Hospital

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
DOUBLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
85 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-06-01
Primary Completion
2027-06-01
Completion
2027-12-15

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07123675 on ClinicalTrials.gov