AI-Assisted Blood Pressure Control During Anesthesia

NCT ID: NCT07123675

Last Updated: 2025-08-14

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

40 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-09-01

Study Completion Date

2025-12-15

Brief Summary

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This study is being done to see if a new computer-based tool can help anesthesiologists keep patients' anesthesia levels more stable during surgery.

Background: During general anesthesia, doctors carefully adjust medication doses to keep patients safely and comfortably unconscious. This requires constant attention. Researchers want to find out if a smart computer assistant can help with this task.

Purpose of the Study: The main goal is to compare two ways of managing anesthesia. One way is the standard method used by doctors today. The other way is having the doctor use suggestions from a new computer program designed to recommend medication doses in real-time. We want to see if using the computer tool can lead to more stable anesthesia, potentially using less medication and helping patients recover more smoothly.

Who Can Participate: The study will include adult patients who are scheduled to have surgery that requires general anesthesia.

What Will Happen in the Study: If you choose to join, you will be randomly assigned (like flipping a coin) to one of two groups:

Standard Care Group: Your anesthesiologist will manage your anesthesia medications based on their expert judgment and normal practice, as is done every day.

Computer-Assisted Group: Your anesthesiologist will also manage your anesthesia, but they will see suggestions from a computer system on a screen. The doctor will always have the final say and can choose to follow or ignore the computer's advice to ensure your safety.

In both groups, you will receive safe and complete anesthetic care. Your participation in the study will last for the duration of your surgery and the immediate recovery period. Researchers will look at information like the amount of medication used and how stable your anesthesia level was.

Detailed Description

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Rationale: Precise control of anesthetic depth during general anesthesia is crucial for patient safety and optimal outcomes. Suboptimal dosing can lead to intraoperative awareness, hemodynamic instability, or excessive drug administration, which may delay recovery. This study will evaluate a novel reinforcement learning (RL) based clinical decision support system designed to provide real-time, personalized dosing recommendations for anesthetic agents.

Hypothesis: The use of an RL-based decision support system for guiding anesthetic drug administration results in a greater percentage of time within a target range of anesthetic depth compared to standard manual practice by anesthesiologists.

Study Design: This is a prospective, dual-center, parallel-group, randomized controlled superiority trial.

Objectives:

Primary Objective: To compare the percentage of case time that the Bispectral Index (BIS) is maintained within the target range of 40 to 60 between the RL-guided group and the standard care group.

Secondary Objectives: To compare total consumption of anesthetic agents (e.g., propofol and remifentanil), incidence and duration of intraoperative hypotension, time to tracheal extubation, and length of stay in the post-anesthesia care unit (PACU).

Interventions:

Intervention Group (RL-Guided): Anesthesiologists will manage general anesthesia with the aid of a real-time decision support system. The system, powered by a reinforcement learning algorithm, will display continuous dosing recommendations for propofol and remifentanil. The attending anesthesiologist retains full clinical autonomy and is responsible for all final dosing decisions.

Control Group (Standard Care): Anesthesiologists will manage general anesthesia according to their clinical experience and institutional standard of care, without input from the decision support system.

Study Population: Adult patients (aged 18-65 years), ASA physical status I-III, scheduled for elective surgery expected to last at least two hours under general anesthesia. Key exclusion criteria include known allergies to anesthetic agents, severe cardiovascular or respiratory disease, and emergency surgery.

Conditions

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General Anesthesia

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

TREATMENT

Blinding Strategy

DOUBLE

Participants Outcome Assessors
The care provider (anesthesiologist) is not masked to the intervention, as they must be able to view and interact with the clinical decision support system in the experimental arm. The patient and the outcomes assessor will be masked.

Study Groups

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Experimental: AI-Assisted Anesthesia Management

Participants in this arm will have their general anesthesia managed by an anesthesiologist with real-time guidance from an AI-based clinical decision support system. The system provides recommendations for drug administration and physiological parameter adjustments.

Group Type EXPERIMENTAL

Device: AI Clinical Decision Support System

Intervention Type DEVICE

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

Active Comparator: Standard Anesthesia Care

Participants in this arm will receive standard-of-care general anesthesia management. The anesthesiologist will make all clinical decisions based on their professional judgment and standard institutional practices, without the aid of the investigational AI system.

Group Type ACTIVE_COMPARATOR

Procedure: Standard of Care Anesthesia

Intervention Type PROCEDURE

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.

Interventions

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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

Intervention Type DEVICE

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.

Intervention Type PROCEDURE

Eligibility Criteria

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Inclusion Criteria

* 1\. Age range: 18 to 85 years. 2. Patients undergoing non-cardiac surgery. 3. Receiving intravenous-inhalation combined general anesthesia with tracheal intubation during surgery.

4\. Patients undergoing elective surgery. 5. Anesthesia maintenance plan includes:
1. Propofol for continuous sedation during the maintenance phase;
2. Remifentanil for continuous analgesia during the maintenance phase;
3. Sevoflurane or desflurane for inhalation anesthesia. 6. ASA physical status I-IV. 7. Continuous monitoring of blood pressure, heart rate, and Bispectral Index (BIS) during surgery.

8\. Continuous invasive arterial blood pressure monitoring during surgery.

Exclusion Criteria

* 1\. Emergency surgery. 2. Continuous infusion of remifentanil or propofol for less than 30 minutes during surgery.

3\. Receiving continuous intravenous sedatives other than propofol, or continuous infusion of intravenous sedatives other than propofol.

4\. Receiving continuous intravenous analgesics other than remifentanil, or continuous infusion of intravenous analgesics other than remifentanil.

5\. Inhaled anesthetic maintenance concentration is not equal to 0.5 Minimum Alveolar Concentration (MAC).

6\. Allergy to propofol or allergic reaction to remifentanil. 7. Severe obesity (BMI ≥ 35 kg/m²).
Minimum Eligible Age

18 Years

Maximum Eligible Age

65 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Peking University People's Hospital

OTHER

Sponsor Role collaborator

Beijing Tsinghua Chang Gung Hospital

OTHER

Sponsor Role lead

Responsible Party

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Zhifeng Gao

Professor, Department of Anesthesiology

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Peking University People's Hospital

Beijing, Beijing Municipality, China

Site Status

Beijing Tsinghua Changgung Hospital

Beijing, Beijing Municipality, China

Site Status

Countries

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China

Central Contacts

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Gao Zhifeng, MD

Role: CONTACT

15801249466

Zheng Zhang, MD

Role: CONTACT

18801238359

Facility Contacts

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Yi Feng, MD

Role: primary

+8618975802249

Zheng Zhang, MD

Role: primary

18801238359

Other Identifiers

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24444-4-03

Identifier Type: -

Identifier Source: org_study_id

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