Decision Support System for Anesthetists (Serenity)

University of Sao Paulo General Hospital

Status: Unknown

Conditions: Anesthesia

Study type: Observational

Funder types: Other

Identifiers

NCT04079036

CAAE 03424918.6.0000.0068

Details and patient eligibility

About

The balanced anesthesia process contains three main parts: the control of hypnosis, analgesia, and neuromuscular blockade. For the induction phase, the anesthesiologist performs protocols based on prior planning specific to each patient and usually performs these controls by monitoring the classic vital signs and other clinical signs for the maintenance phase.

In a way, this professional is the controller in a control system that acts on the plant (the patient) through the infusion of hypnotic drugs, analgesics and neuromuscular blockers. In addition, the anesthesiologist estimates the state of consciousness, the level of analgesia and the level of neuromuscular blockage through other indirect measures, as well as a state observer.

There are different techniques for direct monitoring of these three anesthesia variables (DoA, NMB and NoL), such as BIS and Narcotrend, but all have some disadvantages, especially when the anesthesia process combines different drugs. This work proposes a new way of evaluating DoA, NMB and NoL using data fusion techniques to combine classical clinical signs with advanced EEG monitoring techniques to provide a decision support system for the anesthesiologist.

Full description

The balanced anesthesia process contains three main parts: the control of hypnosis, the analgesia and neuromuscular blockade. For the induction phase, the anesthesiologist performs protocols based on prior planning specific to each patient. Normally, the anesthesiologist controls the process by monitoring the classical vital signs and other clinical most common signs during the maintenance phase. In a way, this professional is the controller in a control system that acts on the plant (the patient) through the infusion of hypnotic and analgesic drugs and neuromuscular blockers.

In addition, the anesthesiologist estimates the the level of consciousness, of nociception and the level of neuromuscular blockade through these indirect measurements, just as a state observer in a control system would do.

There are different techniques for the direct monitoring of these three variables of anesthesia (DoA, NMB and NoL), such as BIS and Narcotrend, but all of them present a few disadvantages and mis-measurements, especially when the anesthesia process combines different drugs.

This work proposes a new way of evaluating DoA, NMB and NoL, using techniques to combine classical clinical signs with advanced EEG monitoring, to provide a decision support system for the anesthesiologist.

For this, we will perform data acquisition from the equipment usually used in surgical procedures with general anesthesia, such as ECG, EEG, blood pressure, mechanical ventilation, among others.

In short, all data of the patient's vital signs during the procedure and the actions taken by the anesthesiologist and surgeons.

The data will be concentrated on a specific equipment, and will be analyzed together with the data of other patients to improve the mathematical models involved in the process.

Enrollment: 360 estimated patients

Sex: All

Ages: 1 month to 80 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Patients under general anesthesia

Exclusion criteria

• Cerebral Palsy patients

Trial design

360 participants in 12 patient groups

Underweight Adult Male

Description:

Male patients with Underweight BMI classification and more than 20 years old https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html

Healthy Weight Adult Male

Description:

Male patients with Healthy Weight BMI classification and more than 20 years old

Overweight Adult Male

Description:

Male patients with OverWeight or Obese BMI classification and more than 20 years old

Underweight Adult Female

Description:

Female patients with Underweight BMI classification and more than 20 years old

Healthy Weight Adult Female

Description:

Female patients with Healthy Weight BMI classification and more than 20 years old

Overweight Adult Female

Description:

Female patients with Overweight or Obese BMI classification and more than 20 years old

Underweight children Male

Description:

Male patients less than 20 year old, and with Underweight BMI classification https://www.cdc.gov/healthyweight/assessing/bmi/childrens_bmi/about_childrens_bmi.html

Healthy Weight children Male

Description:

Male patients less than 20 year old, and with Healthy Weight BMI classification

Overweight children Male

Description:

Male patients less than 20 year old, and with Overweight or Obese BMI classification

Underweight children Female

Description:

Male patients less than 20 year old, and with Underweight BMI classification https://www.cdc.gov/healthyweight/assessing/bmi/childrens_bmi/about_childrens_bmi.html

Healthy Weight children Female

Description:

Female patients less than 20 year old, and with Overweight or Obese BMI classification

Overweight children Female

Description:

Female patients less than 20 year old, and with Overweight or Obese BMI classification