Clinical trial NCT04079036: Decision Support System for Anesthetists
Official title: Decision Support System for Anesthetists for In-room Monitoring of the Level of Consciousness, Neuro-muscular Blocking and Nociception Employing Data Fusion and Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Anesthesia
Keywords: General Anesthesia
Anesthesia Awareness
Anesthesia overdose
Lead sponsor: University of Sao Paulo General Hospital
Sponsor class: OTHER
Collaborators: name: University of Sao Paulo
name: Fundação de Amparo à Pesquisa do Estado de São Paulo
First posted: 2019-09-06
Enrollment: 360
Sex: ALL
Minimum age (years): 1
Maximum age (years): 80
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CROSSOVER
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients undergoing surgical procedures with general anesthesia

Brief summary: 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

* Patients under general anesthesia

Exclusion Criteria:

* Cerebral Palsy patients

Primary outcomes:
measure: Heart Rate
timeFrame: 2 to 3 months of different surgical procedures
measure: Arterial blood pressure
timeFrame: 2 to 3 months of different surgical procedures
measure: Mean Arterial Pressure
timeFrame: 2 to 3 months of different surgical procedures
measure: Non Invasive Blood pressure
timeFrame: 2 to 3 months of different surgical procedures
measure: Conscience Level
timeFrame: 2 to 3 months of diferent surgical procedures
measure: Inspired anesthetic concentration
timeFrame: 2 to 3 months of different surgical procedures
measure: Inspired carbon dioxide concentration
timeFrame: 2 to 3 months of different surgical procedures
measure: Inspired Nitrous Oxide concentration
timeFrame: 2 to 3 months of different surgical procedures
measure: Blood oxygen saturation
timeFrame: 2 to 3 months of different surgical procedures
measure: Exhaled Carbon Dioxide concentration
timeFrame: 2 to 3 months of different surgical procedures
measure: Exhaled Anesthetic concentration
timeFrame: 2 to 3 months of different surgical procedures
measure: Anesthetic agent infusion rate
timeFrame: 2 to 3 months of different surgical procedures
measure: Analgesic agent infusion rate
timeFrame: 2 to 3 months of different surgical procedures
measure: Neuro-muscular Block agent infusion rate
timeFrame: 2 to 3 months of different surgical procedures
measure: Ventilation respiratory rate
timeFrame: 2 to 3 months of different surgical procedures
measure: Ventilation Tidal volume
timeFrame: 2 to 3 months of different surgical procedures
measure: Ventilation Minute Volume
timeFrame: 2 to 3 months of different surgical procedures
measure: Ventilation maximum pressure per cycle
timeFrame: 2 to 3 months of different surgical procedures
measure: Ventilation Plateau pressure
timeFrame: 2 to 3 months of different surgical procedures
measure: Ventilation PEEP
timeFrame: 2 to 3 months of different surgical procedures
measure: Primary anesthesia Data - related to the manual infusion of drugs
timeFrame: 2 to 3 months of different surgical procedures
measure: Preoperative patient medical records - General state of the patient in ASA.
timeFrame: 2 to 3 months of different surgical procedures
measure: Preoperative patient medical records - General state of the patient in METS.
timeFrame: 2 to 3 months of different surgical procedures
measure: Preoperative patient medical records - Clinical state of the patient - Age
timeFrame: 2 to 3 months of different surgical procedures
measure: Preoperative patient medical records - Clinical state of the patient - gender
timeFrame: 2 to 3 months of different surgical procedures
measure: Preoperative patient medical records - Clinical state of the patient - Weight
timeFrame: 2 to 3 months of different surgical procedures
measure: Preoperative patient medical records - Clinical state of the patient - Height
timeFrame: 2 to 3 months of different surgical procedures
Secondary outcomes:
Other outcomes:
Locations:
city: São Paulo
country: Brazil
facility: Hospital das Clínicas - Faculdade de Medicina da Universidade de Sao Paulo
Study officials:
affiliation: University of Sao Paulo School of Medicine
name: Joaquim E Vieira, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References:
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