Anesthesia Clinical Trial
— TIVAlyOfficial title:
The TI.VA Algorithm: Vector Analysis Applied to a Decision-Making Matrix to Model the Reactive Control Strategy During General Anesthesia: a First-in-Humans Test.
Verified date | January 2022 |
Source | Fondazione IRCCS Istituto Nazionale dei Tumori, Milano |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Interventional |
The TI.VA algorithm is a new method to titrate the anesthetic drug concentrations whenever the planned level of anesthesia results to be not appropriate to blunt the patient's reaction to surgical stimulation. TI.VA is a multiple inputs/multiple outputs algorithm. The control variables are the bispectral index (BIS) and the mean arterial pressure (MAP) combined in a decision-making matrix. The optimal range for the two control variables (BIS: 540-60 and MAP: 65-75 mmHg) identified the Optimal Anesthesia Zone (OAZ) at the center of the matrix. Any time one or both control variables escape from the PAZ, the algorithm proposes an intervention on the hypnotic and/or opioid levels (algorithm outputs). A First-in-Humans study was designed to capture preliminary data on the safety and performance of the TI.VA algorithm.
Status | Completed |
Enrollment | 5 |
Est. completion date | January 31, 2021 |
Est. primary completion date | January 31, 2021 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years to 65 Years |
Eligibility | The inclusion criteria were: : - age 18-65 years at the time of recruitment. - candidates for curative surgery for breast cancer. - American Society of Anaesthesiologists (ASA) status I/II. The exclusion criteria were: - ASA status > II. - counter-indications for use of the drugs employed in this protocol. - pregnancy or lactation. - incapacity to understand the study explanation and sign the informed consent form. These criteria were selected according to the risk mitigation strategy described in the protocol. |
Country | Name | City | State |
---|---|---|---|
Italy | Fondazione IRCCS Istituto Nazionale dei Tumori | Milano |
Lead Sponsor | Collaborator |
---|---|
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano |
Italy,
A.R. Absalom, MMRF Struys. Overview on Target Controlled Infusion and Total Intravenous Anaesthesia. 2Ed. Gent, Academia Press 2019.
Absalom AR, De Keyser R, Struys MM. Closed loop anesthesia: are we getting close to finding the holy grail? Anesth Analg. 2011 Mar;112(3):516-8. doi: 10.1213/ANE.0b013e318203f5ad. — View Citation
Brown EN, Lydic R, Schiff ND. General anesthesia, sleep, and coma. N Engl J Med. 2010 Dec 30;363(27):2638-50. doi: 10.1056/NEJMra0808281. Review. — View Citation
Varvel JR, Donoho DL, Shafer SL. Measuring the predictive performance of computer-controlled infusion pumps. J Pharmacokinet Biopharm. 1992 Feb;20(1):63-94. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Adverse Events | An adverse event is defined as any untoward medical occurrence in the study period. Intra-operative adverse events were reported using the institutional incident reporting system. Data was collected in the time between skin incision and the completion of surgical resection. | during the surgical procedure intervention | |
Secondary | Stability of the Control Variables | To characterize the patient's repose to surgery, the TI.VA algorithm uses a Decision-Making Matrix drawn by crossing BIS (Min-max: 0-100, optimal range 40-60 ) and Mean Arterial Pressure (Min-max: 0-150mmHg. Optimal range 65-75mmHg). The Optimal anesthesia zone is defined as the area of the Decision-Making Matrix identify by the optimal range for the two control variables (BIS and MAP).
The stability of the control variables during anesthesia was quantified by the percentage of monitoring points registered in the Optimal Anaesthesia Zone during anesthesia. A monitoring point is understood as a value of BIS and MAP recorded at the same time. The system records a monitoring point every 5 seconds. Data was collected in the time between skin incision and the completion of surgical resection. |
during the surgical procedure intervention | |
Secondary | Performance Error analysis | The performance of the algorithm was assessed using the performance error (PE), median PE (MDPE), median absolute PE (MDAPE), and wobble according to the method of Performance Error.
Data was collected in the time between skin incision and the completion of surgical resection. |
during the surgical procedure intervention |
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