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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.
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 (DMM). 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 OAZ, the algorithm quantifies the inadequacy of anesthesia level through a vector connecting the patient's current position on the DMM to the central point identified by the coordinates BIS= 50 and MAP = 75mmHg. Thereafter, the analysis of the vector main components allows the generation of two coefficients that are used to set out a balanced intervention on the hypnotic and 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 This is a prospective study involving a single cohort of patients without major comorbidity and candidate for minor superficial surgery. All patients received Propofol and Remifentanil administered by TCI systems as part of Total Intravenous Anaesthesia.
The algorithm was tested during maintenance of anesthesia defined as the period between skin incision and completion of surgical resection. In this step of the procedure, the titration strategy for anesthetic drug concentrations was suggested by TI.VA algorithm.
Data was collected automatically using dedicated software.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| TI.VA group | Experimental | The titration of Propofol and Remifentanil levels will be guided by TI.VA algorithm in the time between skin incision and completion of surgical resection. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| TI.VA algorithm: decision support system | Other | TI.VA algorithm uses BIS and MAP values as control variables to suggest the intervention on propofol and remifentanil levels. |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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The inclusion criteria were: :
The exclusion criteria were:
These criteria were selected according to the risk mitigation strategy described in the protocol.
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| Name | Affiliation | Role |
|---|---|---|
| Emiliano Tognoli, MD | Fondazione IRCCS Istituto Nazionale dei Tumori, Milano | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Fondazione IRCCS Istituto Nazionale dei Tumori | Milan | 20133 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 21190458 | Background | 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. No abstract available. | |
| Background | A.R. Absalom, MMRF Struys. Overview on Target Controlled Infusion and Total Intravenous Anaesthesia. 2Ed. Gent, Academia Press 2019. | ||
| 21350226 |
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all IPD that underlie results in a publication
Nov 2021
free
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| Type | Date | Date Unknown |
|---|---|---|
| Release | Jan 17, 2023 | |
| Reset | Nov 3, 2023 |
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| Release Date | Unrelease Date | Unrelease Date Unknown | Reset Date | MCP Release Number |
|---|---|---|---|---|
| Jan 17, 2023 | Nov 3, 2023 |
This is a prospective study involving a series of consecutive patients.
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| during the surgical procedure intervention |
| 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 |
| Background |
| 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. No abstract available. |
| 1588504 | Background | Varvel JR, Donoho DL, Shafer SL. Measuring the predictive performance of computer-controlled infusion pumps. J Pharmacokinet Biopharm. 1992 Feb;20(1):63-94. doi: 10.1007/BF01143186. |
| 37638086 | Derived | Tognoli E, Luigi M. Using the TI.VA algorithm to titrate the depth of general anaesthesia: a first-in-humans study. BJA Open. 2023 Jun 16;7:100203. doi: 10.1016/j.bjao.2023.100203. eCollection 2023 Sep. |