System for identifying patient response to anesthesia infusion

• US 20070015972A1
• Filed: 06/21/2004
• Published: 01/18/2007
• Est. Priority Date: 06/19/2003
• Status: Active Grant

• Current Assignee: Wayne State University
• Original Assignee: Wayne State University
• Inventors: Wang, Hong, Yin, Gang, Wang, Le Yi
• Granted Patent: US 8,998,808 B2
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/377 using evoked responses
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61M 2202/0241 Anaesthetics; Analgesics
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2230/10 Electroencephalographic signals
• CPC Class Codes: A61M 5/142 Pressure infusion, e.g. using pumps
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture

Abstract

A generic model structure captures basic characteristics in BIS-based patients'"'"' responses to anesthesia and surgical stimulation, the model being used in combination with the insight of an anesthesiologist. The model structure represents the patient response with a time delay, a time constant for response speed, and a nonlinear function for drug sensitivity. Clinical data confirms the model structure and is used to establish parameters and function forms for individual patients. A feedback and predictive control strategy for anesthesia drug infusion is then introduced on the basis of the patient model. Feedback control alone cannot avoid large fluctuations in BIS values when significant surgical stimulation is imposed, as a result of time delays in a patient'"'"'s response to drug infusion. Predictive control attenuates fluctuations of BIS levels from surgical stimulation.

Claims

1. A method of assisting a human expert in reducing predictable variations in the depth of anesthesia during the administration of a medical anesthesia drug to a patient, the method comprising the step of solving:

$y=f_p<br><br>\left(x\right)=C_1<br><br>\frac{x}{x_1}<br><br>{\mathrm{\Phi <br><br>}}_1<br><br>\left(x\right)+C_2<br><br>\frac{x}{x_2}<br><br>{\mathrm{\Phi <br><br>}}_2<br><br>\left(x\right)+C_3<br><br>\frac{x}{x_3}<br><br>{\mathrm{\Phi <br><br>}}_3<br><br>\left(x\right)$

where the coefficients C₁, C₂, C₃, as well as the time periods τ

_p (initial time delay after drug infusion) and T_p (time constant representing speed of response) are initiated by assessment of a human expert.

2. The method of claim 1, where the human expert performs the step of assigning a relative value between 1 and 10 to represent the patient'"'"'s response to infusion of the anesthesia drug, where 1 represents the slowest and 10 represents the fastest.

3. The method of claim 1, wherein typical set points are selected to be approximately x₁≅

50, x₂ ≅

100, and x₃ ≅

150.

4. A method of determining a model that corresponds to a predicted response of a patient to anesthesia drug delivery, the method comprising the steps of:

first determining an initial time delay τ

_p after drug infusion for the patient;

second determining a time constant T_p representing speed of response of the patient; and

third determining a nonlinear static function ƒ

_p representing the sensitivity of the patient to a dosage of the anesthesia drug at steady state.

5. The method of claim 4, wherein said steps of first, second, and third determining are implemented in a Weiner structure.

6. The method of claim 4, wherein said steps of first, second, and third determining are implemented in a Hammerstein structure.

7. A system for determining a predicted response of a patient to the administration of an anesthesia drug, the system comprising:

a first memory for storing patient dynamics information relating to the infusion of a bolus dosage of anesthesia drug, said first memory having a first output for producing a first output signal corresponding to a first anesthesia level;

a second memory for storing patient dynamics information relating to the infusion of a titrated dosage of anesthesia drug, said second memory having a second output for producing a second output signal corresponding to a second anesthesia level;

a third memory for storing patient dynamics information relating to the patient'"'"'s predicted response to events of surgical stimulation, said third memory having a third output for producing a third output signal corresponding to an anesthesia effect level;

a signal combiner arrangement for receiving the first and second output signals and the anesthesia effect level, and producing at an output thereof a combined anesthesia effect signal;

a limiter coupled to the output of said signal combiner for establishing maximum and minimum values of the combined anesthesia signal; and

a virtual anesthesia monitor for producing an anesthesia value responsive to the combined anesthesia signal.

8. The system of claim 7, wherein the first, second, and third anesthesia levels correspond to respective BIS levels, the anesthesia effect level is a BIS level, and the combined anesthesia signal is a combined BIS level signal.

9. The system of claim 8, wherein the virtual anesthesia monitor is a virtual BIS monitor for producing a BIS value responsive to the combined BIS signal.

10. The system of claim 7, wherein there is further provided a source of known unpredictable disturbances for producing an unpredictable disturbances signal, and said signal combiner arrangement is arranged to receive the unpredictable disturbances signal and the combined anesthesia effect signal is responsive to the unpredictable disturbances signal.